In a high-volume outpatient practice or during a busy overnight shift where a single radiologist may be covering multiple facilities, AI-assisted prioritization acts as a critical safety net. By scanning images in the background the moment they are acquired, AI can flag urgent findings and move those cases to the top of the worklist. For the late-night worker, this ensures that life-threatening cases don’t sit behind routine scans, triaging the workload when human resources are at their thinnest.
To be truly effective, prioritization tools must be deeply integrated with a practice’s radiology information system and electronic health record. The goal is for AI to function as a silent triage coordinator running in the background, syncing with existing hospital systems, and only intervening when the data suggests a change in priority is medically necessary. When implemented this way, AI actually protects the clinician’s focus.
Empowering Local Innovation with AI
While point solutions and prioritization tools address existing workflows, the next frontier in reducing burnout lies in giving radiology practices the power to build their own tools within a secure environment. Historically, developing a custom AI model required years of coding, massive compute power, and the painstaking accumulation of local data, a luxury few busy practices could afford. This often left clinicians dependent on third-party solutions that don’t always align with their specific patient demographics or clinical preferences.
The emergence of imaging AI foundries changes this dynamic entirely.7 These platforms provide a secure environment equipped with pre-validated foundation models already trained on millions of diverse medical images. With the heavy lifting of initial development already done, practices can bypass the years-long development cycle. Instead of starting from scratch, clinicians can fine-tune these existing models with a smaller, curated set of their own local data to ensure the tool reflects their specific diagnostic standards.
By utilizing an AI foundry, a practice can move beyond the limitations of off-the-shelf software and create bespoke tools, whether it’s a specific measurement automation for a unique surgical group or a specialized screening tool for a local population. This shift from being passive consumers of AI to active creators not only solves specific workflow bottlenecks but also restores a sense of agency to the profession. When radiologists can design the very tools they use, AI stops being a source of secondary frustration and becomes a tailored instrument for professional longevity.
Dr. Siddiqui is the co-founder, chairman, and CEO of HOPPR.
References
- Chen JY, Chen KC, Buch K, Hagiwara M, Lexa FJ. In plain sight, a radiology workforce crisis in the making — gap between job growth and the radiology training pipeline. AJNR Am J Neuroradiol. 2025;46(9):1769-1775.
2. McDonald RJ, Schwartz KM, Eckel LJ, et al. The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload. Acad Radiol. 2015;22(9):1191-1198.
3. O’Reilly KB. More than 80% of physicians use AI professionally: AMA survey. American Medical Association. Available at: https://www.ama-assn.org/practice-management/digital-health/more-80-physicians-use-ai-professionally-ama-survey . Published March 12, 2026. Accessed November 3, 2026.
4. Henry TA. Physicians’ greatest use for AI? Cutting administrative burdens. American Medical Association. Available at: https://www.ama-assn.org/practice-management/digital-health/physicians-greatest-use-ai-cutting-administrative-burdens . Published March 20, 2025. Accessed November 3, 2026.
5. Hasan MK. Digital multitasking and hyperactivity: unveiling the hidden costs to brain health. Ann Med Surg (Lond). 2024;86(11):6371-6373.
6. O’Neill TJ, Xi Y, Stehel E, et al. Active reprioritization of the reading worklist using artificial intelligence has a beneficial effect on the turnaround time for interpretation of head CT with intracranial hemorrhage. Radiol Artif Intell. 2020;3(2). Available at: https://doi.org/10.1148/ryai.2020200024 . Published November 18, 2020. Accessed November 3, 2026.
7. Slavkova KP, Traughber M, Chen O, et al. HOPPR medical-grade platform for medical imaging AI. 2024. ArXiv. Available at: https://arxiv.org/abs/2411.17891 . Published November 26, 2024. Accessed November 3, 2026.