Author: Logan Young

_{Published October 25, 2021}

By now, we are well aware of the challenges that coronavirus disease (COVID-19) has brought along to clinical practice, universally affecting all of us—from changing regulations and workflow limitations, to alterations in clinical coverage and reallocation of resources. The impact on breast imaging practice is an example, particularly concerning the screening population. At the heights of the pandemic in various locations, these seemingly well groups of patients were classified as non-critical, while resources were, justifiably so, reallocated to critical care. However, as the situation gradually settles, or cruising along between waves of infections in some places, there are increasing concerns regarding whether this group of patients is being neglected, or unduly delayed, thereby risking a delayed cancer diagnosis. This is not just by way of delayed screening mammography in itself, but given increasing vaccination rates, questions have been raised concerning its associated ipsilateral axillary lymphadenopathy.

At the beginning of the vaccination drives, this was unchartered territory. Not only were patients’ screening mammograms delayed by the pandemic itself, some patients potentially faced further delays once they were vaccinated. Concerns for delayed cancer diagnoses became increasingly significant.

We were not alone in this struggle here in Singapore, with conflicting schools of thought, which were all individually valid. There were proponents of delaying asymptomatic cases to minimize unnecessary workups, due to false positivities. In addition, this policy assists in reducing hospital admittance rates and the risks of COVID-19 community spread. On the other hand, there were those who strongly recommended business as usual to avoid any delayed cancer diagnoses. Although little known at the time, much work has now been published that affirms the latter tactic. Various cancer models and analyses have been performed in several countries, mostly confirming what we feared could happen: an increase in projected breast cancer deaths in the coming years due to pandemic-related delays. Indeed, there is anecdotal evidence from assorted practitioners who have encountered cases of COVID-19-related delayed cancer diagnoses in their clinical practice.

It is clear how serious the impact to individual patients remains. Therefore, the simple advice of delaying screening may not be entirely justifiable without clearer and more directed guidelines. Much to our relief, both AJR and the Society of Breast Imaging (SBI) have published recommendations for the management of axillary lymphadenopathy in patients with recent COVID-19 vaccinations. According to SBI, specifically, screening-detected unilateral axillary lymphadenopathy should be ascribed BI-RADS 0 category to allow further radiological assessment, coupled with documentation of any recent COVID-19 vaccination in the clinical history [4–6]. This could be surveilled with short-term (4–12 weeks) follow-up after completion of the second vaccination dose, considering nodal sampling in the event of non-resolution at follow-up.

As we anecdotally observed in Singapore, not all patients with recent vaccinations demonstrate evidence of axillary lymphadenopathy in their screening mammograms. This has been corroborated in a published study from Israel, which reported only 49% of cases developed mammographic evidence. Nevertheless, in the remaining patients and in those institutions offering multimodality screening approaches, the axillary lymphadenopathy is bound to be detected with growing frequency as vaccines continue to be administered worldwide. As a result, many breast imaging centers in Singapore have adopted this pragmatic approach clearly documenting recent COVID-19 vaccination in the ipsilateral arm, along with imaging follow-up to resolution of the lymphadenopathy (Fig. 1).

As the pandemic progresses, we will continue to learn from the ever-evolving COVID-19 infection. What is now current may soon prove to be outdated. Regardless, concerns about delayed diagnosis should continue to be addressed, and mitigating steps should be taken, so that the benefit of early detection continues to outweigh the risks—whatever the situation.

_{Published October 25, 2021}

Back in February, American Roentgen Ray Society (ARRS) representatives made a call. On the other end was the American Medical Association (AMA). They weren’t alone. Representatives from three more of this country’s biggest imaging stakeholders were also on the line.

This meeting was the first of three virtual convenings for the AMA’s Current Procedure Terminology (CPT) Editorial Panel, the assembly ensuring “that CPT codes reflect the latest medical care available to patients.” Top line of the agenda for ARRS and the American College of Radiology (ACR), Radiological Society of North America (RSNA), and Association of University Radiologists (AUR) was updating AMA CPT category codes for the 2023 cycle.

It was hardly a modest proposal. ARRS et al. were lobbying AMA to approve not one, but two new CPT codes specifically for medical imaging. One of the code requests, for quantitative ultrasound tissue characterization, seemed innocuous enough, especially compared to the other glaring one. That second request, for incidental vertebral fracture detection, had two small, historic words preceding it: “automated analysis.”

Apropos of North America’s first radiological society, indeed, ARRS was seeking radiology’s first-ever CPT code for artificial intelligence (AI).

Artificial life comes at you fast, too. Should AMA’s CPT panel ultimately approve these two proposals (in the middle of a pandemic, no less), the concomitant codes would be released first-thing on July 1. Their live, in-effect date would start January 1, 2022, thus allowing clinics to start the billing.

Regarding reimbursement, the prevailing ideology still stood. If an AI code were ever granted AMA’s blessing, it would then need to stand before their Specialty Society RVS Update Committee (RUC), that group “dedicated to describing the resources required to provide physician services which the Centers for Medicare & Medicaid Services (CMS) considers in developing Relative Value Units (RVUs).” Whereas the RUC in Chicago is free to advise, of course, it’s the CMS in Washington that would have the final financial consent. Even ACR’s own journal was asking: “Who Pays and How?” Literally.

By now, you know what happened on the first of July. 55 years after publishing its inaugural CPT code, AMA had heard the plea of ARRS, ACR, RSNA, and AUR, found it good, and the specialty’s first Category III CPT codes for AI were born. Three Category III codes, in fact:

• 0689T—Quantitative ultrasound tissue characterization (nonelastographic), including interpretation and report, obtained without diagnostic ultrasound examination of the same anatomy (e.g., organ, gland, tissue, target structure) 
• 0690T—Quantitative ultrasound tissue characterization (nonelastographic), including interpretation and report, obtained with diagnostic ultrasound examination of the same anatomy (e.g., organ, gland, tissue, target structure) (List separately in addition to code for primary procedure)
• 0691T—Automated analysis of an existing computed tomography study for vertebral fracture(s), including assessment of bone density when performed, data preparation, interpretation, and report

Naturally, 0691T hogged the headlines, but imaging professionals paying closer attention were quick to point out the utility that this pair of CPT codes promised for quantitative ultrasound tissue characterization. Able to be used together with or separately from existing ultrasound examination codes (0690T in conjunction; 0689T on its own), they represented a giant leap forward for mainstay ultrasound examinations (e.g., breast and thyroid). Perhaps most importantly, as the Imaging Wire duly noted, they also bode well for ultrasound AI decision supporters, like Koios Medical.

Meanwhile, many radiologists could see that 0691T for “automated analysis” of existing chest CTs to detect vertebral fractures could pave all the way for AI-based population health osteoporosis programs.

“I believe that this type of ‘population’ health application will lead to requests for many additional CPT codes such as coronary artery calcification on conventional thoracic CT, abdominal aneurysm detection on thoracic, abdominal and pelvic CT scans, evaluation of cardiac chamber enlargement, [chronic obstructive pulmonary disease (COPD)], renal calculi, and many others,” Eliot Siegel of the University of Maryland and Baltimore VA Center told Aunt Minnie.

This single CPT code represented quite a milestone for Israeli AI developer Zebra Medical Vision, which garnered FDA 510(k) clearance back in May 2020 for its software that can detect incidental vertebral compression fractures on chest CT scans.

Right now, Zebra is the only company with an FDA-cleared product for population-scale detection. However, on August 10, 2021, Nanox—Israel’s publicly traded x-ray disruptor—signed an agreement to purchase Zebra for up to $200 million in Nanox shares, half based on future milestones. Hours later, after posting a net loss of $13.6 million (compared to a net loss of $6.4 million for the second quarter last year), Nanox’s founder, Ran Poliakine, announced he will relinquish his role as CEO in January 2022. This Zebra-to-Nanox story, the most momentous acquisition in AI imaging’s brief history, is still developing.

It’s important to point out that the three CPT codes above are not Category I CPT codes. Category III CPT codes are wholly provisional, intended to aid data collection for developing technologies, incipient procedures, and service paradigms. Their endgame is to accumulate enough clinical documentation for the FDA to validate and clear for general application. Often dubbed experimental or, at best, tentative by most major insurers, reliable reimbursement remains unlikely until AMA sees fit to assign a permanent Category I CPT code.

Despite that lack of instant reimbursement, it’s still worth submitting claims for CPT III codes; the data from their utilization become primary sources of support for the eventual creation of a CPT I code. Likewise, many advocates advise referencing similar Category I procedures when reporting Category III CPT codes.

Most experts agree that a standard CPT III code should lead to a permanent CPT I code within five years. So, come 2026, radiologists should expect to see the very first Category I CPT code regarding the automated analysis of an existing CT study for a vertebral fracture—brought to you, in part, by your medical imaging society, ARRS.

Climate change has been described as the greatest global health challenge of the 21st century. Many medical societies, including the American Medical Association, American Academy of Pediatrics, and the American College of Emergency Physicians, have warned about the health care industry’s contribution to greenhouse gas emissions and climate change’s potentially dire impacts on public health. Additionally, health care systems across the country have formed the Health Care Climate Council to help decrease their greenhouse gas emissions and establish more environmentally sustainable practices. Notably, Kaiser Permanente and Gundersen Health System in Wisconsin are already carbon neutral. As large hospital systems and our colleagues in other specialties move toward environmental sustainability, we as radiologists should consider our own practices, asking ourselves what changes we can make to limit our contribution to climate change.

What Is Global Warming? What Is Climate Change?

Although they are often used interchangeably, the terms global warming and climate change refer to distinct, but closely related phenomena. Global warming refers to the dramatic increase in average global surface temperature, which has occurred since the beginning of the industrial age. This warming is largely driven by carbon dioxide, methane, and other greenhouse gases emitted by human activities, which trap solar radiation reflected from the earth’s surface and heat the earth’s atmosphere. As a result of increased greenhouse gases emissions, average global surface temperature has increased approximately 1°C (1.8°F) since the middle of the 19th century.

Warming temperatures have led to an increase in intense, unpredictable weather events and to climate change, a term which describes long-term fluctuations in temperature, humidity, rainfall, and other meteorological patterns.

How Does Climate Change Impact Human Health?

The interactions between climate and health are complex and can be divided into direct and indirect impacts. Direct impacts are easier to measure and are related to extreme weather events, such as heatwaves, floods, droughts, and wildfires. In addition to the traumatic injuries that these events can cause to individuals, extreme weather events can disrupt supply chains and operations of health care systems. These disruptions can decrease the short-term availability and quality of health care for entire populations.

The indirect effects of climate change include longer-term phenomena: distribution of vector-borne diseases, increased food insecurity, increased intensity and duration of allergy seasons, and worsening air and water quality.

The severity of the direct and indirect impacts of climate change for an individual or population can be amplified by geographic location, age, sex, and socioeconomic status. Consequently, the health impacts of climate change are often disproportionately felt by those who are least responsible.

How Does Health Care Contribute to Climate Change?

Health care systems in the United States are estimated to generate approximately 8–10% of the country’s greenhouse gas emissions— more than is emitted by the entire United Kingdom.

Health care emissions are categorized into 3 scopes:

• Scope 1 emissions are direct emissions from health care and hospital operations, such as anesthetic gases or onsite incinerators.
• Scope 2 emissions are calculated from direct energy expenditures from health care operations.
• Scope 3 emissions are indirect emissions from the supply chain and waste treatment.

In the US, scope 3 emissions account for approximately 82% of health care-related emissions. Scope 1 and Scope 2 emissions account for 7% and 11% of emissions, respectively.

In radiology, the majority of our emissions fall into Scopes 2 and 3. Our Scope 2 emissions include the energy used by our facilities to power our scanners, PACS stations, electronic and imaging equipment in interventional suites, as well as heating, ventilation, and cooling (HVAC) systems. Our Scope 3 emissions include the energy used to produce, reuse, and dispose of scanners, procedure kits, and single-use supplies.

How Does Radiology Contribute to Climate Change?

Radiology’s greenhouse gas emissions are beginning to receive attention, and several recent studies help us understand our carbon footprint. Ultrasound generates fewer greenhouse gas emissions than CT and MRI, both in use and in production, therefore contributing less to Scope 2 and 3 emissions alike. At one hospital in Switzerland, three CT scanners and four MRI machines accounted for approximately 4% of the hospital’s total energy use. Notably, researchers found that two-thirds of energy consumption occurred in the idle state for CT, and one-third occurred in the idle state for MRI. In a more recent study of an academic interventional radiology suite, investigators estimated that 23,500 kilograms of CO2 equivalents were emitted during a single work week. This is comparable to the amount of carbon a passenger vehicle would emit over 93,825 kilometers (58,300 miles) or the amount of carbon sequestered by 389 trees over 10 years. HVAC systems and single-use disposable items were the biggest contributors to greenhouse gas emissions from the interventional radiology suite. Particularly, researchers found that 57% of HVAC energy use occurred outside of work hours, when the unit was largely unoccupied. These studies highlight the fact that much of our energy use occurs when systems are idle, providing no benefit to patient care. For radiologists, there are many instances where we can reduce our operating costs and greenhouse gas emissions with minimal impact on our practices.

How Can Radiologists Address Climate Change?

There are several opportunities for radiologists to address climate change in our practices and in the health care system at large. As a first step, we can implement simple cost-saving interventions with marginal, if any, upfront costs. Such interventions include double-sided printing, switching from fluorescent to LED lighting, and turning off those lights and other electronics, including PACS stations, when they are not in use. In interventional radiology, we can relax climate control parameters when the suite is not in use, streamline procedure packs to minimize equipment that is unlikely to be needed, and choose reusable equipment, rather than single-use supplies, when possible. In both diagnostic and interventional radiology, we can work with vendors to increase the energy efficiency of our scanners in idle and off states and to recycle the heat generated by our scanners.

More broadly, we can advocate for sustainable practices in our clinics and hospitals and with our peers and colleagues. At the regional and national level, we can join organizations like the Medical Society Consortium on Climate and Health, which educates physicians and other health care professionals about the medical system’s contribution to climate change, while offering ways to decrease our carbon footprint. Through these groups and our radiology societies like ARRS, we can advocate for broader policy changes, encouraging systemic reductions to our collective footprint and improvements in public health.

The potentially profound impacts that global warming and climate change may have on public health can only be mitigated if all parts of our society—including the health care system—reduce greenhouse gas emissions. Be it implementing more sustainable practices in our hospitals and clinics or advocating for overall change, radiologists can play a key role in helping to reduce health care’s carbon footprint and protecting the health of ourselves, our colleagues, our families, and our patients.

^{Published September 14, 2021}

The role of imaging in prostate cancer detection and staging is of paramount importance, as it needs to provide physicians with accurate information on both the presence and extent of the disease—avoiding overdetection and overtreatment at the same time.

As far as prostate MRI is concerned, remarkable advances have been made over the last decade, and several data support the role of this technique for diagnosis, active surveillance, and in the posttreatment setting.

We know that the measurement of prostate specific antigen (PSA) level alone is not enough because radiological progression can occur without an increase in PSA level, due to the molecular heterogeneity of prostate cancer.

An area of particular interest in prostate cancer is represented by next-generation imaging techniques, which include both PET/CT or PET/MRI using different radiopharmaceuticals (e.g., choline or acetate) and whole-body MRI.

When conventional imaging is equivocal, next-generation imaging can investigate the increased metabolism and vascular changes in prostate cancer and help in the detection and characterization of additional sites of disease, which could potentially change management.

In order to improve the assessment of advanced prostate cancer and patient survival, next-generation imaging (e.g., PET/MRI and whole-body MRI) can provide earlier detection of metastatic disease and predictive biomarkers for therapy selection, allowing us to assess treatment response and identify metastasis that can be biopsied for molecular or genomic characterization.

The 2020 guidelines from the American Society of Clinical Oncology on the optimum imaging strategies for advanced prostate cancer can be summarized as below:

• For clinical high-risk disease at initial diagnosis, the literature supports addition of next-generation imaging when conventional imaging (e.g., CT, radionuclide bone scan, or multiparametric prostate MRI) is equivocal or suspicious for metastatic disease.
• For biochemical evidence of recurrent disease after local therapy with negative conventional imaging, next-generation imaging is indicated to assess the presence of local or distant site and burden of disease to plan salvage therapy.
• For metastatic disease at initial presentation on conventional imaging, next-generation imaging may clarify the burden of disease and alter treatment plans.
• For nonmetastatic castrate-resistant prostate cancer, next-generation imaging can be considered to assess for unrecognized metastatic disease.
• For metastatic castrate-resistant prostate cancer with biochemical progression only, use of next-generation imaging is unclear and should be individualized, preferably as part of a clinical trial.

Additionally, on December 1, 2020, the FDA approved the first prostate specific membrane antigen-targeted PET imaging drug (⁶⁸-Ga-PSMA-11) for patients with suspected prostate cancer metastasis whose disease is potentially curable with surgery or radiation therapy and for patients with suspected prostate cancer recurrence based on elevated serum PSA levels.

Unfortunately, there is still a lack of standardization of next-generation imaging in prostate cancer, and most physicians still rely on traditional imaging, as they may not be aware of the potential of these new imaging techniques.

Access to next-generation imaging is still highly limited, and insurance coverage for these tests remains highly variable. Until these barriers are overcome, the widespread use of next-generation imaging in prostate cancer parameters will be challenging.

Although there are still a number of challenges ahead, next-generation imaging will definitely play an increasing role in the management of prostate cancer—enabling assessment of treatment response and disease progression and aiding the delivery of precision oncology in patients with advanced prostate cancer.

The journey is only just beginning, but the future is definitely encouraging.

^{Published on September 1, 2021}

Founded in 1995, the Academy for Radiology & Biomedical Imaging Research (Academy) is a non-profit advocacy organization located in Washington, DC, that unites imaging societies, patient advocates, and academic radiology departments with the goal of securing federal investment for medical imaging research through education and advocacy. ARRS is one of the founding member societies of the Academy. In 2000, the Academy played a pivotal role in the establishment of the National Institute of Biomedical Imaging and Bioengineering (NIBIB), as well as the Interagency Working Group on Medical Imaging in 2016, and continues to raise awareness with policy makers about the importance and impact of medical imaging research.

The Academy benefits from the continuous expertise and leadership of ARRS representatives to our board. Currently, Erik K. Paulson, ARRS vice president, serves on our Executive Committee, and Ruth C. Carlos, ARRS president from 2019–2020, serves as vice president of the Academy. 

Advocacy is vital to the success of investigators and to the success of radiologists in private practice. Federal funding for medical imaging research impacts the breadth of the field. The Academy is the single organization that brings together academia, industry, and patient groups to collectively advocate for strong investment into imaging research (cf., the annual NIH increases to imaging research in the chart below—created, maintained, and advocated for by the Academy each year).

While the Academy advocates on behalf of the community full time, individual ARRS advocacy efforts have tremendous impact. Ensuring that members of the US Congress hear from individual investigators ensures that, as constituents, you are illustrating the impact research efforts have locally. The Academy regularly requests that researchers across the imaging space reach out individually in support of research funding. By utilizing the Academy’s action alert system, you can use existing templates, adding your own personal touch or details related to your institution, and let your elected representatives know that research funding is important to you.

Beyond advocating for research funding, the Academy has a multitude of programs that assist researchers in the field to succeed. In addition to recognizing new members of the Council of Distinguished Investigators every year, the Academy has a robust and growing Council of Early Career Investigators in Imaging (CECI2). Nearly 20 members of the Academy’s CECI2 council identify ARRS as their professional home. Most recently, the Academy recognized ARRS members Joel Fletcher and Martin Torriani as Distinguished Investigators. There is a plethora of resources on our website, www.acadrad.org, for these researchers. Multiple annual events promote engagement between industry, academia, and patient advocates, such as the Medical Imaging Technology Showcase held on Capitol Hill and the Academy Imaging Shark Tank session presented during the Radiological Society of North America meeting. Additionally, the Academy routinely collaborates with the NIH to sponsor interesting workshops relevant to the field. Often, Academy representatives are invited to voice representative opinions and help formulate pertinent policy. 

The Academy unites the voices across imaging to help achieve the collective mission of improving the field to benefit patient outcomes.

^{Published September 14, 2021}

The COVID-19 pandemic continues to exacerbate the pre-existing epidemic of stress, distress, dis-ease and burnout in our profession—and across the country. Contributors to workplace stress in radiology have been further compounded as we grapple to provide safe care to our patients, keep our teams healthy, uphold social distancing requirements, support, sustain, and engage remote teams, deploy effective communication strategies, and cultivate diverse and high-performing teams. People across the country are fighting silent battles against chronic anxiety, depression, and other mental health disorders during any given workday.

Prior to the pandemic, we heard frequent reference to the hamster wheel environment in which we work; expectations of ever increasing workloads and so-called quality metrics driving us to work faster and longer hours all while meeting ever increasing regulatory requirements. Not surprisingly, the consequences of just trying to keep up include burnout, and the field of radiology is still seeking solutions to mitigate our recognized high incidence.

However, in parallel with burnout is the growing focus on mitigating known stressors, those that establish a genuine conflict between our core values as care-providing physicians and our daily activities in the trenches. This is the reality of the so-called moral distress and injury, which is frequently associated with burnout. To me, this implies that we as individuals are unable to balance work expectations against personal resources—that, somehow, we are failing at what we “should” be doing and achieving. There is a growing school of thought that the symptoms of burnout simply reflect a healthcare delivery system in need of urgent repair. The moral insults and injury of healthcare is not being able to provide the high quality of care that we would want to, thus highlighting the opportunity to address what is contributing to this state. And the consequences are dire: physician suicide rates are now twice that of active-duty military members. Now more than ever, it is clear that we must reprioritize employee wellness efforts and implement additional strategies to protect and support our workforce.

Treating the Cause

To effect lasting change, we must reshift our focus and address the cause rather than the symptoms. While appreciated and beneficial in their own right, wellness programs, flexible schedules, extra time off, and other employee benefits oftentimes treat the short-term symptoms, not the long-term cause.

Relaxation practices, exercise, vacation, mindfulness activities and meditation might be extremely effective at resolving some symptoms on a temporary basis, at least until that time that we are back trying to balance on the hamster wheel. To address the causes, we need brave and effective leaders who are willing to question and confront the constellation of drivers, and who recognize and respect the fourth component of the quadruple aim of healthcare (care of the patient requires care of the provider). We must excavate the problem that is moral injury until its origins become clear.

Numerous factors detract from what we believe is our primary mission and contribute to such injury, including the profit-driven healthcare environment, electronic health records and productivity metrics, provider review sites, litigation concerns, turnaround time targets, and the ever-expanding regulatory mandates. Here I refer to practices mandated by regulatory agencies such as audits, documentation expectations, annual testing, and of course, the unpopular practice of peer review.

Let’s consider peer review as our low hanging fruit here. This is a process that in radiology is often known for being onerous, burdensome, distracting, divisive, resource-intensive, inefficient, and ineffective. In my experience, it can be difficult to use peer review as a driver for meaningful and impactful improvement.

However, the concept persists, in large part due to meeting accreditation and reimbursement requirements. As radiologists, we are expected to devote time to rank the diagnostic skills of our colleagues. During this process, targeting occurs, under-reporting is rampant, and job security might be impacted, yet challenging the status quo is difficult. Despite evidence that radiologists make errors almost 30% of the time, national peer review data reports fewer than 5% of these discrepancies. Is this practice truly an effective use of our time and skills?

Forging a New Path

Peer learning and improvement offers us an enormous opportunity to remove a mandated hurdle to our work-related distresses; it also allows us to embrace an emerging practice that will provide new learning and improvement opportunities. Today, I’d like to give a loud shout-out to the many peer learning trailblazers out there, including: David Larson, Richard Sharpe, Jennifer Broder, Nadja Kadom, Lane Donnelly, Mythreyi Chatfied, Andrew Moriarty, and Richard Heller. And this cohort is growing rapidly.

Now is an ideal time for the field of radiology to commit to taking the necessary steps to embrace peer learning in our practices. This will be a journey that many have commenced, along varied paths, influenced by practice patterns and cultures. In some practices, this will require cultural transformations, so that staff are willing to speak up safely in a Just Culture without fear of consequences. It will require hospital administrators to embrace all components of peer learning as meeting local OPPE requirements. It will require that the focus shift from scoring diagnostic discrepancies to identifying learning and improvement opportunities, and that participation is expected. In fact, willing participation could replace annual denominators altogether. Peer learning leaders could be identified and appropriately trained, and their work acknowledged as a vital part of our performance improvement processes. Most important, the American College of Radiology (ACR) has now approved a new pathway for ACR-accredited facilities to meet the Physician Quality Assurance program requirement, opening a path for practices to embrace this learning and improvement and non-punitive approach, thus no longer needing to use a score-based approach.

I started this column addressing the additive impacts of the pandemic on our preexisting stressors and burnout numbers. I highlighted the growing recognition that the so-called moral injury is an additional and major contributor to our current distress. Transitioning from retrospective peer review to prospective peer learning practices is one superb example of how we can mitigate a known contributor and provide what will, hopefully, be some major relief to our radiologists. This could allow our colleagues to participate in a process that is likely to positively impact our performance and the quality of care that we deliver. Because, ultimately, I believe that’s why we are all here.

_{Published August 9, 2021}

From 1950 to 1967, CBS ran an American game show called What’s My Line? As part of the show, celebrity contestants tried to guess a person’s occupation for a $50 prize that was often donated to charity. In the spirit of learning from each other as radiologists and continuing to explore how our services can best serve our patients, colleagues, and other stakeholders, I introduce to you the medical edition of What’s My Line?

Today, you will be asked to guess my medical subspecialty. (Hint: I’m not referring to abdominal radiology; it’s even more specific than that!)

Here are your clues:

• This field of medicine acquires and manages medical imaging information. That’s our primary business. We acquire data, optimize it into viewable images, retrieve them, and then interpret what they reveal. Based on the information we receive, we generate reports, some of which contain recommendations, and we communicate the results. Some results require urgent notification or follow-up measures to ensure all safety loops are closed.

• We examine high-resolution images to pinpoint abnormalities, propose diagnoses, stage diseases, characterize the effects of therapies or underlying conditions, and guide follow-ups.  We also enjoy and thrive at developing diagnostic differentials; we observe the abnormality and derive appropriate and rational shortlists of the most likely etiology for the findings we observe.

• We are dedicated to providing top-level, timely service to our many stakeholders, including patients and their families, our referring providers, administrators, and payors. As part of this commitment, we are exploring informatics solutions to improve our diagnostic sensitivity and specificity.

• Ensuring staff and patient safety is paramount. We are proud of the quality and safety initiatives that our field has introduced.
• Our field is rapidly transforming from primarily gross anatomic interpretations to today’s cellular and even molecular imaging applications. We now use site or physiological targeting agents to improve our diagnostic sensitivity and specificity. We are excited about the emergence of functional and metabolic imaging applications.

• As you might expect, the costs of our imaging equipment and storage continue to escalate. We are constantly seeking solutions for maintaining and upgrading our fleet of imaging equipment. Not surprisingly, the costs of image management software and its associated licensing are escalating. Our cybersecurity risk management efforts are also ever-vigilant to safeguard all patient health information.

• We are vitally dependent on our superb technical staff on many levels, not only in the pre-imaging stages, but also in helping us to acquire, label, store, and retrieve digital images. We seek to continuously improve our clinical performance through peer feedback and learning, and often rely on the opinions of colleagues to broaden our differentials and confirm our impressions.

• We devote many resources to upholding regulatory requirements. We must meet all HIPAA requirements, be compliant with the National Patient Safety Goals, and ensure that all of our physicians are fully credentialed, licensed to practice, and achieving required CME credits.

• We’re excited and optimistic about the opportunities that artificial intelligence (AI) and machine learning will bring to our field. On one hand, AI will become an essential diagnostic tool to manage the growing demand and sheer volume of images and sequences. On the other, informatics is rapidly becoming a necessary tool for managing and optimizing our operations and ability to positively impact patient outcomes. Our expanding digital image storage needs are resulting in new discussions about data ownership, costs, security, access, and responsibilities.

• We use operational metrics—capturing report content and turnaround time, standardized recommendations, and diagnostic accuracy—to manage and continuously improve our clinical workflows. We strive to add value through our wide array of clinical contributions and are sensitive to stakeholder feedback and experience, which we also actively measure.

Any more clues would simply give it away! What is my line? You’re quite correct if you guessed anatomic pathologist.

The point here really is to show the many and ever-increasing overlaps between pathology and diagnostic radiology. These two subspecialties are rapidly converging from an operational perspective, based in large part on the increasing complexity of effectively and safely managing medical hordes of imaging information and our escalating reliance on sophisticated machines that can learn and facilitate our tasks. At the end of the day, there are so many operational parallels for us to examine and glean best practices for the benefit of those in our care.

_{Updated October 25, 2021}

Practical pediatric imaging education, especially virtually, benefits most from a combination of didactic lectures and interactive case-based reviews that are relevant for a broad audience: dedicated pediatric radiologists, general radiologists who interpret pediatric imaging examinations, and radiology trainees—residents and fellows.

On September 8 and 9, the ARRS Virtual Symposium, Practical Pediatric Imaging, provided a head-to-toe review of both standard and novel, state-of-the-art methods, delivered by an esteemed and diverse faculty. If you were not able to attend this live symposium, the following offers a perfect primer.

During the neonatal session, Judy H. Squires of UPMC Children’s Hospital in Pittsburgh addressed neonatal head ultrasound, including the application of newer imaging methods, such as ultrasound elastography and contrast-enhanced ultrasound (Fig. 1).

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Recently, AJR published Squires’ “Contrast-Enhanced Ultrasound in Children: Implementation and Key Diagnostic Applications” manuscript. According to her review, “contrast-enhanced ultrasound utilization is rapidly expanding, particularly in children, for whom there is a growing range of FDA-approved and off-label diagnostic indications throughout the body.” Noting that ultrasound contrast agents lack renal excretion and can be administered to neonates, Squires concluded that the research thus far points to an “excellent pediatric safety profile of ultrasound contrast agents.” Squires’ facility with contrast-enhanced ultrasound implementation, as well as her knowledge of the supporting evidence in children, proved to be prized assets. Likewise, ARRS Symposia course codirector Rama S. Ayyala’s update on evaluating the vomiting infant was a boon for the curriculum, as was the comprehensive review of neonatal chest disorders from Peter J. Strouse of C. S. Mott Children’s Hospital in Ann Arbor, MI.

After Nancy A. Chauvin of Penn State Health Children’s Hospital in Hershey expertly examined pediatric arthritis, bone tumors, and bone marrow disorders, Strouse, senior author of the recent AJR Expert Panel Narrative Review on “Debunking Fringe Beliefs in Child Abuse Imaging,” explained how misattributing injuries leaves children at risk for future abuse (Fig. 2).

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“Careful review of the scientific evidence and professional society consensus statements is important in differentiating findings attributable to child abuse from fringe beliefs used to discount the possibility that a child’s constellation of injuries is consistent with abuse,” Strouse maintained. He then listed the three categories that these fringe beliefs most often fall into:

1. legitimate alternative diagnoses that should be considered;
2. real disorders with actual findings that do not mimic child abuse;
3. fabricated pathologies.

Meanwhile, 2018 ARRS Scholar Rupa Radhakrishnan of the Riley Hospital for Children at Indiana University Health in Indianapolis led an enlightening session on contemporary imaging and pediatric stroke, later helping ARRS Symposia course codirector Jonathan R. Dillman and Andrew T. Trout, also of Cincinnati Children’s Hospital Medical Center, to steer the interactive case-based review session. Additional neuroimaging lectures were given by Children’s Healthcare of Atlanta researcher Neil Lall, who skillfully detailed congenital brain malformations and pediatric brain tumors.

Following the thorough analyses of pediatric renal and liver tumors by Ayyala and Squires, respectively, Trout shared his multimodality assessment of adrenal tumors—staging, treatment response, the role of nuclear medicine—with Dillman joining to discuss peritoneal, mesenteric, and omental disorders, as well as congestive hepatopathy and Fontan-associated liver disease. (Fig. 3). For more Trout and Dillman research, turn to page 16 for a summary of their study on reduced-dose CT for lung nodules in children and young adults with cancer.

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Trout was the primary author, alongside Ayyala and Squires, on yet another AJR Expert Panel Narrative Review, “Current State of Imaging of Pediatric Pancreatitis”. Imaging timing, secretin-enhanced MRCP, urgent MRI, severity prediction, autoimmune pancreatitis, serial imaging best practices—these are all distinct concerns for imaging pediatric acute, acute recurrent, and chronic pancreatitis. Trout and colleagues’ appraisal concluded with a 12-point consensus statement and structured reporting template; we are still studying both. Apropos, AJR editor in chief Andrew B. Rosenkrantz handpicked this Expert Panel Narrative Review to lead ARRS’ newest newsletter for in-training radiologists, The Resident Roentgen File.

_{Published July 28, 2021}

During this year’s virtual and highly successful American Roentgen Ray Society meeting, it became apparent that we are living in a time of accelerated development and deployment of existing and emerging digital technologies. Individuals and teams are using innovative solutions to care for patients, teach trainees, collaborate with colleagues, and connect within an expanding digital universe.

I for one never imagined that my weekly mobile COVID-19 prediction report would include hourly population densities in nearby airports, supermarkets, restaurants, and bars. With geographically traceable devices, what data could possibly be next?

In the same way that NASA’s Apollo program sparked the development of new technologies (many of which were largely realized and appreciated years later) that landed the first humans on the moon, we are witnessing a fundamental transformation in health care operations that will be captured in future history books. Few could have predicted, for example, that CT scans would become an indispensable screening, diagnostic, staging, and management tool during a global pandemic. Providers have harnessed such a wide swath of tools—from laptops, mobile and wearable devices, and video conferencing to artificial intelligence, thermal sensors, and robots—to better serve patients and their loved ones, sustain remote reading and teaching environments, and uphold compliance and safety protocol. We now achieve efficiencies through rapid scanning, recruit new faculty through social media, teach our trainees in cloud-based classrooms, and attend national conferences with just a click—all without ever boarding a plane or even crossing clinical campuses.

The Future Is Now

The evidence shows that embracing digital technologies results in improved patient outcomes, cost savings and efficiency, increased productivity, heightened compliance and safety, transformed teaching methods, stakeholder satisfaction through digital connections, sustained remote teams, and accessible employee communications and wellness initiatives.

Previously, such innovation resided primarily within the hospital and physician domains, with the gradual integration of patients as they began accessing their personal electronic health records. Now, our digital stakeholders include not only patients, but referring providers, remote teams, educators and learners, researchers, public health authorities, policymakers, schedulers, transporters, the public, commuters, and travelers.

And as the digital stakeholder pool expands, so does its impact: Such technologies now routinely support telehealth, data analysis, access, scheduling, and follow-ups, management decision-making, bidirectional communication, safety compliance and practices, PACS enhancements, teaching and readouts, patient monitoring, diagnostics, consulting, screening, training, forecasting, reporting, and, of course, socializing.

Examining Digital Disparities

We must remember that our digital environment is far from globally universal. At-risk, vulnerable, underserved, and marginalized populations, such as those living more than 7,600 miles away in India today, are grappling to secure simple access and connect effectively with providers and health care delivery services through traditional means, let alone digital ones. They desperately need hospital beds, oxygen and plasma, life-saving vaccine doses, and medical workers. Resources that hospitals, such as ours, are so fortunate to have readily on hand. However challenging these issues are to address, such disparities in access, care, and connections must be studied and included in the many national efforts aimed at eliminating them. What a terrific opportunity for us to make a meaningful difference that matters.

To a large extent, this digital divide is driven by equality, equity, and justice, or the lack thereof. With equality, we assume that here in Massachusetts, for example, all of our patients benefit from the same supports. All are treated equally, irrespective of any differences. But this isn’t necessarily true yet. Having a laptop certainly doesn’t mean a patient can easily access and understand one’s medical records. Additionally, not all laptops have video cameras, and not all hardware supports the ability to participate in video conferencing or telehealth solutions. And then there are those patients who don’t have access to a laptop to begin with. Where does that leave them? It is our responsibility to find out.

From the perspective of equity, everybody receives the specific and different supports they need and, therefore, receive equitable treatment. This is closely tied to justice (some view this as liberation); our underserved patients receive access to appropriate care without requiring specific accommodations because the fundamental causes of inequities have been addressed. In other words, the preexisting systemic barriers have been effectively identified and removed. Consider the impacts and barriers that may exist due to language, poverty, mobility, cognition, geography, access to water, electricity, food, transport, comorbidities, and employment status. By working to eliminate or flatten these barriers, care becomes more equitable and just. There are innumerable opportunities for making a difference that matters here, starting locally.

Bridging Local Gaps

Consider your own imaging team: When you hold video meetings, do all members have equal access to the necessary hardware and software to participate effectively? Are all members afforded the same privacy and time to participate in these meetings? This lesson was brought home to us when we recently convened a video meeting of our wellness council and noticed that several of our technical and nursing staff did not have access to video equipment in their workplace.

Consider your patients, as well: While a health care system might deploy sophisticated software to support their telehealth endeavors, this does not mean that all patients have the necessary hardware or software to participate. Additionally, solutions to barriers such as vision, language, and hearing must be readily available. One additional effort I applaud is to make our digital reports more comprehensible; not every patient understands what is meant by the phrase “the hepatic parenchyma demonstrates a normal echotexture,” nor should they. We should support software solutions to simplify the communication and accuracy of our recommendations.

And in keeping with our educational mission, think about the brisk implementation of so many solutions to support ongoing academic efforts. Will we ever return to our traditional morning resident teaching conference? I’d imagine not; if anything, the pandemic will finally allow us to move away from the prolonged didactic and synchronous teaching methods to ones that are more appropriate, personalized, and contemporary.

Another essential pillar of academic radiology is teaching and developing the next generation of radiology leaders during readouts. We seem to be mired in surveys and comparisons about what processes work best for our traditional readouts. Let’s instead open our eyes to completely new and asynchronous approaches. What an opportunity! And last within this category is lifelong learning. The necessary transformation to virtual national academic meetings this past year has demonstrated the many advantages that our digital environment offers for such forums. Be it cost savings for participants and practices, wider availability of CME credits and on-demand content, less time away from the workplace, or and the ability to directly connect with speakers, the benefits are plentiful.  

Keeping Our Imaginative Focus

Where the opportunities lie here are in fostering participant connections and rethinking how we should transform the content, styles, and media of our traditional talks to take full advantage of individual learner needs and preferences. Again, what terrific opportunities exist in this domain!

So, where do we go from here?

While tremendous and necessary strides have and continue to take place in our abilities to communicate, manage, and connect remotely, I only ask that we continue to be mindful and considerate that not all stakeholders are currently able to participate equally and effectively. The phrase “you’re only as fast as the slowest member of your relay team” is so apt nowadays. In our digital environment, the concept of “precision medicine” should now expand to embrace the specific needs and preferences of our many stakeholders.

As we continue to build and expand our digital frontend, it is equally necessary to focus on supporting the backend, so that all of our team members and stakeholders can participate and benefit from the systems and solutions that are being deployed. The opportunities here are endless, and we need to develop, implement, and share solutions that will ultimately meet the needs and improve the outcomes for our patients. Let’s please keep our imaginative focus on why we entered this wonderful, exciting, and ever-expanding field of radiology in the first place.

^{_{Updated October 25, 2021}}

As MRI technology continues to improve, radiologists must maintain a mastery of complex sequences, evolving protocols, and advanced techniques. Likewise, disease-specific guidelines, protocols, and reporting continue to evolve. Radiologists in all practice types are often tasked with managing a busy practice at this intersection of advanced technology and state-of-the-art clinical care.  

From September 9–10, the ARRS Virtual Symposium, Abdominal MRI: Practical Applications and Advanced Imaging Techniques, delivered trusted perspectives on the most pressing issues in body MRI—ranging from protocol and acquisition optimization, incorporation of advanced techniques in routine clinical practice, and interpretation/reporting for the most important trends in liver, prostate, emergency, and gynecological MR imaging. What follows is our primer for those unable to attend the live sessions.

Emergency MR Imaging

Although CT is the workhorse imaging modality in the emergency department (ED), there are many concerns related to ionizing radiation exposure in populations that are vulnerable due to young age, pregnancy, or exposure to repeated imaging examinations. In patients with nontraumatic acute abdominal symptoms, non-contrast MRI offers similar diagnostic performance to CT. For instance, in a prospectively enrolled cohort of 48 patients with head-to-head comparison of MRI to CT, there was no significant difference in performance for diagnosing acute appendicitis in young adults and adolescents (Fig. 1).

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A key to harnessing the efficacy of MRI in the ED setting is through protocol optimization, focusing on efficiency and speed. Abbreviated protocols, comprising fewer and faster sequences, allow for improved adoption, decreased cost, and maintained sensitivity for answering directed clinical questions. For example, in the ED setting, compared to conventional MR cholangiopancreatography (MRCP) protocols, abbreviated MRCP provides significant time savings, while maintaining similar diagnostic accuracy for the detection of choledocholithiasis. ARRS Symposia course director Victoria Chernyak, ARRS Instructional Courses Committee chair Courtney Coursey Moreno, and Elena Korngold highlighted ED MR protocols and imaging findings for common genitourinary and gastrointestinal emergencies.  

Advanced MR Techniques

Beyond the ED setting, advanced MR imaging techniques have opened the door for quantitative imaging, and multiparametric assessment has become standard practice for many disease processes. Sequences for assessment of liver steatosis, iron deposition, and fibrosis are now available on all major MR vendor platforms, allowing accurate diagnosis and monitoring of patients with chronic liver diseases. A working knowledge of how to extract and report the quantitative data derived from MRI proton density fat fraction, R2* maps, and elastography is required to build a state-of-the-art radiology liver practice. Diffusion weighted imaging (DWI) is no longer an ancillary or optional sequence but is required for accurate multiparametric assessment of prostate cancer. DWI can be used as a biomarker of tumor response; for instance, DWI adds value in assessing response to neoadjuvant therapy in patients with rectal cancer. While important and useful, DWI can be challenging to optimize and interpret in practice. 2002 ARRS Scholar Claude B. Sirlin imparted his applied wisdom regarding DWI acquisition optimization and interpretation, Mustafa Rifaat Bashir offered insights into some of the new sequences and future directions, and Antonio Carlos A. Westphalen emphasized the utility of DWI in the current prostate imaging reporting and data system (PI-RADS) v2.1. 

Protocol Optimization

In practice, MRI provides both great potential and great challenges. Optimizing sequences across multiple scanners, technologists, and protocols can be laborious and frustrating for radiologists. Optimized MRI protocols achieve a delicate balance between acquisition times, image quality, and sequence comprehensiveness, and they are—to paraphrase Albert Einstein—made as simple as possible, but no simpler. Richard Kinh Gian Do, Steven S. Raman, Elizabeth A. Sadowski, and Korngold shared their expert insights into optimal protocols for hepatobiliary, prostate, female pelvis, and bowel imaging. Bashir presented real-world methods for recognizing and, most importantly, mitigating artifacts commonly encountered in abdominal MR imaging.

State-of-the-Art Reporting

Optimizing images is just one hurdle for delivering the best imaging care to patients. Over the last decade, there has been a major movement toward standardized reporting for many disease processes in the abdomen and pelvis. Standardized reporting allows for more precise communication of results and improves compliance with diagnostic criteria. Notably, the liver imaging reporting and data system (LI-RADS), PI-RADS, ovarian reporting and data system (O-RADS), and updated Bosniak criteria are increasingly recognized as the standard of care for interpretation and reporting. ARRS Symposia course codirector Kathryn J. Fowler, Westphalen, Raman, and Sadowski provided an overview of these important systems, a framework for applying them, as well as insights into creating templates to improve reporting efficiency (Fig. 2).

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Delivering state-of-the-art care with MRI requires comfort with the technical aspects of image acquisition, reporting standards and approaches to common disease processes, and advanced sequences as integrated into practice. Taught by world-renowned MR imaging experts, Abdominal MRI: Practical Applications and Advanced Imaging Techniques provided practicable tools to harness the power of these expanded procedures for improving patient care.

^{Published June 28, 2021}

In the past decade, interest in artificial intelligence (AI) research in radiology has increased dramatically, as reflected by the tenfold increase in papers published in the subject over this period. This development is accompanied by fears from radiologists that AI will eventually replace human readers for the diagnostic interpretation of imaging studies. This article will review the current methods AI uses for diagnostic imaging tasks, the challenges AI algorithms have to overcome for their more widespread implementation, and how radiologist roles may shift to better accommodate the strengths of machine learning.

Deep Learning: An Introduction

The term “artificial intelligence,” in the most general sense, refers to the ability of algorithms to mimic human cognitive abilities. AI algorithms used for image analysis are typically designed using neural networks, which involves layers of processing nodes organized into an input, output, and multiple hidden layers. The architecture is feedforward; outputs of the previous layer are used as the inputs of the next. Each layer is composed of nodes, each of which has a weighted function that associates the inputs to outputs. The function weights are determined by backpropagation during training, an algorithm which updates function weights by minimizing a loss function.

Convolutional neural networks (CNNs) are neural networks specifically designed to handle image data and are the most common, though by no means only, deep learning architecture used for medical image applications. In diagnostic imaging studies, CNNs are usually trained with supervised learning by radiologist annotated data. The layers of a CNN process input images into an image classification output decided by weighted probabilities. For example, one hypothetical CNN’s classification output of a chest radiograph may be an 80% probability of pneumonia diagnosis, 15% pleural effusion, and 5% pneumothorax. The architecture of a CNN can be summarized as follows: first, convolutional layers extract features from an input image. The resulting feature map then passes through pooling layers, which decrease the number of trainable parameters by reducing the dimensions of the convolutional layer input [2]. Finally, a fully connected layer uses the inputs from the feature map and applies weights to parameters to classify the image, typically using rectified linear unit (ReLU) as its activation function.

CNNs have been developed for a wide range of diagnostic tasks based on modality image input data. They have shown comparable results to radiologists for identification of lung nodules and quantification of coronary artery calcium volume during low-dose CT screening, comparable performance to radiologists on the diagnosis of multiple thoracic pathologies from chest radiographs, and better diagnostic performance than radiologists for diagnosis of pneumonia from chest radiographs. Aside from applications for chest imaging, CNNs have achieved high accuracy (AUC) for liver fibrosis staging, accurate segmentation of clinical target volume (CTV) and organs at risk (OARs) in colorectal cancer films, and improved prediction of overall survival in glioblastoma patients from MRI data. However, while the results of CNNs are often comparable to diagnostic radiologists, it is important to note their detection tasks are narrow in scope, and like other AI methods, they are dependent on high quantities of quality training and validation data.

Challenges

There are several challenges to AI implementation in diagnostic radiology not readily solved with mere improvements in technology. The opaque nature of an AI algorithm’s functionality is one such challenge. The nature of backpropagation is such that algorithm developers are inherently unable to explain why parameters end up at their trained values, only that they were the values the algorithm discovered as optimal. Likewise, the precise features of an image that a neural network used to arrive at its classification output are impossible to know. In this regard, a neural network resembles the brain it was loosely inspired by—scientists understand that they work, but not how or why. This “black box” quality of machine learning has not, however, prevented the FDA from approving commercial AI-based medical devices and algorithms, and the history of medicine shows the field has accepted other black boxes for patient care. For example, the exact mechanisms by which many drugs work or produce side effects are poorly understood, but such drugs are used and trusted by physicians. Computer scientists are also attempting to make AI more transparent by developing processes to delineate the methods by which neural networks reach conclusions. This new field, called explainable artificial intelligence (XAI), is an active area of research.

Nevertheless, it is easy to imagine a future where AI becomes part of standard of care for diagnostic radiology, wherein health systems, and specifically the radiologist, would still be responsible for algorithm errors, even if an AI’s reasoning for the error is unexplainable. It is still unknown who would bear the liability for an algorithm misdiagnosis, nor whether the public and their primary physicians would accept machine diagnosis.

The training and validation of medical image algorithms is yet another challenge to their implementation. Deep learning algorithms such as CNNs often require huge amounts of data because of the high number of parameters that must be optimized during training. Large high-quality datasets, which must be annotated by radiologists, are expensive to procure and often several orders of magnitude smaller than the training datasets used for other deep learning applications. They usually number from the hundreds to the tens of thousands, compared to the 4 million labeled images Facebook’s DeepFace facial recognition neural network used as its training dataset. There has been recent progress on creating publicly available image datasets for algorithm development and on medical image analysis challenges that provide datasets for developers. Stanford’s CheXpert dataset is the largest of these, with 224,216 labeled chest radiographs available for public use. However, training datasets is still often proprietary due to privacy concerns or intended commercial use of the algorithms utilized. This dearth of training data and lack of transparency can create problems of algorithmic biases; homogenous training data can cause health care AI algorithms to weigh certain diagnoses unequally based on socioeconomic status, race, or gender, and properly diverse datasets are difficult to assemble.

AI and the Future of Radiology

However, the greatest challenge for AI in diagnostic radiology may be whether it replaces a diagnostic radiologist’s function entirely, and the resulting health care implications for patients. Medical experts have even raised the question if diagnostic radiologists should continue to be trained, given the fast-approaching integration of AI technologies as part of diagnostic algorithms, which will almost certainly include some form of initial automated machine-driven image analysis. The advantages of AI over human radiologists seem overwhelming at first. Computers can work 24/7 without signs of fatigue. While diagnostic radiologists typically read between 10,000–20,000 films annually, the number of images a neural network could read in the same time period is multiple orders of magnitudes greater, in the tens or hundreds of millions. As such, the economic reasoning for institutions to replace diagnostic radiologists seems obvious. However, currently developed algorithms only accomplish tasks narrow in scope (e.g., the binary classification of a lung nodule or the density analysis of a renal stone), whereas radiologists read studies holistically, looking for all possible abnormalities displayed in an imaging study. For a computer to read a diagnostic imaging data set in the same way a radiologist typically does would require the development, training, validation, and integration of thousands of discrete AI algorithms into one workflow. This is a difficult task, and though theoretically possible, a single generalized AI algorithm that can holistically analyze all features of an image for all possible diagnoses is even harder. For the foreseeable future, radiologists will still be required for accurately reading film, with AI packages offering decision support algorithms, and boosting reader efficiency by both providing second opinions on the primary detection tasks and simplifying workflow (e.g., prepopulating reports, automatically creating ROIs, etc.)

Even if AI technologies improve such that diagnostic AI application packages become capable of holistically reading modality studies, one must also remember that radiologists are not just image analysts, but fundamentally clinicians, trained to interpret and communicate imaging findings in the clinical context of the patient. Radiomics that uses AI generates complex data that must be interpreted by radiologists and linked to clinical uses; this is a need that will only grow in the future. AI-driven increased efficiency of image analysis may cause radiologists to pivot to more patient interaction and to become even more integrated in the clinical decision processes, in collaboration with a patient’s care team.

Given the uncertainties of the black box nature of AI, radiologists appear to be the best positioned medical professionals to elucidate a neural network’s probability outputs to a patient, along with the image features the machine is using to arrive at its diagnoses and their meaning. To best synergize with AI, radiologists of the future will have to improve care through knowledge of and empathy for their patients, the ability to identify which AI-produced data is relevant to meet diagnostic demands, and the interpretation of scans to elucidate the next steps following image findings to better advocate for their patients.

AI, and in particular convolutional neural networks, have seen success in narrow detection tasks for medical imaging diagnoses. While various challenges exist, such as their opaque nature and the cost and small scale of training datasets, they will likely be surmountable in time. AI will inevitably enter the diagnostic radiologists’ workflow. Although AI will not replace radiologists in the future, it is likely that radiologists who use AI will eventually replace radiologists who do not. The use of AI will lead to more efficient image diagnosis, in combination with optimized decision support algorithms, which will benefit patient care. In the not too distant future, AI could usher a potential realignment of radiologist duties towards a more interactive and patient-focused paradigm, in addition to traditional models centered around the reporting of imaging findings.

^{Published June 28, 2021}

For valiant service selflessly rendered on the frontlines of the fight against COVID-19, the American Roentgen Ray Society symbolically awarded each and every one of our members the 2021 ARRS Gold Medal. The ARRS Gold Medal Story Series shares perspectives of imaging professionals who conquered the day-to-day challenges of battling COVID-19.

The declaration by the World Health Organization of the coronavirus disease (COVID-19) pandemic in December 2019 led to an avalanche of information which did not manifest clearly, especially here in Nigeria. The signs, symptoms, presentation, and management of patients with COVID-19 slowly evolved for us.

The breast imaging sections of our hospitals went into an almost complete lull, as radiologists were exceedingly careful about exposing themselves to patients whose infection status was unknown. Testing patients for COVID-19 was not easy, due to lack of resources and consumables. More so, there was a dearth of personnel protective equipment (PPE), which was in extremely short supply from hospital management. Initially, breast clinics were shut down, and all activities were suspended. We had to set up an Infection, Prevention, and Control (IPC) Committee, which I chaired, while developing core guidelines for operations in the department.

All of the underlisted were considered crucial standard operating procedures:

• Undergo IPC training and tutorials
• Audit staff for protection
• Inventory, maintain, and decontaminate equipment
• Aerate all facilities
• Identify isolation opportunities for at-risk patients

Contamination Prevention

With temperature checks and masks mandatory for entry, our front desk served as patient registration only, not for triaging, so that foot traffic did not build up. To maintain social distancing of at least 2 meters apart in all waiting areas, the floors were marked in red to depict this distance. All persons entering the triage zone had to wash their hands with soap and running water. Posters showing proper modes of mask-wearing and hand-washing—as well as for gloves, goggles, and faces shields—were posted around the hospital. Only one relative was allowed to accompany a patient who could not stand on their own. To further reduce foot traffic, payment areas were augmented and fast-tracked.

Examination Rooms

Only one patient at a time was allowed in the examination room, and the radiographer/radiologist was well-kitted, depending upon that patient’s risk assessment. For COVID-19-postive patients, we had a dedicated mobile radiography unit, though all rooms were decontaminated after every procedure.

Decontamination

With hand sanitizers at each interactive point and zone, hands were properly washed before and after every procedure. All surfaces were decontaminated after each procedure, including cleaning and decontamination of reusable PPE and proper disposal of disposable ones. Our hospital’s standardized protocols for decontaminating an imaging room and equipment—especially CT after attending to a patient with COVID-19—featured downtime of about one hour in between procedures. Result retrieval points were set up in a cubicle outside the facility, and to reduce human occupancy, results were dispatched by e-mail. Remote reporting was encouraged, too.

Communication

Bold signage was displayed all over the hospital, including clear instructions and visible explanation notices posted on our doors. Relevant phone numbers (e.g., Central Preparedness Team, Hospital Infection Control Committee, Departmental IPC, National Center for Disease Control, etc.) were displayed on the front desk, triage zone, and notice boards.

Capacity Building

In addition to restricting their local and international travel, our entire staff received regular training (and retraining) in IPC protocols, especially how to don and doff PPE.

Some of our radiologists also contracted COVID-19. It got to a stage where all the staff in the pathology department tested positive, so ultrasound-guided biopsies were not possible. Even patients had restricted themselves from showing up for treatment in the hospitals, for fear of the unknown and restriction of movement. Breast cancer patients preferred to receive treatment through phone calls, since there were no teleradiology options.

However, musculoskeletal services were made available for patients who needed to be seen as emergency cases.

With the gradual improvement of the knowledge of the disease, availability of testing facilities, PPE, and now, vaccines, there has been much easing of restrictions. A gradual return to normal is envisaged. In our hospitals, there has been a return of influx for breast imaging patients in the last two months, especially for ultrasound and mammography, since these are the readily available investigations.