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🧠 AI🟢 BullishImportance 7/10
A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic
arXiv – CS AI|Peter Brodeur, Jacob M. Koshy, Anil Palepu, Khaled Saab, Ava Homiar, Roma Ruparel, Charles Wu, Ryutaro Tanno, Joseph Xu, Amy Wang, David Stutz, Hannah M. Ferrera, David Barrett, Lindsey Crowley, Jihyeon Lee, Spencer E. Rittner, Ellery Wulczyn, Selena K. Zhang, Elahe Vedadi, Christine G. Kohn, Kavita Kulkarni, Vinay Kadiyala, Sara Mahdavi, Wendy Du, Jessica Williams, David Feinbloom, Renee Wong, Tao Tu, Petar Sirkovic, Alessio Orlandi, Christopher Semturs, Yun Liu, Juraj Gottweis, Dale R. Webster, Jo\"elle Barral, Katherine Chou, Pushmeet Kohli, Avinatan Hassidim, Yossi Matias, James Manyika, Rob Fields, Jonathan X. Li, Marc L. Cohen, Vivek Natarajan, Mike Schaekermann, Alan Karthikesalingam, Adam Rodman|
🤖AI Summary
Google's AMIE conversational AI successfully completed a clinical feasibility study with 100 patients at an academic medical center, demonstrating 90% accuracy in including correct diagnoses and achieving high patient satisfaction. The AI showed comparable diagnostic quality to primary care physicians while requiring no safety interventions during real-world clinical interactions.
Key Takeaways
- →AMIE achieved 90% accuracy in including the final diagnosis in its differential diagnosis list, with 75% top-3 accuracy.
- →All 100 patient interactions were completed safely without requiring human supervisor intervention based on predefined safety criteria.
- →Patients reported high satisfaction and improved attitudes toward AI after interacting with AMIE.
- →Primary care physicians found AMIE's diagnostic output useful and noted positive impact on their preparedness.
- →While AMIE matched physicians in diagnostic quality, physicians outperformed in practicality and cost-effectiveness of treatment plans.
#ai-healthcare#clinical-ai#conversational-ai#medical-diagnosis#llm#healthcare-automation#patient-care#clinical-study
Read Original →via arXiv – CS AI
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