Dr Krish Rawal, Infinity Group Medical Director, examines the role of Artificial Intelligence in medicine.

This week I wanted to look a little bit more closely at the role of artificial intelligence (AI) in medicine. I am sure many of us use ChatGPT or another AI system to look up symptoms, and the honest truth is that the options and responses that AI provides are often really good. But I suppose there is also a worry as to whether consulting AI for a medical issue is as good or as safe as consulting a medical professional. Certainly AI is more available for consultation!

AI is increasingly being integrated into clinical care, particularly through what are known as clinical decision support systems (CDSS) that analyse large volumes of patient data and provide evidence-based recommendations to clinicians. These systems use machine learning and natural language processing to interpret electronic health records, laboratory results, imaging data, and clinical guidelines in real time. Evidence from systematic reviews indicates that AI-enabled CDSS can improve clinical decision-making by delivering patient-specific insights and highlighting diagnostic or therapeutic options that might otherwise be overlooked. Studies have shown that such systems can enhance diagnostic accuracy, optimise treatment selection, and reduce medical errors, thereby contributing to improved patient outcomes and more efficient healthcare delivery. The strength of these systems is the ability to analyse very large volumes of data extremely quickly, but the human interaction is lost and there are verbal and non-verbal cues that a medical professional can pick up on that an AI tool cannot.

AI is already demonstrating measurable benefits in several clinical domains. For example, AI-assisted diagnostic tools in Radiology and screening programmes have improved early disease detection and reduced clinician workload. A large Swedish study of approximately 100,000 women found that incorporating AI into breast cancer screening programmes increased early cancer detection and reduced later diagnoses by about 12%, while also reducing the workload for Radiologists. Similarly, clinical trials evaluating AI-assisted diagnostic support have shown meaningful improvements in diagnostic accuracy; in one study, physicians using AI support improved diagnostic accuracy from 47% to 65% when assessing clinical cases. These findings illustrate how AI functions most effectively as a decision-support tool that augments, rather than replaces, clinician judgement. 

Despite these promising developments, the integration of AI into clinical practice raises important challenges. Concerns include bias, transparency of decision-making (“explainability”), data privacy, and the need for rigorous clinical validation before widespread adoption. Regulatory bodies such as the U.S. Food and Drug Administration have begun evaluating and approving AI-enabled medical devices, reflecting the growing recognition that these technologies must meet standards of safety and effectiveness comparable to other medical tools. As research continues and governance frameworks evolve, AI is likely to become an increasingly important component of precision medicine and data-driven healthcare, supporting clinicians in delivering more accurate, personalised, and efficient patient care.

So it seems that my job is safe for the time being, but like all of us we have to embrace this new technology I am very happy to use a patient's background research into their own condition as part of my medical discussion and consultation. And of course the beauty of AI is that the more we use it and the more data input, the more accurate it becomes.