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What Do Clinicians Really Need From AI—and Why Does It Matter?

Hansa Bhargava, MD Chief Clinical Strategy and Innovation Officer, Healio

Published July 2, 2026

Clinicians and AI

When clinicians see patients, they need relevant, accurate, and comprehensive information to help guide decision-making. Having access to the right information at the right time can support faster diagnoses, more informed treatment decisions, and ultimately better patient care.

With frontier models such as ChatGPT, Gemini, and Claude, access to health information has become widely available. But should healthcare professionals rely on general-purpose AI models, or are specialized clinical models trained on medical data and resources the better choice?

Recent research published in Nature comparing medical knowledge, expert clinical alignment, and responses to clinical queries found that some general large language models performed surprisingly well. At the same time, several healthcare-focused models demonstrated challenges such as factual inaccuracies, omissions, incomplete responses, and disorganized outputs. These findings highlight an important reality: specialized training alone does not guarantee clinical usefulness.

Does Recent Medical News Matter?

Timely information can be critical in clinical practice. Consider a patient presenting with fever and rash. A clinician may need to know which infectious diseases are currently circulating in the local area or whether new guidance has emerged regarding diagnosis and treatment.

This raises an important question for healthcare AI tools: How current is the information being used to generate answers? Beyond established medical literature, can a model incorporate emerging evidence, recent medical news, and insights presented at scientific meetings and conferences? Access to up-to-date information may be essential for supporting clinical decision-making.

Should Clinicians Help Build AI Tools?

Healthcare AI tools are most effective when they are developed with input from the people who use them every day.

Physicians, nurse practitioners, physician assistants, and other healthcare professionals bring real-world clinical experience that can help shape product design, testing, and ongoing refinement. Their perspectives can identify practical challenges, uncover gaps in performance, and ensure that tools align with clinical workflows.

As a physician, I have long believed that frontline clinical experience is invaluable in helping digital tools evolve into more useful and trustworthy resources. When evaluating a healthcare AI solution, one important question is whether clinicians have been involved throughout product development, iteration, and implementation.

Why Understanding AI Use Matters

Equally important is understanding how clinicians are actually using AI in practice.

The ways healthcare professionals interact with AI—and the impact those interactions have on workflow, decision-making, and patient outcomes—provide valuable insights for improving future tools. Organizations across healthcare continue to study adoption patterns, user preferences, and real-world applications of AI to better understand what works and where improvements are needed.

These insights can help developers create more practical, clinically relevant solutions that support healthcare professionals in delivering high-quality care.

The Bottom Line

Building effective AI tools for healthcare requires more than strong model performance. Accuracy, completeness, timeliness, clinical relevance, and clinician involvement all play important roles. When these elements come together, AI has the potential to become a valuable partner in helping clinicians deliver the best possible care to patients.

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