AI in Healthcare: Triage, Accuracy, Bias & Privacy – A Review
The rapid advancement of artificial intelligence (AI) is prompting both excitement and caution within the medical community, particularly regarding its application to emergency triage. A growing body of research, including studies published as recently as 2025, highlights the potential of AI to improve triage accuracy and efficiency, but also underscores significant limitations and risks.
Recent studies demonstrate that large language models (LLMs) are increasingly being evaluated for their ability to assist in medical triage. The MedTriage benchmark, introduced in 2024, is designed to rigorously assess these models across diverse clinical scenarios, utilizing real-world clinician-patient dialogues. The competition spurred development of models like MedGPT-Guide, which employs a strategy of combining relevant and random data to achieve improved accuracy on the benchmark. However, researchers at Nature cautioned that while promising, these advancements require continued evaluation and refinement.
Despite progress, concerns remain about the reliability of AI in critical decision-making. A 2025 study published in Nature Communications found that LLMs often lack the “metacognition” necessary for reliable medical reasoning. This means the models can confidently provide incorrect answers without recognizing their own uncertainty. Further complicating matters, comparative studies, including one published in the Journal of Medical Internet Research in 2024, have shown varying degrees of accuracy between different symptom checkers like Ada and Symptoma, and even when compared to physicians.
The potential for bias in AI algorithms is also a significant concern. Research, including work cited by Noble in “Algorithms of Oppression,” demonstrates how algorithms can perpetuate and even amplify existing societal biases. In healthcare, this could lead to disparities in care, particularly for marginalized communities. Obermeyer et al. (2019) demonstrated racial bias in an algorithm used to manage population health, highlighting the need for careful scrutiny and mitigation strategies.
Practitioners themselves express mixed feelings. A 2023 survey published in Frontiers in Digital Health revealed that medical practitioners have varying perspectives on the integration of AI into emergency triage. Another study, also from 2023, found similar sentiments, with some expressing concerns about job displacement and the potential for over-reliance on technology. These concerns are amplified by the need to address data privacy and security, particularly in light of regulations like HIPAA and GDPR, as noted by Miles and Quinlan (2023) and Orel and Bernik (2018).
The development of standardized evaluation frameworks, such as the MedBench platform, is seen as a crucial step forward. However, researchers like Burnell et al. (2023) and Wang et al. (2024) emphasize the need to move beyond simple leaderboards and adopt more comprehensive benchmark suites that assess fairness and robustness. The Liquid Biopsy Consortium, established through the National Cancer Institute, provides a model for collaborative efforts to address technical and clinical challenges in biomarker development and validation, a strategy that could be applied to AI triage systems.
As of February 28, 2026, the National Institutes of Health has not issued comprehensive guidelines for the implementation of AI-driven triage systems, and the legal and regulatory frameworks surrounding their use remain largely undefined. Further research and careful consideration of ethical implications are needed before widespread adoption can occur.