Though still in its infancy as a field, artificial intelligence (AI) is poised to transform the practice of medicine and the delivery of healthcare. Powered by breakthroughs in machine learning (ML) algorithms, enhanced computing power, and increasing data volume and storage capacity, AI has made noteworthy advances over the past decade across many medical subspecialties. Experts predict AI-based medical devices and algorithms will play a major role in the delivery of preventive, diagnostic, and therapeutic interventions. In addition to our occasional blog on the topic (see recent example), our Public Health Genomics and Precision Health Knowledge Base (PHGKB) and our weekly update display the latest scientific literature, evidence synthesis, guidelines, evaluation, and implementation studies for the applications of AI in a wide variety of diseases across the lifespan.
A recent Nature Medicine article discusses promising uses of artificial intelligence in medicine, particularly in medical imaging and big data integration, and considers technical and ethical challenges for their applications in improving human health. Here is a quick summary of the review and the implications for population health.
In the interpretation of medical images — a niche where AI models have made great strides — the AI workflow starts with images that have been read and annotated by human experts. The AI model can analyze and interpret images and compare its interpretation to that of human experts. AI can then learn and refine its interpretation models over time and after analyzing numerous images. AI tools have shown that they can meet, or even exceed, experts’ performance across medical specialties that rely on human interpretation — namely, radiology, pathology, dermatology, gastroenterology, and ophthalmology. For instance, one study used AI methods to analyze whole-slide images and demonstrated that their model was more accurate in predicting patient survival from malignant mesothelioma, compared to current pathology practices. Another study demonstrated that an AI model for the optical diagnosis of colorectal cancer can achieve precision comparable to that of skilled endoscopists. Such advances have demonstrated how AI can refine diagnostic accuracy and improve patient outcome predictions, while enabling a faster clinical workflow and more efficient use of healthcare resources.