The promises and pitfalls of AI in healthcare | Atin Jindal | TEDxBryantU

Healthcare Challenges & Opportunities - AI in healthcare aims to augment human intelligence, not replace it, utilizing technologies like machine learning and natural language processing [4][5] - The healthcare industry faces challenges including information overload, clinician burnout, and wasteful spending, with 20% of costs considered wasteful and significant expenses related to billing and administration [8][10][11] - AI can improve diagnosis using image recognition, reduce documentation burden through automated note-taking, and enhance hospital flow by triaging patients and allocating resources [12][13][15] AI Adoption & Concerns - AI adoption in healthcare follows the Gartner hype cycle, with image recognition already productive but disease treatment and behavioral health still facing inflated expectations and disillusionment [6][7] - There is existing bias against AI-generated medical advice, with people finding it less reliable and empathetic compared to advice from human doctors [16][17] - Legal and ethical questions arise regarding data ownership, liability for incorrect AI advice, and potential loss of trust in manual processes due to AI involvement [18][19] - Bias can be built into AI systems through problem selection, data collection methods, and inherent assumptions, potentially leading to skewed outcomes [21] Future Vision - The future vision involves AI-powered wearable devices that can detect health issues, alert emergency services, and transmit vital information to hospitals, improving patient care and outcomes [22][23][24][25]