⚡ Quick Summary
This study evaluated seven leading large language models (LLMs) for their effectiveness in clinical decision support specifically in headache management. The findings revealed that while some models showed improved diagnostic accuracy and readability, overall performance remains below that of expert clinicians.
🔍 Key Details
- 📊 Dataset: 13 headache cases from the New England Journal of Medicine (NEJM)
- 🧩 Models evaluated: Seven leading LLMs including ChatGPT-4o, Grok-3, and DeepSeek-R1
- ⚙️ Prompting strategies: Ask-in-sequence (AS) and ask-at-once (AO)
- 🏆 Evaluation metrics: Diagnostic accuracy, supplementary value, and readability
🔑 Key Takeaways
- 📊 Diagnostic accuracy varied significantly among models, with ChatGPT-4o outperforming Grok-3 in the AS strategy.
- 💡 Supplementary value was generally higher with the AS strategy, indicating its effectiveness in enhancing clinical reasoning.
- 👩🔬 Readability scores showed Gemini 2.5 Pro had the best readability, while ChatGPT-4o had the worst in terms of Flesch-Kincaid Grade Level.
- 🏥 Performance was case-dependent, with certain cases consistently receiving low scores, highlighting challenges in integrating psychiatric signs.
- 🌍 Overall clinical accuracy of LLMs remains below expert performance, suggesting the need for supervised use in clinical settings.
- 🔍 Study conducted by a team of headache specialists, ensuring a robust evaluation framework.
- 📝 Publication: J Oral Facial Pain Headache, 2026; 40:140-150.
📚 Background
Headache disorders are a significant global health issue, often leading to substantial disability. The complexity of diagnosing and managing these conditions arises from overlapping symptoms between primary and secondary headaches. As clinicians navigate through clinical, imaging, and pathological information, the integration of advanced technologies like large language models (LLMs) presents a promising avenue for enhancing clinical decision-making.
🗒️ Study
This study aimed to systematically evaluate the performance of LLMs in the context of headache management. By analyzing 13 headache cases sourced from the NEJM, researchers compared two prompting strategies—ask-in-sequence (AS) and ask-at-once (AO)—to assess how these models could assist in clinical reasoning. Three headache specialists independently scored the models across six critical dimensions, ensuring a comprehensive evaluation.
📈 Results
The results indicated that diagnostic accuracy varied by model, with ChatGPT-4o outperforming Grok-3 in the AS strategy. Additionally, the supplementary value was generally higher with the AS approach, while the AO strategy saw DeepSeek-R1 outperforming ChatGPT-5. Notably, the readability of outputs also differed significantly, with Gemini 2.5 Pro achieving the highest Flesch Reading Ease score.
🌍 Impact and Implications
The implications of this study are profound for the future of headache management. While LLMs show potential in enhancing diagnostic processes, the findings underscore that their current performance does not yet match that of expert clinicians. This highlights the importance of continued research and development in integrating AI technologies into clinical practice, ensuring that they can effectively support healthcare professionals in making informed decisions.
🔮 Conclusion
This study provides a structured evaluation of LLMs in headache case analysis, revealing both their strengths and limitations. While advancements in diagnostic accuracy and readability are promising, the overall clinical accuracy remains insufficient for unsupervised use. As we look to the future, further research is essential to refine these technologies and enhance their applicability in real-world clinical settings.
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Benchmark evaluation of large language models for clinical decision support in headache management.
Abstract
BACKGROUND: Headache disorders are a major cause of disability worldwide. In routine practice, diagnosis and guideline-based management are difficult because symptoms can overlap between primary and secondary headaches, and clinicians must combine clinical, imaging, and pathological information. Large language models (LLMs) are being proposed to assist clinical reasoning, but their performance on headache cases and their sensitivity to prompting have not been systematically assessed.
METHODS: We evaluated seven leading LLMs using 13 headache cases from the New England Journal of Medicine (NEJM). We compared two prompting strategies: ask-in-sequence (AS) and ask-at-once (AO). Using a 5-point Likert rubric, three headache specialists independently scored six dimensions: rationality of diagnostic thinking, comprehensiveness of differential diagnosis, diagnostic accuracy, completeness of pathological diagnosis, clinical management, and supplementary value. Readability was measured with Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL). We analyzed differences across models, prompting strategies, and cases.
RESULTS: Diagnostic accuracy differed by model: in the AS strategy, ChatGPT-4o outperformed Grok-3. Supplementary value also varied: in AS, Grok-3 outperformed ChatGPT-5 and Hunyuan-T1; in AO, DeepSeek-R1 outperformed ChatGPT-5. Overall, supplementary value was generally higher with AS, while strategy-related differences in diagnostic accuracy were observed only for Grok-3. Performance also depended on the case; C8 and C11 consistently received very low scores, suggesting difficulty integrating psychiatric or warning signs with pathological findings. Readability differed significantly: Gemini 2.5 Pro had the highest FRE (best readability) across strategies, and AS outputs generally had higher FRE. Within AS, ChatGPT-4o had the highest FKGL (worst readability). No significant model differences were found for the other four clinical dimensions.
CONCLUSIONS: This study provides a structured, reproducible evaluation of LLMs on headache case analysis. While some models improved supplementary value, diagnostic accuracy, or readability, overall clinical accuracy remains below expert performance and is not sufficient for unsupervised clinical use.
Author: [‘Chen S’, ‘Liang D’, ‘Qiu X’, ‘Dong C’, ‘Deng J’, ‘Xu L’, ‘Dong X’, ‘Zhao Y’, ‘Fan X’, ‘Liu X’, ‘Wu Y’, ‘Sun J’, ‘He F’, ‘Ma K’, ‘Yu L’, ‘Wang H’]
Journal: J Oral Facial Pain Headache
Citation: Chen S, et al. Benchmark evaluation of large language models for clinical decision support in headache management. Benchmark evaluation of large language models for clinical decision support in headache management. 2026; 40:140-150. doi: 10.22514/jofph.2026.029