Large language medicine: defining a new paradigm in human health

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Artificial intelligence (AI) is poised to revolutionize our understanding of disease and pre-disease, which could transform the way medicine is practiced. Advances in deep learning systems, alongside the advent of generative AI and large language models, promise to usher in an era of multimodal AI systems that permeate every aspect of medicine. AI is expected to support the full spectrum of public health and clinical functions, including surveillance, monitoring, protection, health promotion, and disease prevention. In parallel, molecular biology has been revolutionized by AI solutions that can accurately model proteins and other biological molecules at scale with the potential to accelerate drug discovery and reshape our understanding of disease and pre-disease mechanisms. However, AI has yet to be incorporated into common day-to-day practice, underscoring the difficulty of clinical integration. Achieving this goal will require advancing algorithmic architecture, sourcing higher-quality multimodal data, increasing processing power and efficiency, developing secure and fit-for-purpose data infrastructures, ensuring interoperability with diverse health systems and clinical workflows, and establishing regulatory pathways to protect patient safety and clarify clinician liability. The potential emergence of autonomous systems capable of carrying out an increasing number of clinical tasks raises important ethical and legal questions about accountability and responsibility in patient care. This review explores the current state of AI in medicine, highlighting that current clinically mature and regulatory-approved products are based on deep learning architecture and are predominantly diagnostic. Robust regulatory frameworks and ethical guidelines must be established to govern the development and deployment of AI, ensuring alignment with patient safety, clinical guidelines, and public trust. Multidisciplinary collaboration among clinicians, researchers, technologists, ethicists, regulators, and policymakers is essential moving forward.