Can AI Chatbots Effectively Triage Patient Symptoms?

By Rasit Dinc

Artificial intelligence (AI) is rapidly transforming the healthcare landscape, with AI-powered chatbots emerging as a promising tool for patient triage. These chatbots can interact with patients, assess their symptoms, and provide initial guidance, potentially streamlining the triage process and improving healthcare efficiency. However, the question remains: can AI chatbots effectively and safely triage patient symptoms? This article explores the current state of AI in patient triage, examining its effectiveness, accuracy, and the challenges that need to be addressed for its widespread adoption.

A recent study published in Frontiers in Public Health highlights the transformative role of AI-powered hybrid chatbots in healthcare. These chatbots, which combine AI with human input, have demonstrated significant benefits, including reducing hospital readmissions by up to 25%, improving patient engagement by 30%, and cutting consultation wait times by 15% [1]. They are increasingly used for chronic disease management, mental health support, and patient education, showcasing their potential to enhance healthcare delivery in both developed and developing countries [1].

However, the effectiveness of AI in patient triage is a subject of ongoing research and debate. A study published in the International Journal of Emergency Medicine evaluated the accuracy of an AI chatbot in triaging patients in the emergency department (ED) [2]. The study found a high agreement rate (85.61%) between the AI chatbot and ED physicians, with substantial inter-rater reliability. This suggests that AI can align closely with human decision-making in a triage setting. However, the study also revealed that the AI chatbot had a tendency to overestimate the acuity of cases, particularly in critical situations. This over-triaging could lead to the misallocation of resources and increased healthcare costs [2].

The accuracy of AI in medical diagnosis is a critical factor in its effectiveness for triage. While some studies have shown impressive accuracy rates for AI in diagnosing specific conditions, the overall diagnostic accuracy of AI models can vary. A systematic review and meta-analysis of 83 studies found an overall diagnostic accuracy of 52.1% for AI models, with no significant performance difference between AI models and physicians [3]. This suggests that while AI has the potential to match human performance, it is not yet a perfect tool.

Several challenges need to be addressed to ensure the safe and effective use of AI chatbots for patient triage. Trust is a major barrier, as patients may be hesitant to rely on AI for medical advice due to concerns about accuracy, data privacy, and the ability of AI to understand the nuances of human health [1]. Integrating AI chatbots with existing healthcare systems is another significant challenge, as many systems lack the necessary infrastructure to support seamless data exchange [1]. Furthermore, the cultural and linguistic adaptability of AI chatbots needs to be improved to ensure they can effectively serve diverse patient populations [1].

In conclusion, AI chatbots have the potential to be a valuable tool in patient triage, offering benefits such as improved efficiency and patient engagement. However, their effectiveness is dependent on their accuracy, which can vary, and their tendency to over-triage is a concern. Addressing the challenges of trust, system integration, and cultural adaptability is crucial for the successful implementation of AI in patient triage. While AI is not yet ready to replace human healthcare professionals, it can serve as a powerful support tool, augmenting their capabilities and improving the overall quality of care.

References

[1] Wah, J. N. K. (2025). Revolutionizing e-health: the transformative role of AI-powered hybrid chatbots in healthcare solutions. Frontiers in Public Health, 13, 1530799. https://pmc.ncbi.nlm.nih.gov/articles/PMC11865260/

[2] Alomari, L. M., Alshammari, M. M., Arbaeen, A. O., Alshehri, R. A., & Almalki, H. S. (2025). Safety and accuracy of AI in triaging patients in the emergency department. International Journal of Emergency Medicine, 18(1), 243. https://pmc.ncbi.nlm.nih.gov/articles/PMC12636208/

[3] Takita, H., et al. (2025). A systematic review and meta-analysis of diagnostic ... npj Digital Medicine, 8(1), 1-11. https://www.nature.com/articles/s41746-025-01543-z