Artificial intelligence (AI) is rapidly becoming more integral to both biologic discovery and the practice of medicine. In the Journal of Insurance Medicine, Dr. Tim Meagher traces AI’s evolution, from early rules-based systems to today’s deep learning and generative AI, and examines its growing influence on diagnostics, research, and clinical efficiency.

Across radiology, ophthalmology, cardiology, dermatology, oncology, and other specialties, AI-enabled tools have the potential to improve diagnostic workflows by supporting accuracy, assisting with image interpretation, and identifying patterns that may warrant further clinical evaluation. At the same time, administrative tools such as automated documentation and workflow optimization are beginning to show promise for easing some of the operational pressures faced by healthcare providers.

Despite these gains, adoption remains slow. Fragmented data, regulatory uncertainty, algorithmic bias, clinician resistance, and cost barriers continue to impede widespread implementation. Even so, Dr. Meagher notes that over the next decade, AI has the potential to advance precision medicine, preventive care, cancer detection, and biomedical discovery. If this trajectory continues, improvements in morbidity, mortality, and the depth of risk‑relevant information available to insurers may reasonably be expected.

For insurance medicine, the electronic health record (EHR) will be especially significant. As multimodal data and wearable-derived insights flow into future EHRs, underwriting will have access to more comprehensive information about individual health risk, reshaping medical risk selection.