V. Harish is a fourth-year MD-PhD student, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; ORCID: https://orcid.org/0000-0001-6364-2439 .
F. Morgado is a fourth-year MD-PhD student, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; ORCID: https://orcid.org/0000-0003-3000-9455 .
Acad Med. 2021 Jan 1;96(1):31-36. doi: 10.1097/ACM.0000000000003707.
Estimates in a 1989 study indicated that physicians in the United States were unable to reach a diagnosis that accounted for their patient's symptoms in up to 90% of outpatient patient encounters. Many proponents of artificial intelligence (AI) see the current process of moving from clinical data gathering to medical diagnosis as being limited by human analytic capability and expect AI to be a valuable tool to refine this process. The use of AI fundamentally calls into question the extent to which uncertainty in medical decision making is tolerated. Uncertainty is perceived by some as fundamentally undesirable and thus, for them, optimal decision making should be based on minimizing uncertainty. However, uncertainty cannot be reduced to zero; thus, relative uncertainty can be used as a metric to weigh the likelihood of various diagnoses being correct and the appropriateness of treatments. Here, the authors make the argument, using as examples the experiences of 2 AI systems, IBM Watson on Jeopardy and Watson for Oncology, that medical decision making based on relative uncertainty provides a better lens for understanding the application of AI to medicine than one that minimizes uncertainty. This approach to uncertainty has significant implications for how health care leaders consider the benefits and trade-offs of AI-assisted and AI-driven decision tools and ultimately integrate AI into medical practice.
1989 年的一项研究估计，美国医生在多达 90%的门诊患者就诊中无法做出能解释患者症状的诊断。许多人工智能（AI）的支持者认为，从临床数据收集到医疗诊断的当前过程受到人类分析能力的限制，并期望人工智能成为完善这一过程的有价值工具。人工智能的使用从根本上质疑了在多大程度上可以容忍医疗决策中的不确定性。一些人认为不确定性是根本不可取的，因此，对他们来说，最佳决策应该基于最小化不确定性。然而，不确定性不能降低到零；因此，可以使用相对不确定性作为衡量各种诊断正确的可能性和治疗方法适当性的指标。在这里，作者通过两个 AI 系统（IBM Watson 在 Jeopardy 和 Watson for Oncology 上的经验）举例证明，基于相对不确定性的医疗决策为理解 AI 在医学中的应用提供了一个比最小化不确定性更好的视角。这种不确定性方法对医疗保健领导者如何考虑 AI 辅助和 AI 驱动的决策工具的收益和权衡以及最终将 AI 整合到医疗实践中具有重大意义。
