“Above all else, do no harm.” The Hippocratic Oath remains the ultimate guiding principle in medicine. As artificial intelligence (AI) rapidly spreads through clinical care teams, the medical community faces a pivotal test. Using AI to guide patient care decisions requires a clear, discerning eye. Understanding foundational source content and governance is now essential for every clinician and healthcare organization.
When reviewing clinical information to make patient care decisions, healthcare providers often use three considerations:
- What is the information, and where (and who) does it come from?
- How is this information judged or evaluated?
- How can care teams incorporate it into actual patient care?
When AI enters the workflow, a fourth critical consideration emerges:
- Who builds and monitors the technology generating these responses?
Given the nature of generative AI is to always provide an answer, there is a potential tension in its use in a field where evidence is not always clear, and lives are on the line. Therefore, monitoring the quality and accuracy of AI outputs depends on human expertise for validation and accountability. Clinical AI systems should be grounded in human-generated, expertly curated content that reflects the wider body of medical literature.
Research findings require clinical context
Clinicians know they must always review evidence within a wider clinical context. No system is infallible, and no system should rely solely on unfiltered streams of medical research when informing patient care. Why? Individual research studies can carry author bias, hold unstated business ties, or quickly become outdated due to newer data. Simply having access to information is not enough; clinicians must exercise sharp discernment. True clinical intelligence requires fluency in evidence-based medicine, critical thinking, and a willingness to evaluate new forms of knowledge synthesis.
Determining the most effective treatment plan or precise drug dosage requires context and accountability. Any generative AI response used in patient care should go beyond a basic summary and be subject to critical analysis. Clinicians need to assess whether the tool is delivering clinically sound information or just producing a plausible response based on patterns in language. They also need to verify if it references the absolute latest research report instead of an outdated version. Finally, they must check if it provides the findings of a single study without considering other papers that challenge those findings.
Without this deep contextual framing, AI tools risk sharing incorrect medical advice, even when they reference authoritative sources.
One important marker of a safe, credible clinical AI tool is its ability to recognize when it does not have an answer. In medical training, learning when to say “I don’t know” is a fundamental milestone. The best surgeons know when not to operate, and the best clinicians recognize that a quick answer is not always the right one. When clinicians turn to software for guidance, they need responses grounded in evidence and context—not a plausible-sounding guess. To support systems that can safely deliver a non-answer, developers must build strict safeguards, rubrics, and standards. High-quality systems ensure clinical claims are traceable and verifiable. As patients ourselves, we aren’t immune to the harm that can come from an AI tool trying to guess a medical diagnosis. These outputs impact real lives every single day.
The traditional clinician-authored editorial process remains the foundation of medical knowledge. As new clinical studies emerge, human experts review them, interpret them within the broader clinical context, and assess the strength of the resulting care recommendations.
Integrating AI is not a departure from this process, but an extension of the same values and rigor into a new technological era. In addition to shaping foundational content, clinical experts must also guide how AI-generated responses are developed and delivered. We refer to this as a clinical intelligence model.
In this model, human expertise remains embedded at key stages:
- Clinical experts define the clinical logic that guides AI response generation, establishing clear boundaries and guardrails.
- They evaluate outputs for clinical intent, accuracy, and appropriateness at the point of care.
- They monitor feedback and continuously audit system performance for safety and reliability.
- They refine and improve the system over time, incorporating advances in technology, new clinical evidence, and real-world feedback.
The established process of vetting medical literature has decades of proven value. As both the volume of information and the capabilities of AI continue to grow, it is essential that these systems are built and maintained with the same clinical rigor, accountability, and continuous oversight that have long defined medical knowledge development.
The opportunity for AI in patient care carries incredible long-term potential. However, the gravity and consequence of medical decision-making reinforce the need for human expertise to remain central.
To maximize its value, it needs to build on the deep history of human clinical thinking. When applied with rigorous standards and accountability, AI will move healthcare forward by complementing human expertise, not replacing it.
About Dr. Sheila Bond
As Director of Clinical Content Strategy, Dr. Bond leads initiatives to ensure clinical content continuously evolves to meet the needs of users, customers, and their environment. Her life’s work is centered around improving healthcare decision-making and patient outcomes by creating content that improves diagnostic and therapeutic accuracy, with particular emphasis on bringing technology to the fore. Dr. Bond believes that high-quality information should be accessible and understandable to anyone who needs to make a healthcare decision and that incorporating new technologies like generative AI can enable universal accessibility when properly trained.
In addition to her work with Wolters Kluwer, Dr. Bond maintains a faculty position at the Brigham and Women’s Hospital and Harvard Medical School, where she cares for patients, teaches the next generation of healthcare providers, and continues research. She received her BA from Columbia University, New York City, NY, and MD from Northwestern University, Chicago, IL.
Dr. Bond combines practicality, collaboration, and forward-thinking innovation to push the boundaries of possibility, delivering effective solutions for conveying complex information and enabling exceptional patient care.
