Three key lessons for regulating disruptive medical technologies, though AI's rapid evolution indicates a need for novel regulatory paradigms, possibly including self-regulation.
There is an urgent need for innovation in clinical research, and whilst AI has huge potential to offer improvements, there is a necessity for a robust regulatory framework to ensure reliability and equity.
The rapid growth of large language models holds promise for assisting clinical decision-making, but they cannot, and should not, take place of all the combined cognitive and perceptive capabilities of clinicians.
While AI holds vast potential for healthcare, slow adoption stems from data hurdles, workflow disruption, and a lack of long-term vision.
Artificial intelligence in healthcare and its Oppenheimer moment: duality, accountability, and uncertainty
Oppenheimer's atomic bomb and AI share duality, lack of regulation, and uncertainty, but AI can become a force for good, revolutionizing diagnosis, treatment, and patient care.
A groundbreaking international meeting focused on the integration of AI into medical education, highlighting the Chinese University of Hong Kong at Shenzhen's innovative AI-driven curriculum and the growing global interest in AI education for healthcare professionals
Despite facing skepticism and challenges, artificial intelligence is gradually gaining acceptance and adoption, similar to previous technological innovations like the Model T automobile, automatic elevators, and home desktop computers.