Artificial Intelligence in Medical Diagnostics: Performance, Validation Gaps, and Translational Challenges

Abstract

A great deal of progress has occurred in recent years in deep learning, self-supervised learning, and transformer-based architectures, which is why AI has become proficient at detecting health issues in individuals. It is true that AI systems tend to struggle when working alongside radiologists, pathologists, dermatologists, and cardiologists, despite operating relatively well in controlled environments. However, there exists an issue regarding global generalizability and the integration of the clinical aspect. This narrative review analyses the literature published between 2018 and 2025, applying specific parameters across various databases. A qualitative analysis was conducted of 126 publications that identified novel methods to integrate research and implementation activities into existing diagnostic methodologies. AI systems based on imaging have shown significant diagnostic performance in controlled environments; however, the greatest challenge is implementing them in real-world settings. It is anticipated that additional research will be conducted to determine the actual function of these systems in real-life applications. Certain AI frameworks are becoming more open, interconnected, and scalable without requiring imaging capabilities. Examples of these include explainable AI, multimodal systems, federated learning, and clinical decision support. Most models still demonstrate sensitivity to data-set shifts, lack appropriate external validation, and lack proof of impact on patient-centred outcomes, i.e., evidence of assisting patients. AI cannot be viewed as replacing medicine, but rather as providing an opportunity to better understand medicine. Extensive vetting and testing against real-world data are needed.

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