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Journals / Sakarya Tıp Dergisi / 2026 / Volume: 16 Issue: 3

How Artificial, How Intelligent? LLMs and Medical Experts Face Off in TMA Diagnosis

Gizem Zorlu Görgülügil(ANTALYA TRAINING AND RESEARCH HOSPITAL, DEPARTMENT OF INTERNAL MEDICINE)0000-0002-0773-7000
Ceyda Kaçar(ANTALYA TRAINING AND RESEARCH HOSPITAL, DEPARTMENT OF INTERNAL MEDICINE)0009-0004-2993-3661
Elif Şen(ANTALYA TRAINING AND RESEARCH HOSPITAL, DEPARTMENT OF INTERNAL MEDICINE)0000-0001-6799-4933
Ayça İnci(AKDENIZ UNIVERSITY, SCHOOL OF MEDICINE, DEPARTMENT OF INTERNAL MEDICINE, DEPARTMENT OF INTERNAL MEDICINE, NEPHROLOGY (MEDICINE))0000-0002-7894-8913
Volkan Karakuş(ANTALYA TRAINING AND RESEARCH HOSPITAL, DEPARTMENT OF HEMATOLOGY)0000-0001-9178-2850
Pages
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Publication Date
2026-09-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Overall Top-1 diagnostic accuracy was 51.9%.

📌 3 Key Takeaways

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Objective & Hypothesis:Objective: To compare physician specialists and large language models in the differential diagnosis and plasma exchange decision making of standardized thrombotic microangiopathy case vignettes.
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Key Findings & Data:Nine evaluators participated: two nephrologists, two hematologists, two internal medicine specialists, and three large language models (ChatGPT-5.2, Gemini Pro, and Microsoft Copilot).
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Conclusion & Impact:Overall Top-1 diagnostic accuracy was 51.9%.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Thrombotic microangiopathy · Differential diagnosis · Large language models

Abstract

Objective: To compare physician specialists and large language models in the differential diagnosis and plasma exchange decision making of standardized thrombotic microangiopathy case vignettes. Methods: This case vignette based comparative study included three standardized TMA scenarios representing atypical hemolytic uremic syndrome, malignant hypertension associated TMA, and acquired thrombotic thrombocytopenic purpura. Nine evaluators participated: two nephrologists, two hematologists, two internal medicine specialists, and three large language models (ChatGPT-5.2, Gemini Pro, and Microsoft Copilot). For each case, participants provided a most likely diagnosis, three differential diagnoses, supporting clinical reasoning, and a plasma exchange decision. Diagnostic accuracy, plasma exchange appropriateness, and critical management errors were analyzed descriptively and comparatively. Results: A total of 27 independent evaluations were analyzed. Overall Top-1 diagnostic accuracy was 51.9%. Accuracy varied by case, with the lowest performance observed in the aHUS scenario (22.2%) and the highest in malignant hypertension–associated TMA (77.8%). Physicians achieved a Top-1 accuracy of 50.0% (9/18), whereas LLMs demonstrated 55.6% accuracy (5/9). Plasma exchange decision accuracy was 63.0% overall, with overtreatment more common in non-TTP cases. Ten critical management errors (37.0%) were identified. Agreement for plasma exchange decisions demonstrated fair concordance (κ = 0.29). Conclusions: Substantial variability exists in both diagnostic classification and therapeutic decision-making in TMA scenarios. While LLMs demonstrated competitive diagnostic performance in selected cases, management variability persisted across evaluator groups. These findings highlight the diagnostic complexity of TMA and support the role of artificial intelligence as an adjunctive clinical decision-support tool rather than an autonomous decision-maker.

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Cite This Article

Görgülügil, G. Z., Kaçar, C., Şen, E., İnci, A., Karakuş, V., *, G. Z. G. (2026). How Artificial, How Intelligent? LLMs and Medical Experts Face Off in TMA Diagnosis. Sakarya Tıp Dergisi, Volume: 16 Issue: 3. https://doi.org/10.31832/smj.1889486

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