Evaluating the o1 Model's Impact on Decision-Making in Medical AI Agents
Integrating the o1 model into medical AI agents significantly improves diagnostic accuracy and reasoning consistency, particularly in complex medical scenarios, but potential drawbacks like increased computational demands require further investigation.
PIORS: Using Large Language Models and Simulation to Improve Outpatient Reception in China
This paper introduces PIORS, a system that uses a large language model (LLM) trained with a novel simulation framework to improve the efficiency and quality of outpatient reception in Chinese hospitals.
PediatricsGPT: A Chinese Large Language Model for Pediatric Applications
This paper introduces PediatricsGPT, a new large language model specifically trained on a massive dataset of Chinese pediatric medical texts to address the shortage of pediatricians and improve healthcare access in China.
Large Language Models as Second Opinion Tools in Medicine: An Analysis of Performance and Limitations
While not ready to replace human physicians, large language models (LLMs) show promise as valuable tools for generating second opinions in complex medical cases, particularly by offering comprehensive differential diagnoses and potentially mitigating cognitive biases in clinical decision-making.
A Practical Guide to Using Large Language Models in Medicine
Large language models (LLMs) hold immense potential for revolutionizing healthcare, but their successful implementation requires a structured approach encompassing task formulation, model selection, prompt engineering, fine-tuning, and careful consideration of deployment factors like regulatory compliance, equity, and cost.
Real-World Challenges and Risks of Using Large Language Models in Healthcare
While Large Language Models (LLMs) show promise in healthcare, significant challenges regarding their operational vulnerabilities, ethical implications, performance evaluation, and legal compliance must be addressed before their safe and effective integration into real-world clinical practice.
Improving Diagnostic Accuracy of Large Language Models in Simulated Clinical Environments Through Adaptive Reasoning and Acting
Large language models (LLMs) can achieve higher diagnostic accuracy in simulated clinical environments by incorporating an adaptive reasoning framework that allows them to learn from incorrect diagnoses and refine their decision-making process over time.
PharmacyGPT: Exploring the Potential of Large Language Models for Intensive Care Unit Pharmacotherapy Management
PharmacyGPT, a framework leveraging large language models (LLMs) like ChatGPT and GPT-4, shows promise in assisting with complex pharmacotherapy management in the ICU, but further development and integration of domain expertise are crucial for real-world application.
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