AI Applications in Medical Reporting and Diagnosis

The integration of artificial intelligence (AI) in healthcare is revolutionizing diagnosis and patient care by improving clinical documentation and the management of electronic health records that depend on medical image interpretation, increasing accuracy, and reducing time. Egypt ranks first in liver disease and second in liver cancer mortality worldwide in 2020. Large language models, a subset of AI techniques, can assist in disease diagnosis. LLM models with multimodal capabilities can classify and describe patient scan images and extract information from clinical notes. These models can extract vital diagnoses with the support of prompt engineering, as one of these models can answer questions, summarize information, and translate complex medical terminology into plain language, enabling patients to understand their medical reports and diagnoses. There are two primary approaches to achieving this. First, fine-tuning can adapt the model to medical data, which can be resource-intensive. The second approach, pre-trained LLM models can be utilized to leverage pre-trained models to perform the necessary tasks, focusing on effectively using prompts to guide the model for precise and relevant outputs. This study highlights the role of generative AI models by focusing on prompt engineering, and how carefully crafting prompts can enhance the effectiveness of LLM models in medical applications with high accuracy. It demonstrates this through experiments using pre-trained models based on semantic similarity with GPT-4o and BioGPT. Implementing a zero-shot model for liver tumor classification is one of the prompt engineering techniques. The performance metrics achieved were impressive, accuracy, precision, recall, and F1-scores are 88, 81, 88, and 83 percent, respectively.