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October 27, 2025International Journal of Diabetes and Technology

Clinical Assessment of Large Language Models: A Comprehensive Multi-domain Performance Study for Healthcare Applications

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Authors

HHHarsh HiraniBSBharat SabooAMAlok Modi

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Overview

Comprehensive evaluation shows Perplexity leads in safety metrics, indicating variances among LLM applications in healthcare.

Key Points

  • Significant performance variations were revealed among large language models in healthcare applications.
  • Perplexity emerged as the top model for safety, achieving 94% accuracy in source citations while exposing critical safety concerns.
  • Evaluation framework assessed four models across 15 clinical domains using standardized testing scenarios to mimic real-world applications.
  • Current models need rigorous safety protocols and multi-platform strategies for effective clinical integration.

Cite This Study

Hirani et al. (2025) studied this question.

synapsesocial.com/papers/68ff87d8c8c50a61f2bdcc67https://doi.org/10.4103/ijdt.ijdt_31_25
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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  3. 3GALATEA II: Benchmarking LLM Safety in Clinical Simulation. Behavioural Safety and Ethical Robustness of Large Language Models in a Multi-Agent ICU Decision Support Architecture2026
  4. 4Evaluation of the Performance of 3 Large Language Models in Clinical Decision Support: A Comparative Study Based on Actual Cases (Preprint)2024
  5. 5Evaluation of large language models in emergency medicine scenarios: a comparative analysis of ChatGPT-4o, ChatGPT-o3mini, Gemini 2.0-pro, and DeepSeek-R12026 · 1 citations