Summary of Establishing Rigorous and Cost-effective Clinical Trials For Artificial Intelligence Models, by Wanling Gao et al.

Establishing Rigorous and Cost-effective Clinical Trials for Artificial Intelligence Models

by Wanling Gao, Yunyou Huang, Dandan Cui, Zhuoming Yu, Wenjing Liu, Xiaoshuang Liang, Jiahui Zhao, Jiyue Xie, Hao Li, Li Ma, Ning Ye, Yumiao Kang, Dingfeng Luo, Peng Pan, Wei Huang, Zhongmou Liu, Jizhong Hu, Gangyuan Zhao, Chongrong Jiang, Fan Huang, Tianyi Wei, Suqin Tang, Bingjie Xia, Zhifei Zhang, Jianfeng Zhan

First submitted to arxiv on: 11 Jul 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary A novel paper emphasizes the importance of rigorous and cost-effective evaluation methodologies for artificial intelligence (AI) models in clinical practice. The study highlights the gap between AI research and its application in medicine, primarily due to limited evaluation methods. State-of-the-art evaluations often rely on laboratory studies or clinical trials with patient-centered controls, neglecting the crucial role of clinicians in collaborating with AI. To address this gap, the authors propose dual-centered AI randomized controlled trials (DC-AI RCTs) and virtual clinician-based in-silico trials (VC-MedAI) as effective proxies for DC-AI RCTs. The study leverages 7500 diagnosis records from two-step inaugural DC-AI RCTs across 14 medical centers with 125 clinicians, demonstrating the necessity of DC-AI RCTs and the effectiveness of VC-MedAI in replicating insights and conclusions from prospective DC-AI RCTs.
Low	GrooveSquid.com (original content)	Low Difficulty Summary A new study shows that AI models in medicine need to be tested more effectively. Right now, there’s a big gap between how AI is developed and how it’s used in hospitals. To close this gap, researchers propose two new ways to test AI: one where patients work with doctors and machines, and another virtual trial that simulates what would happen if humans were involved. The study uses real medical data from 14 hospitals and shows that these new testing methods are effective and can help develop better AI for medicine.