Usama Awan

Although many qualified individuals outside the field of medicine are also referred to as

“doctors,” the mission of healthcare practitioners in medicine-and radiology, in particular-is centered on teaching, knowledge-sharing, and collaboration to achieve the best possible patient outcomes. After all, the word doctor 

 docÄre, which means “to “teach.”

Every patient, every image, every new diagnosis is a teaching and learning opportunity. The radiologist not only teaches residents and students who will one day go on to teach others, but also his or her daily work involves educating referring physicians about imaging findings and the best course of action.

 initiative, radiologists are increasingly using their expertise to teach patients directly about their imaging findings-leading to more informed patients as well as a more gratifying experience for the radiologist. Thus, the teaching role of the radiologist is vast: consisting of teaching oneself, trainees, other radiologists, referring clinicians, and patients.

Once a physician enters the field of radiology, he or she become committed to life-long learning. One does not truly become an expert in any field-much less a field such as radiology which requires a vast knowledge base spanning many disciplines-without continuous and deliberate practice.

A paper published in the Harvard Business Review titled, “

” claims that “The journey to elite performance is not for the impatient or the faint of heart. It takes at least a decade and requires the guidance of an expert teacher to provide tough, often painful feedback. It also demands would-be experts to develop their ‘inner coach’ and eventually drive their own progress.”

The notion of requiring a decade to become an expert in a field is often repeated in various circles and holds true for radiology. Expertise requires an external teacher to provide critical feedback as well as a great internal drive and for that, one must be willing to teach themselves even after having completed residency.

Beyond life-long learning and self-teaching, one of the core pillars of radiology is trainee education and teaching. Many radiologists enter academics with the ambition of teaching tomorrow’s radiology and healthcare leaders. This mission is critical for ensuring a supply of well-trained, competent radiologists needed to address the rapidly-growing imaging needs of our increasing population.

While teaching is an honorable and noble mission, increasing rvu demands and decreasing reimbursements represent emerging challenges for radiologists to provide robust teaching while maintaining productivity. Interestingly, 

 published in the Journal of the American College of Radiology demonstrated a positive relationship between clinical productivity and teaching-highlighting the role of well-trained residents and fellows in helping maintain efficient imaging operations. Despite the challenges, the opportunity to teach trainees and have a positive impact on the personal and career development of trainees is ultimately rewarding to both the staff radiologists and trainees.

Beyond that, radiologists also spend time teaching their fellow colleagues by writing case reports, giving lectures, conducting various CME sessions, and by sharing their research findings. In this manner, radiologists continue to develop the field professionally and help contribute to life-long learning of radiologists.

Radiologists are also responsible for teaching and mentoring their junior colleagues within a radiology group or academic department. Senior residents are often an instrumentional factor in the education of junior residents, and various models for such teaching already exist.

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For example, one particular challenge for new, first-year residents is physics. 

 implemented upper-class residents teaching introductory radiological physics to first-year radiology residents in their first month of training. This teaching was well received by the first-year residents-they found both improvement in their knowledge as well as an increased interest in physics. It also served as an excellent way for residents to practice teaching in a formal manner, an opportunity many residents miss during their residencies.

Yet another aspect of teaching that radiologists must undertake is when discussing patients with referring physicians. In the past-before the advent of PACS and digitilized images-clinical rounds often began in the radiology reading room, which promoted robust radiologist-clinician interactions. While the direct interactions have decreased in the age of PACS, the role of the radiologist as a consultant is more important than ever with opportunities to positively impact patient care in interdisciplinary consultations and tumor boards.

As a means of promoting further collaboration, some institutions have adopted a system of “virtual radiology rounds” where clinicians and their teams discuss their patients at a set time with the attending radiologists via web conferencing. 

 that this concept can lead to increased collaboration and, therefore, potentially improved patient care as well as excellent teaching opportunities for attendings, trainees, and other staff.

Whether a radiologist contributes to an M&M conference, a tumor board, a grand round lecture, or a one-on-one curbside, the role of a radiologist as an educator to other healthcare providers is at the center of modern healthcare.

Finally-and arguably most importantly-the radiologist’s role in teaching patients is ultimately the most critical and rewarding. With ACR’s imaging 3.0 initiative, there has a renewed effort to increase the role of the radiologist in direct patient care. As a result, many successful implementions and case studies of this patient-centered radiologic care have demonsrated the positive impact of radiologists in educating patients directly regarding their imaging findings and disease implications.

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While initiatives like Imaging 3.0 have their own set of challenges in some practice settings, the effect of having a better-informed patient is immense in improving outcomes. No other specialist is better equipped than a radiologist to discuss imaging with patients, simplyfing technical details and results of imaging-guided procedures into lay terms while understanding the subtleties of findings and their meaning in the context of the clinical and seriologic data.

While these interactions are centered on educating the patients, they are also educational for the radiologists, offering them an opportunity to learn from the patients and correlate the imaging with the clinical findings.

Furthermore, these patient interactions contribute to a more rewarding radiology career and improved job satisfaction in spite of current alarming rates of burnout due to increasing work demands. Ultimately, these direct patient interactions are not only impactful and rewarding, but are also the right thing to do for our patients.

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Why radiology is really all about teaching.

The Art of Teaching: Perspectives on the Role of Radiologists as Master Educators

There is much concern over the potential of Artificial Intelligence (AI) and job security in all fields, and when it comes to medicine, radiology seems to particularly be in the crosshairs. This uncertain territory, as well as the unfamiliar jargon, only adds to the fear. Terms such as "deep learning," "machine learning," "big data" and so on induce more than just a flutter of unrest. As with all things unfamiliar to us-including AI-the best way to curb irrational fears is to get the facts and learn. AI has the potential to transform the practice of radiology in the modern era, and instead of being left behind, we must use our expertise as master diagnosticians and imaging experts to properly guide the AI revolution and positively impact radiologic care. Not only can AI improve our workflow efficiency, productivity, accuracy, and satisfaction, but it can most importantly help improve the quality, outcomes, and delivery of our imaging patient care.

Several practical implementations of AI have already been studied and put to use, providing a glimpse of our future. A team at The Ohio State University led by Prevedello et al. developed an AI algorithm which automatically evaluated head CT studies as they were scanned for critical findings to help appropriately order the studies by severity on the radiologist's worklist. The algorithm was successfully able to identify acute hemorrhage, mass effect, hydrocephalus and suspected acute infarcts, and flag these studies to be read first by the radiologists in that busy, high-volume practice (1). This work demonstrated great promise for further developing similar algorithms to make the initial assessment and guide the radiologist to review studies with potentially critical findings first to deliver timely patient care. Rather than "competing" with radiologists, AI hence can aid radiologists to provide an even better service to patients.

Other future avenues of improving the radiology workflow include AI delving into the sometimes hundreds of medical notes and previous radiology reports to help the radiologist generate a more clinically relevant report efficiently. AI can potentially mine the patient's electronic medical record for pertinent medical and surgical history, labs, and reported physical exam findings. This automatization alone could provide the radiologist with helpful clinical history leading to better interpretations for our increasingly complex patients. Likewise, AI can increase our accuracy and productivity by automatically measuring lesions and correlating size changes with prior studies, so we can focus on our roles as interpreters of the findings and as consultants to our colleagues and patients-an emphasis of the ACR's Imaging 3.0 initiative. In addition to helping provide better imaging care, this role of AI will likely improve our work environment, job satisfaction, and reduce burnout as we get to focus more on our patients and the true application of our expertise in patient care while reducing the mundane, repetitive aspects of our work.
        
In addition to gains in improved finding detection and efficiency, the potential role of AI in prognostication and personalized therapies is enormous. AI applications such as texture analysis can have implications in detecting whether a lesion is benign or malignant and further predicting long-term outcomes, acting essentially as a non-invasive digital biopsy. A recent study by Choi et al. demonstrated the potential role of this analysis in predicting outcomes of pancreatic adenocarcinoma evaluated by MRI (2). Another work by Digumarthy et al. showed successes in reliably predicting well- and poorly differentiated lung malignancies evaluated by CT (3). As we embrace our role as experts in imaging informatics, we can use such tools to provide better consultations to our patients and our colleagues. In fact, such synergistic tools can solidify our role at the center of healthcare delivery, rather than replace or outcompete us.

Despite the positive potential impact for radiologists and radiology, the perceived fear of AI within the field is real. A recent survey showed that this fear is palpable not only among radiologists, but even more so among trainees. In the current crossroads, many second guessed their decision of entering radiology with half of the respondants indicating either yes or a maybe to the question: Would their existing knowledge of AI have changed their decision to enter radiology? (4). The only true way to dispel this fear and ensure a bright future in radiology is to equip ourselves to lead this revolution and shape the future applications of AI. Together, human and machine can create a better patient experience and better clinical outcomes than either can alone. We have always successfully adapted whenever a technological advancement rolled out or rapid change faced us, and AI is no different. The path to success in the AI revolution is to own it now.

References:
1. Prevedello LM, Erdal BS, Ryu JL, et al. Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology. 2017;285(3):923-931.
2. Choi SY, Kim SH, Kang TW, et al. Differentiating mass-forming autoimmune pancreatitis from pancreatic ductal adenocarcinoma on the basis of contrast-enhanced MRI and DWI findings. AJR Am J Roentgenol. 2016;206(2):291-300.
3. Digumarthy SR, Padole AM, Logullo R, et al. CT texture analysis of histologically proven benign and malignant lung lesions. Medicine. 2018;97(26):1-3.

4. Collado-mesa F, Alvarez E, Arheart K. The role of artificial intelligence in diagnostic radiology: A survey at a single radiology residency training program. J Am Coll Radiol. 2018;17:1440-1546.

The only true way to dispel fear and ensure a bright future in radiology is to equip ourselves to lead the future applications of AI.

Usama Awan

Articles

The Art of Teaching: Perspectives on the Role of Radiologists as Master Educators

Artificial Intelligence: Own it Now