The integration of AI into healthcare is no longer speculative; AI is already reshaping how clinicians document care, interpret data, and communicate with patients. As AI-enabled tools continue to expand in scope and sophistication, their potential to support healthcare delivery grows. Realizing this potential depends not only on the capabilities of the technology itself but also on how it is evaluated, implemented, and used by healthcare institutions, clinicians, and patients. Addressing risks such as bias, hallucinations, and lack of transparency will require deliberate oversight, clear communication, and continued investment in training and governance. As this Issue Brief has outlined, equipping stakeholders with the knowledge to ask the right questions, and to recognize both the opportunities and limitations of AI, is essential to promoting safe, effective, and equitable adoption.
Key Points
- There are three primary types of AI used in healthcare: rule-based systems, traditional machine learning, and deep learning. Deep learning is the primary driver behind many recent advances.
- Deep learning models identify patterns, adapt based on feedback, and improve over time through repeated exposure.
- AI is already at work in healthcare across the diagnostic process—from triage and symptom checking to interpretation of clinical data and followup—illustrating its practical presence today, not just in the future.
- While AI offers the potential to improve efficiency and diagnostic support, it also introduces risks, including biased outputs, opaque reasoning, and errors such as hallucinations, all of which require careful oversight.
- As AI tools reshape clinical roles, clinicians must shift from being the sole decision-makers to including interpretations of AI output. This raises new concerns about cognitive biases, such as automation bias and complacency, which can impact safety.
- Safe and effective AI adoption requires shared responsibility: for healthcare leaders to ensure rigorous evaluation and ongoing oversight; for clinicians to understand AI’s capabilities and limitations and communicate them clearly; and for patients to feel empowered to ask informed questions about how AI is used in their care.
