⚡ Quick Summary
This study evaluated the performance and safety of GPT-4 in generating management plans for type 2 diabetes mellitus, comparing its outputs with those from physicians using real patient records. While GPT-4 showed promise in reducing unnecessary prescriptions, it did not match the completeness of physician-generated plans.
🔍 Key Details
- 📊 Dataset: Anonymized patient records from West Bengal, India
- 👩⚕️ Participants: 50 patients with type 2 diabetes mellitus
- ⚙️ Technology: GPT-4 vs. three blinded physicians
- 📋 Evaluation Criteria: Completeness, necessity, dosage accuracy, and a Prescribing Error Score
🔑 Key Takeaways
- 📉 Fewer missing medications were found in physician plans compared to GPT-4 (p=0.008).
- 💊 GPT-4 generated fewer unnecessary medications (p=0.003).
- ⚖️ Dosage accuracy showed no significant difference between GPT-4 and physicians (p=0.975).
- 🔍 Overall error scores were comparable (p=0.301).
- ⚠️ Safety issues were noted in 16% of GPT-4 plans.
- 🤖 GPT-4 can serve as a supplementary tool in diabetes management.
- 🔄 Continuous human oversight is essential for AI efficacy and safety.
📚 Background
The integration of large language models (LLMs) like GPT-4 into healthcare is a burgeoning field, promising to enhance patient care through personalized medicine and efficient management plans. However, concerns regarding accuracy, ethical implications, and bias necessitate thorough evaluations to ensure these technologies meet established medical standards.
🗒️ Study
Conducted in West Bengal, India, this study involved a comparative analysis of management plans for 50 patients with type 2 diabetes mellitus. Both GPT-4 and three physicians, who were blinded to each other’s responses, generated management plans that were then evaluated against a reference plan based on American Diabetes Society guidelines.
📈 Results
The findings revealed that physician-generated plans had significantly fewer missing medications compared to those produced by GPT-4, with a p-value of 0.008. Conversely, GPT-4’s plans included fewer unnecessary medications (p=0.003). Notably, there was no significant difference in the accuracy of drug dosages (p=0.975), and the overall error scores were comparable between the two groups (p=0.301). However, safety concerns were raised, as 16% of GPT-4 plans exhibited potential risks.
🌍 Impact and Implications
The results of this study highlight the potential of LLMs like GPT-4 to assist in diabetes management by reducing unnecessary prescriptions. However, the findings also underscore the importance of human oversight and the need for improved algorithms to enhance the completeness and safety of AI-generated management plans. This dual approach could lead to better patient outcomes and more efficient healthcare delivery.
🔮 Conclusion
This study illustrates the promising role of AI in healthcare, particularly in generating management plans for chronic conditions like type 2 diabetes mellitus. While GPT-4 shows potential as a supplementary tool, it currently does not replace the comprehensive care provided by physicians. Ongoing research and development are crucial to refine these technologies and ensure their safe integration into clinical practice.
💬 Your comments
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Evaluating the Performance and Safety of Large Language Models in Generating Type 2 Diabetes Mellitus Management Plans: A Comparative Study With Physicians Using Real Patient Records.
Abstract
Background The integration of large language models (LLMs) such as GPT-4 into healthcare presents potential benefits and challenges. While LLMs show promise in applications ranging from scientific writing to personalized medicine, their practical utility and safety in clinical settings remain under scrutiny. Concerns about accuracy, ethical considerations, and bias necessitate rigorous evaluation of these technologies against established medical standards. Methods This study involved a comparative analysis using anonymized patient records from a healthcare setting in the state of West Bengal, India. Management plans for 50 patients with type 2 diabetes mellitus were generated by GPT-4 and three physicians, who were blinded to each other’s responses. These plans were evaluated against a reference management plan based on American Diabetes Society guidelines. Completeness, necessity, and dosage accuracy were quantified and a Prescribing Error Score was devised to assess the quality of the generated management plans. The safety of the management plans generated by GPT-4 was also assessed. Results Results indicated that physicians’ management plans had fewer missing medications compared to those generated by GPT-4 (p=0.008). However, GPT-4-generated management plans included fewer unnecessary medications (p=0.003). No significant difference was observed in the accuracy of drug dosages (p=0.975). The overall error scores were comparable between physicians and GPT-4 (p=0.301). Safety issues were noted in 16% of the plans generated by GPT-4, highlighting potential risks associated with AI-generated management plans. Conclusion The study demonstrates that while GPT-4 can effectively reduce unnecessary drug prescriptions, it does not yet match the performance of physicians in terms of plan completeness. The findings support the use of LLMs as supplementary tools in healthcare, highlighting the need for enhanced algorithms and continuous human oversight to ensure the efficacy and safety of artificial intelligence in clinical settings.
Author: [‘Mondal A’, ‘Naskar A’, ‘Roy Choudhury B’, ‘Chakraborty S’, ‘Biswas T’, ‘Sinha S’, ‘Roy S’]
Journal: Cureus
Citation: Mondal A, et al. Evaluating the Performance and Safety of Large Language Models in Generating Type 2 Diabetes Mellitus Management Plans: A Comparative Study With Physicians Using Real Patient Records. Evaluating the Performance and Safety of Large Language Models in Generating Type 2 Diabetes Mellitus Management Plans: A Comparative Study With Physicians Using Real Patient Records. 2025; 17:e80737. doi: 10.7759/cureus.80737