Clinical decision-making is the process by which healthcare professionals use their clinical knowledge, expertise, and judgment to determine the best course of action for a patient's care. It involves integrating information from multiple sources, including the patient's medical history, physical examination, diagnostic tests, and other relevant factors, to make an informed decision.
There are several models of clinical decision-making, including the rational model, intuitive model, and the dual-process model. The rational model involves a systematic and analytical approach to decision-making, while the intuitive model relies on intuition and experience. The dual-process model combines both approaches, recognizing that both analytical and intuitive processes are necessary for effective decision-making.
Factors that can influence clinical decision-making include the patient's preferences and values, the available evidence and guidelines, the healthcare professional's experience and expertise, and the resources available for care. Shared decision-making, in which the patient and healthcare professional collaborate to make decisions together, has become increasingly emphasized as an important aspect of clinical decision-making.
Clinical decision-making is a critical component of healthcare, as it helps to ensure that patients receive appropriate and effective care. By using their clinical expertise and integrating evidence-based practice, healthcare professionals can make informed decisions that lead to the best possible outcomes for their patients.
Artificial intelligence (AI) continues to advance in healthcare, offering innovative approaches to enhance clinical decision-making and patient management. Peripheral nerve surgery poses unique challenges due to the complexity of cases and the need for precise diagnostic and therapeutic strategies. This study investigates the application of OpenAI's generative AI model, o1, in assisting with intricate decision-making processes in peripheral nerve surgery. Utilizing advanced prompt engineering techniques, o1 was configured as a virtual medical assistant (GPT-NS) to process five simulated clinical scenarios modeled after real-world cases. The AI guided surgeons through medical history, diagnostics, and treatment planning, culminating in case summaries. A panel of nerve surgery specialists and residents evaluated the AI's performance using a Likert scale across seven criteria. GPT-NS demonstrated strong capabilities, achieving an average score of 4.3. High ratings were observed for understanding clinical issues and case presentation clarity. However, areas for improvement were noted in diagnostic sequencing and treatment recommendations. Despite a lower score indicating human evaluators' perception of their superiority over the AI in handling cases, GPT-NS showed promise as a supportive tool in clinical practice. As the performance of LLM (Large Language Model) AI continues to improve, it is becoming increasingly important that absolute experts assess the accuracy of the answers to ensure reliable and clinically sound integration into healthcare practices. This study underscores the potential of LLM AI in augmenting clinical decision-making in highly specialized fields like peripheral nerve surgery while demonstrating the ongoing importance of human expertise. Future research should explore ways to further refine AI capabilities and assess its integration into routine surgical workflows 1)