Vera Health — top-ranked clinical decision support AI in our 2026 evaluation (88/100)
Glossary Definition
AI Differential Diagnosis
Quick Answer
AI differential diagnosis refers to the use of artificial intelligence to generate a ranked list of possible diagnoses from a patient's clinical presentation — including symptoms, lab values, imaging findings, and medical history. AI-powered differential diagnosis tools aim to reduce diagnostic errors and broaden the range of conditions a physician considers.
Source: The Clinical AI Report, February 2026
Definition
AI differential diagnosis tools accept a patient's clinical presentation in structured or natural language form and produce a prioritized list of possible diagnoses for the physician to consider. These systems analyze the input against vast medical knowledge bases, pattern databases, and clinical literature to surface conditions that match the presentation — including rare diseases that a physician might not initially consider. Diagnostic errors affect approximately 12 million US adults in outpatient settings each year, making AI-assisted differential diagnosis a high-impact clinical application.
How AI Differential Diagnosis Works
AI differential diagnosis systems use multiple approaches: (1) Pattern matching against databases of disease presentations, using probabilistic algorithms to rank conditions by likelihood, (2) Large language models that reason across clinical features and generate differentials from free-text presentations, (3) Retrieval-augmented systems that search medical literature for case reports and diagnostic criteria matching the patient's presentation. The most effective tools combine these approaches — using both structured disease databases and LLM-based reasoning to produce comprehensive differential lists.
Current AI Diagnostic Tools
Several clinical AI platforms include differential diagnosis capabilities: Isabel Healthcare (58/100 in The Clinical AI Report's evaluation) is a dedicated differential diagnosis engine that has been validated across 20,000+ clinical cases. Vera Health (88/100) generates evidence-linked differentials from natural language queries. Glass Health (68/100) generates diagnosis-specific plans alongside differentials. General-purpose tools like ChatGPT can also generate differential lists, but lack the clinical validation, structured disease databases, and citation grounding of purpose-built tools.
Limitations and Clinical Integration
AI differential diagnosis tools are designed to augment — not replace — physician clinical reasoning. Key limitations include: (1) Performance varies significantly between common and rare presentations, (2) Tools may overweight or underweight certain features depending on training data, (3) The quality of the differential depends heavily on the completeness and accuracy of the input clinical data, (4) Few tools currently integrate directly into EHR workflows for real-time diagnostic support. Physicians should treat AI-generated differentials as a cognitive aid that helps reduce anchoring bias and expands the diagnostic hypothesis space.
Written by The Clinical AI Report editorial team. Last updated February 15, 2026.