Over the years, healthcare has advanced greatly through various innovations and technological advancements. At the core of healthcare has always been the clinical decision making expertise of healthcare providers, who provide real-time knowledge and critical thinking elements to patient care. When providers start to utilize AI, they may maximize the benefits that the technology services provide, such as improving “the quality, efficiency, and effectiveness of healthcare services” (Khosravi et al, 2024), which can enhance healthcare delivery and improve patient outcomes by being able to provide the best treatment options for them.

Furthermore, AI can “free up clinicians’ time to focus more on their patients” (Elis, 2024), thereby allowing healthcare providers to regain the human aspect of care. AI can also support clinical decision making by analyzing large volumes of data more efficiently (MacIntyre et al., 2023) than human providers can, which, in turn, can help the diagnostic and intervention processes occur more quickly than relying solely on clinical knowledge and experience.

With all the benefits AI provides, healthcare professionals may become overly dependent on AI and rely less on the critical thinking skills developed through education and clinical experience, thus jeopardizing the patients they serve. While AI can provide beneficial recommendations that can help “improve diagnostic accuracy” (Kücking et al., 2026), if an AI system makes an incorrect recommendation, patients may be at risk of receiving an inaccurate diagnosis or an inappropriate intervention. In addition to risks to diagnostic accuracy, AI systems may introduce bias in how they analyze and present information. As AI systems grow, there is a risk that not all patient types and data will be available for AI to analyze properly and produce equitable results (Cross et al., 2024). Presenting biased data and results can lead to “substandard clinical decisions” (Cross et al, 2024), which can directly impact patients and their health. Additionally, “overreliance on performance metrics... may obscure bias” (Cross et al., 2024), underscoring the need for providers to double-check and verify AI-generated clinical findings and results before solely following them.

By understanding both the benefits and limitations of AI, providers can make more informed clinical decisions by using techniques such as their own critical thinking and the skills they have gained over years of hands-on clinical practice, in addition to the data AI provides. Providers can also address biases during the development of the AI model (Cross et al., 2024) to prevent discrimination across patient groups and pursue equitable healthcare for each patient treated. Ultimately, AI can strengthen clinical decision making when used appropriately, but it is most effective when paired with professional judgment, critical thinking and patient-centered care.