Other specialties, including ophthalmology, dermatology, and pathology, have already faced many of the challenges radiology is now confronting.
Artificial intelligence (AI) has had a major impact on radiology, from detecting pulmonary nodules to generating structured reports. But as we refine our tools and navigate real-world implementation, radiology will not have to do it alone. Other specialties, including ophthalmology, dermatology, and pathology, have already faced many of the challenges radiology is now confronting.
By studying how these fields approach explainability, bias, workflow integration, and continuous learning, we can shape radiology AI to be not only more effective but more reliable and useful in clinical practice.
One of the biggest obstacles to AI adoption is the “black box” problem. If a model flags a lung mass or dismisses an intracranial hemorrhage, radiologists want to know why. Without transparency, trust breaks down.
In ophthalmology, AI tools for diabetic retinopathy (DR) detection have shown how explainable artificial intelligence can bridge that gap. For example, models like ExplAIn segment specific lesions before classifying DR severity, making the decision process interpretable for physicians. Similarly, FDA-cleared IDx-DR uses visual overlays to show where pathology is located.
Radiology AI can employ a similar algorithm through heatmaps, region-based saliency, or case-based retrieval to ensure radiologists can understand and verify what the AI model sees.
Radiologists have long feared that AI might replace them. But successful applications in dermatology and ophthalmology suggest a better model: augmentation.
Tools like IDx-DR work autonomously but are designed to support rather than replace the clinician’s judgment. Dermatology AI is being used to triage benign lesions or assist in documentation, not create final diagnoses. This kind of “copilot” model has proven more sustainable and acceptable across specialties.
In radiology, this means focusing on tools that flag abnormalities or highlight missed findings, while always keeping a radiologist in the loop. As Dr. Curt Langlotz famously wrote: “AI won’t replace radiologists, but radiologists who use AI will replace those who don’t.”
Bias in AI is well documented. A PNAS study found that gender imbalance in imaging datasets led to biased classifiers. Another cardiac MRI study showed that segmentation algorithms performed worse for minority groups due to training data disparities.
Fields like natural language processing have responded by developing auditing tools and bias-mitigation protocols. Radiology can follow their lead by reporting performance across different demographic subgroups (race, sex, and age, etc.) and training on diverse, multi-institutional datasets.
AI is not static. In industries like autonomous driving, models constantly learn from new scenarios through feedback loops. Yet, radiology AI tools are often only trained once, then deployed and left unchanged.
A more sustainable model is continuous learning. In a feedback maturity framework, Dikici et al. describe how radiologist corrections can be captured and used to retrain models, leading to fewer false positives over time.
In primary care settings, AI systems like AI Consult in Nairobi improved diagnostic accuracy by integrating real-time clinician input. Radiology can adopt similar pipelines, making the radiologist not just the end-user but part of the training loop.
Even a powerful AI model will fail if it disrupts workflow. Early digital pathology tools struggled not due to poor accuracy but because they required clinicians to change how they worked.
The same risk exists in radiology. Tools must be: 1) integrated into existing PACS systems. 2) easy to use with minimal clicks. And 3) designed to help radiologists under time pressures.
Human-centered design is critical. As Dr. Eric Topol notes, high-performance medicine requires aligning AI with clinician needs, and not the other way around.
Radiology can also learn from how other fields approach clinical validation and regulation. The FDA approved IDx-DR after a prospective trial in primary care settings. This real-world evidence helped ensure that the model worked safely and consistently across a diverse patient population.
Radiology AI needs the same rigor through prospective clinical trials, external validation, and use of reporting standards like SPIRIT-AI and CONSORT-AI.
Radiology’s future with AI is bright, but it does not need to be built in a vacuum. By borrowing lessons from ophthalmology, dermatology, pathology, and non-medical fields, we can avoid common pitfalls and make the adoption of AI more seamless.
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