Abstract
Background Globally we face a projected shortage of 11 million healthcare practitioners by 2030, and administrative burden consumes 50% of clinical time. Artificial intelligence (AI) has the potential to help alleviate these problems. However, no end-to-end autonomous large language model (LLM)-based AI system has been rigorously evaluated in real-world clinical practice. In this study, we evaluated whether a multi-agent LLM-based AI framework can function autonomously as an AI doctor in a virtual urgent care setting.
Methods We retrospectively compared the performance of the multi-agent AI system Doctronic and board-certified clinicians across 500 consecutive urgent-care telehealth encounters. The primary end points: diagnostic concordance, treatment plan consistency, and safety metrics, were assessed by blinded LLM-based adjudication and expert human review.
Results The top diagnosis of Doctronic and clinician matched in 81% of cases, and the treatment plan aligned in 99.2% of cases. No clinical hallucinations occurred (e.g., diagnosis or treatment not supported by clinical findings). In an expert review of discordant cases, AI performance was superior in 36.1%, and human performance was superior in 9.3%; the diagnoses were equivalent in the remaining cases.
Conclusions In this first large-scale validation of an autonomous AI doctor, we demonstrated strong diagnostic and treatment plan concordance with human clinicians. These findings indicate that multi-agent AI systems can achieve comparable clinical decision-making to human providers and offer a potential solution to healthcare workforce shortages.
Competing Interest Statement
All authors are equity owners of Doctronic Inc.
Funding Statement
This study did not receive any funding
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
Ethics committee of Doctronic Research Group gave ethical approval for this work. The work was deemed to utilize only de-identified and unlinked data from an EHR for the study.
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Yes
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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
Yes
Data Availability
All data produced in the present study are available upon reasonable request to the authors