This lecture will provide an overview of history, challenges, current state, and future potential of AI in healthcare from the perspective of a neuroradiology physician-scientist. Recent technical advances including foundation models, generative AI, agentic AI, and quantum computing, offer novel approaches to complex problem-solving in healthcare. However, medical technology implementation is constrained by several parameters including patient safety, privacy, and market considerations. Clinical AI development requires a clear understanding of intended use cases, data quality, computing power, and postmarket surveillance. We will investigate clinical and research applications in radiology, which has led the medical AI revolution with routine storage of massive digital datasets used for training large ML/DL models. Several clinical AI solutions are available for image acquisition, enhancement, detection, classification, diagnosis, and clinical decision support. However, current FDA-cleared tools represent narrow models that lack generalizability and adaptability. With the advent of GAI, we are seeing broader relevance to multimodal datasets and multitask applications in various domains. As AI technologies increasingly permeate clinical care, strong legal and governance frameworks are needed to mitigate failure modes and provide human oversight. We will highlight promising research collaborations with MU IDSI in health informatics, bioinformatics, and geoinformatics. Emerging technologies and transdisciplinary interactions offer exciting opportunities to reimagine the future of healthcare with evolutionary multi-omics, precision theranostics, human–robot collaboration, medical metaverse, and multi-agent superintelligence.
