They are both image-centric, diagnostic disciplines, which, when combined, give the most complete picture of disease. But does it make sense for pathology and radiology to fully merge into one specialty?
A new paper published in the European Journal of Radiology Artificial Intelligence suggests that a merger is: “not only a likely development, but also important for the future of precision medicine.” The commentary – by radiologists Evis Sala and Mathias Goyen – has escalated a debate that has been rumbling for some time.
In radiology newsletter AuntMinnieEurope, Sala said, “The convergence of radiology and pathology is a bold idea that challenges tradition, but the intensity of the response, especially from the younger professionals, has been quite remarkable. It shows there is appetite for change and the appetite is real.”
The separation of radiologists and pathologists is largely historical and, the authors pose, increasingly counterproductive in the era of digital and computational medicine. Their arguments center on the idea that AI is rapidly dissolving boundaries between image-based and tissue-based diagnostics, allowing seamless integration of radiology, pathology, and molecular data within unified platforms.
The paper introduces the concept of a new hybrid "diagnostician" – trained in imaging, histopathology, and molecular analysis – who will deliver integrated, data-driven diagnostic reports for precision medicine. It goes as far as to suggest a unified curriculum for the new specialty: a 5- to 6-year integrated residency combining radiology, pathology, oncology, informatics, and AI – culminating in a joint certification in “Diagnostic Medicine.”
With shortages in both pathology and radiology personnel, the rationale for cross-trained diagnosticians is that they could mitigate workforce gaps, reduce burnout, and enhance system resilience.
The authors go on to present case studies that demonstrate the feasibility of a merger. Examples like UCLA’s Integrated Diagnostics Initiative and Proscia’s integrated diagnostics and R&D platform show real-world success in merging workflows, reducing diagnostic discordance, and improving accuracy in cancer diagnostics. The report also flags support of the concept by national bodies such as the US National Academies of Sciences, which is formally exploring integrated diagnostics.
Precision oncology, in particular, demands interdisciplinary collaboration, according to the commentary. In complex cancer care, merging radiologic, histologic, and genomic data improves therapeutic targeting and response monitoring, reinforcing the merger’s clinical necessity.
And technology is now geared up for this merger, according to the authors. Advances in cloud-based platforms, interoperable databases, and AI-assisted tools are enabling real-time cross-specialty collaboration – removing previous technical barriers to integration.
The authors conclude that merging radiology and pathology is not merely a futuristic vision but a "clinical necessity" – essential for achieving the goals of precision, efficiency, and patient-centered diagnostic medicine.
But do stakeholders from pathology share this vision of the future? Here are a few comments from Panel of National Pathology Leaders experts:
“There is no doubt that pathology and radiology are converging. AI may help facilitate this, but the explosion in molecular technologies and precision imaging is already pushing the two fields closer together, independent of AI. It will be interesting to see how long it takes for this to happen - traditional boundaries being what they are.”
“In my honest opinion, and as I have advocated for numerous times in the past, radiology and pathology could be combined under one operation anytime and independently of AI. However, there is no doubt that AI will also accelerate the professional integration of both specialties… It’s just a matter of time and generational progress.”
“Despite both being critical to diagnosis and involving pictures, the fields remain very different. The logistics alone for care delivery, professional training, patient throughput, regulatory oversight, etc., have little overlap. While I could see them combining in some form at the top organizational/hospital/system level simply as a way to reduce bureaucracy or management overgrowth, I otherwise have a hard time seeing a comprehensive merger – unless AI makes both fields unrecognizable in terms of how medicine is currently practiced.”
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