Behind the Image: The Hidden Burdens of Contemporary Radiology Practice

Radiologists operate in a uniquely complex legal and administrative environment. While they often work behind the scenes — interpreting scans, flagging anomalies, and guiding diagnoses — they face a relatively higher malpractice risk and growing regulatory responsibilities.

Despite limited direct patient interaction, radiologists consistently rank among the top specialties for malpractice claims. Between 2008 and 2012, diagnostic errors accounted for 57 percent of radiology-related claims with cancer diagnoses being the most frequent source of litigation.¹ The clinical impact of a missed lesion on a CT or mammogram can be significant, sometimes comparable to procedural errors in other specialties. Radiologists often interpret studies without full clinical context, which can increase their vulnerability to diagnostic challenges and potential errors.²

Federal programs like the Merit-Based Incentive Payment System (MIPS) and the Appropriate Use Criteria (AUC) Program require meticulous documentation, coding, and justification for every imaging study.^3,4 Accreditation bodies such as the American College of Radiology (ACR) add further layers of oversight related to image quality, safety, and peer review.⁵ While these initiatives aim to improve care, they can increase administrative burden and may not directly reduce legal exposure.

In high-pressure settings, radiologists are expected to interpret complex studies in real-time, document findings, and directly communicate critical results to referring providers. These urgent interactions, though vital for patient safety, introduce additional liability, especially when communication breakdowns or documentation gaps occur.⁶

Defensive Medicine: A Double-Edged Sword

In response to these pressures, some radiologists may adopt defensive strategies, such as recommending excessive follow-up imaging, using cautious language in reports, or relying more heavily on artificial intelligence (AI) tools. While intended to reduce risks, these strategies can paradoxically lead to interpretive ambiguity, increased healthcare costs, and, in some cases, unintended legal vulnerability.⁷ Research shows that most diagnostic errors stem not from knowledge deficits, but from cognitive bias, fatigue, or incomplete information.^8,9

Keys to Facilitating a Collaborative Future

Reducing malpractice risk requires more than compliance. It demands cultural and systemic change. Radiology must be fully integrated into collaborative clinical care. This includes:

• direct communication with ordering clinicians
• participation in multidisciplinary case reviews and
• clear documentation of diagnostic uncertainty as a rational clinical stance.

Structured reporting, embedded decision-support tools, and real-time image access can streamline workflows and enhance both diagnostic accuracy and legal defensibility.¹⁰

(Editor's note: For related content, see "Seven Takeaways from New Analysis of Malpractice Cases Involving Interventional Radiologists," "When to Say When on Recommendations in Radiology" and "When Radiologists are Held Captive by Unreachable Referring Clinicians.")

In Conclusion

Radiologists carry significant diagnostic responsibility in an environment defined by legal scrutiny and administrative complexity. Their decisions can alter the course of treatment yet radiologists frequently interpret studies with limited clinical context and under significant time constraints. Strengthening collaboration across specialties, improving access to clinical information and tools, and integrating radiology more directly into patient care are essential steps. These changes won’t eliminate risk, but they will support clearer communication, more confident interpretation, and ultimately better outcomes for patients and clinicians alike.

Dr. Makary is an associate professor of radiology at the Ohio State University College of Medicine.

Ms. Almashni is a third-year medical student at the Ohio State University College of Medicine.

References

1. Harvey HB, Tomov E, Babayan A. Radiology malpractice claims in the United States from 2008 to 2012: characteristics and implications. J Am Coll Radiol. 2016;13(2):124-30.

2. Rosenkrantz AB, Siegal D, Skillings JA, Muellner A, Nass SJ, Hricak H. Oncologic errors in diagnostic radiology: a 10-year analysis based on medical malpractice claims. J Am Coll Radiol. 2021;18(9):1310-1316.

3. Centers for Medicare and Medicaid Services. Merit-Based Incentive Payment System (MIPS). Available at: https://www.cms.gov/medicare/quality/value-based-programs/quality-payment-program . Updated September 10, 2024. Accessed August 5, 2025.

4. Centers for Medicare and Medicaid Services. Appropriate Use Criteria (AUC) Program. Available at: https://www.cms.gov/medicare/quality/appropriate-use-criteria-program . Updated September 10, 2024. Accessed August 5, 2025.

5. American College of Radiology. ACR Accreditation. Available at: https://www.acr.org/Accreditation . Accessed August 5, 2025.

6. Berlin L, Murphy DR, Singh H. Breakdowns in communication of radiological findings: an ethical and medico-legal conundrum. Diagnosis (Berl). 2014;1(4):263-268.

7. Sumner C., Kietzman A, Kadom N, et al. Medical malpractice and diagnostic radiology: challenges and opportunities. Acad Radiol. 2024;31(1):233-241.

8. Waite S, Scott JM, Gale B, Fuchs T, Kolla S, Reede D. Interpretive error in radiology. AJR Am J Roentgenol. 2017;208(4):739–749.

9. Abujudeh HH, Boland GW, Kaewlai R, et al. Abdominal and pelvic computed tomography (CT) interpretation: discrepancy rates among experienced radiologists. Eur Radiol. 2010;20(8):1952-7.

10. Thrall JH, Li X, Li Q, et al. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol. 2018;15(3 Pt B), 504–508.