Artificial Intelligence in Medical Diagnosis: A Comprehensive Review

Artificial Intelligence (AI) is transforming healthcare, especially in medical diagnostics, by using the vast amount of digital medical data such as images, electronic health records (EHRs), lab results, and clinical notes. AI systems help improve diagnostic accuracy, reduce human error, and speed up clinical decision-making. Through methods like machine learning (ML), deep learning (DL), and natural language processing (NLP), AI can detect patterns in complex data that may be missed by humans. These technologies have shown strong potential in diagnosing cancers, heart diseases, neurological disorders, and infections. This paper reviews AI-based diagnostic technologies, explaining their methods, advantages, limitations, and real-world uses in fields like radiology, pathology, oncology, cardiology, and primary care. It also discusses challenges such as data quality, bias, model transparency, and regulations, along with key performance measures like accuracy, sensitivity, specificity, and AUC-ROC. As AI continues to evolve, trends such as explainable AI (XAI), federated learning, and multimodal data integration are helping create more reliable, transparent, and personalized diagnostic tools, while addressing ethical issues and guiding future development in modern medicine.