Enhancing Medical Diagnosis and Reporting: A Comparative Analysis of GPT-4 Omni and Gemini Advanced

Abstract: Medical diagnosis and reporting are critical components of effective healthcare, yet they are often hampered by time constraints, human error, and the complexities of clinical data. This study presents a comparative analysis of two Large Language Models (LLMs), GPT-4 Omni and Gemini Advanced, to assess their capabilities in enhancing the efficiency and precision of medical diagnosis and reporting. Using anonymized patient records and simulated clinical cases, the models were evaluated for their diagnostic accuracy and the speed of report generation. The results indicate that both LLMs show significant promise in enhancing diagnostic precision and speed of reporting, however, they also present unique strengths and limitations. GPT-4 Omni demonstrated a greater accuracy in diagnosing complex cases, whereas Gemini Advanced showed a faster report generation time. Both tools have the potential to support clinicians in making more accurate diagnoses and generating more comprehensive reports in a timely manner. The findings underscore the clinical implications for integrating LLMs into clinical workflows, whilst emphasizing the need for careful oversight, robust validation and ethical considerations, prior to implementation. Future research should focus on assessing the real-world impact of these tools, and also on developing clear guidelines for their integration into clinical practice. Keywords: AI, Research, GPT-4, Gemini Advanced