How Does AI Support Guideline Adherence in Clinical Practice?

Author: Rasit Dinc

Introduction

Clinical guidelines are the bedrock of modern, evidence-based medicine. They provide healthcare professionals with a framework for making informed decisions about patient care, ensuring that treatments are both safe and effective. However, the consistent application of these guidelines in busy clinical settings presents a significant challenge. The sheer volume of medical literature, coupled with the unique complexities of individual patient cases, can make it difficult for clinicians to stay abreast of the latest recommendations and apply them consistently. This is where Artificial Intelligence (AI) is emerging as a powerful ally, offering innovative solutions to enhance guideline adherence and, ultimately, improve patient outcomes.

AI-Powered Assessment of Guideline Adherence

One of the most promising applications of AI in this domain is its ability to automatically assess adherence to clinical guidelines. Recent research has demonstrated that Large Language Models (LLMs), a type of AI, can be trained to review clinical trial reports and other medical documents to determine whether they comply with established reporting standards. For instance, a 2025 study by Wrightson et al. found that the GPT-4 model could assess reporting guideline compliance with approximately 90% accuracy [1]. This capability has the potential to revolutionize the peer-review process and ensure that published research adheres to the highest standards of transparency and rigor. By automating the tedious process of checking for guideline adherence, AI can free up valuable time for researchers and clinicians, allowing them to focus on the more nuanced aspects of their work.

AI in Clinical Decision Support Systems (CDSS)

The integration of AI into Clinical Decision Support Systems (CDSS) represents another significant step forward in promoting guideline adherence. The concept of "Compliance by Design" (CbD) is central to this effort, where AI-based CDSS are developed with built-in mechanisms to ensure that their recommendations align with current clinical guidelines. However, as Pardoux and Kerasidou (2025) argue, a rigid, purely automated approach to compliance is not always desirable [2]. Clinical guidelines are, by their nature, general recommendations and may not be applicable to every patient, especially those with complex comorbidities. Therefore, a more nuanced, socio-technical approach is required, where AI acts as a supportive tool within the broader clinical decision-making process. This approach acknowledges the importance of clinical expertise and patient-specific factors, allowing for a more flexible and context-aware application of guidelines.

Comparing AI and Human Experts

A 2025 study by Ucdal et al. provided compelling evidence for the potential of AI to enhance guideline adherence by comparing the performance of AI models with that of human experts in managing dyslipidemia [3]. The study found that AI models demonstrated superior adherence to clinical guidelines in standardized scenarios. However, the study also highlighted the complementary strengths of human experts, who were better at considering contextual factors such as frailty and life expectancy. The most promising finding was that a hybrid AI-human approach, where the AI provides guideline-based recommendations and the clinician applies their expertise to tailor them to the individual patient, resulted in the highest rate of LDL-C target attainment. This underscores the idea that AI is most effective when used as a tool to augment, rather than replace, human intelligence.

Conclusion

Artificial Intelligence holds immense promise for improving guideline adherence in clinical practice. From automating the assessment of reporting standards to providing real-time decision support, AI-powered tools can help clinicians navigate the complexities of modern medicine and deliver the best possible care to their patients. However, it is crucial to recognize that AI is not a panacea. The most effective implementation of AI in this context will be one that embraces a collaborative, human-in-the-loop approach, leveraging the strengths of both artificial and human intelligence to optimize patient outcomes. As AI technology continues to evolve, further research is needed to explore its full potential and ensure its safe and ethical integration into the healthcare landscape.

References

[1] Wrightson, J. G., Blazey, P., Moher, D., Khan, K. M., & Ardern, C. L. (2025). GPT for RCTs? Using AI to determine adherence to clinical trial reporting guidelines. BMJ Open, 15(3), e088735. https://pmc.ncbi.nlm.nih.gov/articles/PMC11927406/

[2] Pardoux, É., & Kerasidou, A. (2025). Compliance with Clinical Guidelines and AI-Based Clinical Decision Support Systems: Implications for Ethics and Trust. Science and Engineering Ethics, 31(34). https://link.springer.com/article/10.1007/s11948-025-00562-z

[3] Ucdal, M., Yurtsever, K., Yildiz, P., Akalin, A., Mert, K. U., & Guven, G. S. (2025). Comparison of Artificial Intelligence Models and Human Experts in Managing Dyslipidemia: Assessment of Adherence to Clinical Guidelines. Cureus, 17(8), e91363. https://pubmed.ncbi.nlm.nih.gov/40904968/