Alemi Farrokh, Wojtusiak Janusz, Ursani Aneel, Eklou K Pierre, Lybarger Kevin
Health Administration and Policy, George Mason University, Fairfax, VA 22030, USA.
Klinic Inc., Seattle, WA 98101, USA.
Alpha Psychiatry. 2025 Jul 8;26(4):44608. doi: 10.31083/AP44608. eCollection 2025 Aug.
Herein, we report on the initial development, progress, and future plans for an autonomous artificial intelligence (AI) system designed to manage major depressive disorder (MDD). The system is a web-based, patient-facing conversational AI that collects medical history, provides presumed diagnosis, recommends treatment, and coordinates care for patients with MDD.
The system includes seven components, five of which are complete and two are in development. The first component is the AI's knowledgebase, which was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression to analyze extensive patient medical histories and identify factors influencing response to antidepressants. The second component is a series of adjustments to the knowledgebase designed to correct algorithm bias in patient subgroups. The third component is a conversational Large Language Model (LLM) that efficiently gathers patients' medical histories. The fourth component is a dialogue management system that minimizes digressions in the LLM conversations, using a topic network statistically derived from the AI's own knowledgebase. The fifth component is planned to enable real-time, human-in-the-loop monitoring. The sixth component is an existing analytical, non-generative module that provides and explains treatment advice. The seventh component is planned to coordinate care with clinicians via closed-loop referrals.
In component 1, the AI's knowledgebase correctly predicted 69.2% to 78.5% of the variation in response to 15 oral antidepressants. Patients treated by AI-concordant clinicians were 17.5% more likely to benefit from their treatment than patients of AI-discordant clinicians. In component 2, the use of the system required adjustments to improve accuracy for predicting the responses of African Americans to four antidepressants and no adjustments were required for the remaining 10 antidepressants. In component 3, the conversational intake efficiently covered 1499 relevant medical history events (including 700 diagnoses, 550 medications, 151 procedures, and 98 prior antidepressant responses). In the fourth component, the dialogue management system was effective in maintaining a long dialogue with many turns in the conversation. In the sixth component, the advice system was able to rely exclusively on pre-set text. An online ad campaign attracted 1536 residents of Virginia to use the advice system. Initially, a focus group of clinicians was skeptical of the value of the advice system and requested more prospective studies before they would implement the system in their clinics. When the system was redesigned to advise patients at home, clinicians were willing to receive referrals from the system and discuss the advice of the system with their patients.
Further research is needed to refine and evaluate the system. We outline our plans for a prospective randomized trial to assess the system's impact on prescription patterns and patient outcomes.
在此，我们报告一个旨在管理重度抑郁症（MDD）的自主人工智能（AI）系统的初步开发、进展及未来计划。该系统是一个基于网络、面向患者的对话式AI，可收集病史、提供初步诊断、推荐治疗方案并为MDD患者协调护理。
该系统包括七个组件，其中五个已完成，两个正在开发中。第一个组件是AI的知识库，它使用最小绝对收缩和选择算子（LASSO）逻辑回归构建，以分析大量患者病史并识别影响抗抑郁药反应的因素。第二个组件是对知识库的一系列调整，旨在纠正患者亚组中的算法偏差。第三个组件是一个对话式大语言模型（LLM），可有效收集患者的病史。第四个组件是一个对话管理系统，使用从AI自身知识库统计得出的主题网络，尽量减少LLM对话中的离题内容。第五个组件计划实现实时的人工介入监测。第六个组件是一个现有的分析性、非生成性模块，可提供并解释治疗建议。第七个组件计划通过闭环转诊与临床医生协调护理。
在组件1中，AI的知识库正确预测了15种口服抗抑郁药反应中69.2%至78.5%的变化。接受与AI一致的临床医生治疗的患者比接受与AI不一致的临床医生治疗的患者从治疗中获益的可能性高17.5%。在组件2中，使用该系统需要进行调整以提高预测非裔美国人对四种抗抑郁药反应的准确性，而对其余10种抗抑郁药则无需调整。在组件3中，对话式问诊有效地涵盖了1499个相关病史事件（包括700个诊断、550种药物、151项手术和98次先前的抗抑郁药反应）。在第四个组件中，对话管理系统在保持长对话且对话中有多个回合方面很有效。在第六个组件中，建议系统能够完全依赖预设文本。一项在线广告活动吸引了1536名弗吉尼亚居民使用该建议系统。最初，一组临床医生焦点小组对该建议系统的价值持怀疑态度，并要求进行更多前瞻性研究，然后才会在他们的诊所中实施该系统。当该系统重新设计为在家中为患者提供建议时，临床医生愿意接受该系统的转诊并与他们的患者讨论该系统的建议。
需要进一步研究来完善和评估该系统。我们概述了我们进行前瞻性随机试验的计划，以评估该系统对处方模式和患者结局的影响。
