LLMs in Healthcare

• Large language models (LLMs) are advanced artificial intelligence (AI) systems designed to understand, process, and generate human language

• LLMs in healthcare can support clinical decision-making, streamline documentation, enhance patient communication, and assist research

• LLMs carry inherent risks, including factual errors, bias, and data privacy concerns, so responsible deployment with human oversight and strong governance is essential

Large language models (LLMs) can analyze large volumes of unstructured data and generate fluent, context-aware text, positioning them as a significant and rapidly emerging artificial intelligence (AI) application in healthcare. For both patients and clinicians, the growing adoption of LLMs in healthcare has fueled high expectations for rapid transformation of the healthcare ecosystem.¹

An LLM is a type of AI trained to understand, analyze, and generate human language. In healthcare, these capabilities can support clinical decision-making, improve patient communication, and assist medical research.^1-3

Despite the many promising applications of large language models in healthcare, their potential must be balanced with an understanding of risks and limitations. As adoption grows, leaders need clarity on where LLMs can add real value and where caution is warranted. The central question for healthcare leaders is not whether LLMs can improve healthcare, but how to deploy them responsibly while managing risks effectively.

Large language models are advanced AI systems designed to understand, process, and generate human language. Well-known examples include ChatGPT, Gemini, Claude, and Microsoft Copilot.^2,4 

Built on deep learning architectures and trained on vast datasets—from books and research papers to online forums—LLMs use billions to trillions of parameters. This scale enables them to recognize language patterns, analyze complex ideas, and generate human-like text in real time.⁴ For this reason, LLMs are particularly well-suited to healthcare, where much of the data exists as unstructured text across medical records, patient communications, biomedical research, and clinical guidelines.⁵ 

Healthcare has long leveraged AI in imaging, diagnostics, and predictive analytics. Traditional AI systems excel at clearly defined tasks using structured inputs and data. They produce outputs such as scan analyses, lab result predictions, or risk alerts—all explicitly defined by human engineers. These systems are precise, task-specific, and typically trained for a single purpose.⁴

LLMs are designed to understand and generate human language, working primarily with unstructured text. Rather than being programmed for one narrowly defined task, they can analyze text, identify inconsistencies, answer complex questions, synthesize information, and support open-ended tasks by generating context-aware responses.⁴

The unique strengths of LLMs lie in their language fluency, versatility, and adaptability. Unlike traditional AI systems, LLMs can navigate complexity and adapt continuously to a wide range of inputs.⁴ For healthcare leaders, this means LLMs can support more open-ended applications, from improving patient communication to enhancing research insights.

Large language models can complement existing AI tools and power a range of practical applications in healthcare, from summarizing clinical notes to supporting patient communication and improving operational workflows. 

Clinical decision support

LLMs have shown promise in helping clinicians across various medical specialties make better-informed decisions, such as identifying potential diagnoses, suggesting medical tests, and recommending treatments.^2,3 With quality clinical data and effective prompting, LLMs can achieve high diagnostic accuracy across diverse cases.^5,6 However, human oversight remains essential. When used for clinical decision support, LLMs are best leveraged in an assistive role, with clinicians evaluating outputs for accuracy and relevance.⁵

Supporting clinical documentation and workflows

Documentation and administrative tasks can consume up to 25% of clinicians’ time each day, contributing to fatigue and burnout.^5,7,8 LLMs can help reduce this burden by summarizing reports and clinical notes, converting unstructured text and voice input into standardized formats, and drafting documentation, including patient referral letters and discharge summaries.^5,7,8 By alleviating some of the administrative workload, LLMs can free up clinicians to focus more on direct patient care.

Enhancing patient communication and education

Effective communication is central to the patient-physician relationship, and LLMs’ language capabilities make them particularly well-suited to enhancing patient communication and education. 

LLMs can help by:

• Translating complex medical information, such as pathology reports, into patient-friendly language^9,10

• Answering patient questions out of hours via virtual chatbots^2,3,7

• Bridging the communication gap between patients and healthcare teams by translating documentation into patients’ native languages, improving understanding across diverse populations^2,8

• Generating new patient education materials and improving existing ones for clarity and readability^2,9

Medical research and knowledge support

LLMs also support clinical research by quickly retrieving and organizing relevant information from vast biomedical literature. This capability allows researchers to locate and compare findings more efficiently than manual searches.^1-3

By surfacing insights across diverse data sources, LLMs can support hypothesis generation, deepen understanding of pathology and disease, and contribute to the development of new treatments and drug discovery.^3,5

Across these use cases, the potential benefits of LLMs in healthcare are significant. Key advantages include:^2,5

• Streamlining clinical workflows

• Reducing time spent on documentation

• Improving information access for patients, healthcare professionals, and researchers

• Freeing clinicians to spend more time on direct patient care 

LLMs can also improve patient communication, helping build trust, increase satisfaction, and support better adherence to treatments.¹⁰ For healthcare leaders, these benefits translate into measurable value: More efficient operations, higher clinician engagement, stronger patient relationships, and ultimately, improved health outcomes. 

In an environment where tolerance for error is low and patient safety is paramount, healthcare leaders must proactively ensure responsible LLM deployment.^1,2

It’s important to recognize that LLMs do not truly understand the content they generate—their responses are predictive and probabilistic.^2,4 This means factual errors, or “hallucinations,” can appear and may be presented confidently as facts.^1-4 Ultimately, large language models are only as reliable as their training data, meaning outdated information, data bias, and misinformation can be encoded into outputs.^2,4,7,11 For these reasons, LLMs cannot replace clinical judgement.  

Data privacy and security are additional concerns. LLMs often rely on large volumes of sensitive, unstructured text, including clinical notes and patient communications. Without adequate safeguards, sensitive data could be memorized or leaked.⁷ Responsible use of LLMs in healthcare therefore requires strong governance frameworks, compliance with existing data protection regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR), robust cybersecurity measures, effective data anonymization, and transparent patient consent processes.^1-3,7,11

Integrating LLMs into healthcare systems can seem complex, but a structured, practical approach can help healthcare leaders move from planning to responsible implementation.

Best practices for implementing large language models in healthcare include:

• Define clear use cases with boundaries: Start with specific, high-value tasks (such as clinical note summarization) rather than broad pilots. This reduces risk and enables straightforward evaluation.¹²

• Maintain human-in-the-loop oversight: Take a human-centered approach by designing human-in-the-loop workflows that ensure clinician oversight while supporting effective collaboration between humans and LLMs.^2,5,6

• Integrate with existing clinical workflows and technology: Plan carefully to ensure interoperability with existing electronic health record systems (EHR) and other clinical technologies, minimizing disruption to care delivery.⁵

• Educate and train end users: Invest in user training and digital literacy to gain buy-in, address clinician concerns, and promote appropriate and effective use of LLMs.^2,5,12

• Track performance and safety: Conduct risk assessment before implementation and establish continuous monitoring to evaluate accuracy, effectiveness, and potential safety issues.^1,12

• Prioritize data privacy and security: Adopt strict governance around data privacy and compliance, cybersecurity, and anonymization of sensitive data to reduce risk and protect sensitive patient information.^1-3,7,11,12

Large language models are transforming healthcare by improving access to information, streamlining clinical workflows, and enhancing patient communication. To realize the benefits of LLMs, healthcare leaders must understand how to leverage their strengths while mitigating risks to deploy them responsibly.  

As with any emerging technology, responsible deployment requires strong governance, rigorous evaluation, a sustained focus on patient safety, and, most importantly, human oversight. By positioning LLMs as assistive tools rather than replacements for clinical expertise, healthcare organizations can deliver meaningful value while safeguarding trust and care quality.