Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
J Med Internet Res. 2020 Aug 26;22(8):e19918. doi: 10.2196/19918.
In contrast with medical imaging diagnostics powered by artificial intelligence (AI), in which deep learning has led to breakthroughs in recent years, patient outcome prediction poses an inherently challenging problem because it focuses on events that have not yet occurred. Interestingly, the performance of machine learning-based patient outcome prediction models has rarely been compared with that of human clinicians in the literature. Human intuition and insight may be sources of underused predictive information that AI will not be able to identify in electronic data. Both human and AI predictions should be investigated together with the aim of achieving a human-AI symbiosis that synergistically and complementarily combines AI with the predictive abilities of clinicians.
与近年来人工智能（AI）驱动的医学成像诊断不同，患者预后预测是一个具有挑战性的问题，因为它关注的是尚未发生的事件。有趣的是，基于机器学习的患者预后预测模型的性能在文献中很少与人类临床医生进行比较。人类的直觉和洞察力可能是被人工智能无法从电子数据中识别出来的潜在预测信息的来源。应该一起研究人类和人工智能的预测结果，以实现人机共生，将人工智能与临床医生的预测能力相结合，达到协同互补的效果。
