LLM in healthcare: use cases, risks, and what deployment actually requires
- 10Pearls Editorial Team
- 11 min read
Summary
Explore how LLMs are being used in healthcare, from clinical documentation and EHR summarization to research and patient communication, along with model selection. Use these insights to assess workflows, choose the right approach, and build in governance and clinical validation as you move from an LLM pilot to production.
Healthcare teams deal with a large amount of information every day, and the effort required to process it can add costs, slow workflows, and reduce care capacity. Clinicians and administrative teams process documentation and other information-heavy tasks that keep healthcare operations moving. As patient volumes and data continue to grow, healthcare organizations are under increasing pressure to improve workforce productivity, reduce turnaround times, and serve more patients.
Large language models (LLMs) offer a new way to work with this information. They can summarize lengthy clinical records, draft documentation, extract relevant details, answer questions using medical knowledge, and synthesize research. AWS identifies tasks such as summarization, question answering, and insight generation as areas where LLMs can add value. LLMs in healthcare are also transforming clinical documentation, research, patient communication, and information management, reshaping how healthcare organizations handle information.
This article examines where LLMs are being used in healthcare, how organizations can choose the right model and architecture, and what it takes to move from an initial pilot to a production-ready system.
What makes a healthcare LLM different from a general-purpose model
A general-purpose LLM can already write, summarize, and answer questions. But the healthcare industry requires far more than basic language capability. The model must be able to interpret clinical terminology, understand provided patient-specific context, integrate with existing systems, work with sensitive information, and produce outputs that meet a much higher standard for accuracy. As a highly regulated industry, healthcare also requires LLMs to meet strict governance and compliance standards.
General-purpose LLMs can struggle when a task depends on specialized knowledge such as medical terminology, clinical codes, or healthcare-specific information. Models trained or fine-tuned on biomedical and clinical data can be better suited to specialized tasks such as clinical information extraction, and question answering. Research on medical large language models in healthcare also shows that specialization can improve performance on healthcare-specific tasks, although it does not eliminate the need for validation and oversight.
For healthcare organizations, this creates several options. A general-purpose model may be sufficient for a lower-risk documentation or administrative workflow. While a domain-specific model may make more sense for specialized clinical tasks. The right choice also depends on the organization’s healthcare IT services, data environment, and integration requirements.
Use RAG when a model needs access to current organization-specific information, such as clinical guidelines, internal policies, or patient records. Fine-tuning is more useful for repeatable tasks where you want the model to follow a particular format or approach, such as clinical note formatting, coding support, or an organization’s communication style.
There are also situations where using both makes sense. Fine-tuning can help the model handle a task the way you want, while RAG gives it access to the latest information needed to complete that task. The decision ultimately comes down to the workflow, the value it can provide, the quality of the available data, implementation costs, governance needs, and the potential impact of errors.
Where LLMs are being used in healthcare today
Healthcare organizations are exploring LLMs across both clinical and administrative workflows. They can help with high-volume and repetitive information processing from multiple sources. Some of the areas where these capabilities are already being tested or deployed are:
Clinical documentation & ambient scribing
Clinical documentation is one of the most actively studied applications of LLMs in healthcare. A 2026 scoping review of 41 studies found that the technology was primarily being evaluated for clinical note generation, discharge summaries, and provider-patient encounter documentation.
EHR summarization & chart review
Patient records can span years and contain information across multiple encounters, notes, reports, and other documents. LLMs can help synthesize this material into concise summaries, extract relevant clinical information, and help clinicians navigate unstructured records. Report summarization and extracting insights from medical information are among the more advanced applications of healthcare LLMs.
Medical coding & prior authorization
LLMs can help extract information from clinical documentation to support medical coding, billing, prior authorization, referrals, and related administrative workflows. They can also be incorporated into healthcare payer solutions to help process documentation and route cases for review.
Patient communication & portal message triage
LLMs in healthcare can help teams handle the volume of messages coming through patient portals. They can group requests, identify routine questions, draft responses, and send more complex messages to the appropriate staff member. Clinical messages still require human review.
Clinical decision support
LLMs can bring together relevant details from a patient's record and medical literature to support clinical assessment. They may help answer medical questions, summarize findings, or surface information a clinician might want to review. Since these outputs can influence care, they need to be checked by a qualified professional.
Literature synthesis & research
Healthcare researchers face an enormous volume of scientific literature and clinical research data. LLMs can help summarize papers, extract findings, synthesize evidence, and process qualitative research material. Applications range from literature review to clinical-trial research, while broader reviews identify research and medical education as additional areas of LLM use.
A 2026 scoping review of 41 studies found that the technology was primarily being evaluated for clinical note generation, discharge summaries, and provider-patient encounter documentation.
How to choose between a general-purpose or domain-specific LLM
Healthcare organizations have several options when selecting an LLM, from general-purpose foundation models to medical LLMs, RAG-based systems, and fine-tuned models. The right approach depends on the task and the level of specialization required.
| Approach | Best suited for | Key consideration |
|---|---|---|
| General-purpose LLM | Documentation, summarization, administrative workflows | May need additional medical context |
| Medical/domain-specific LLM | Specialized clinical and biomedical tasks | Requires validation for the intended use |
| LLM & RAG | Applications that need current or organization-specific information | Retrieval quality directly affects output quality |
| Fine-tuned LLM | Highly specialized workflows with suitable training data | Higher development, validation, and maintenance requirements |
Choosing the right approach starts with the medical NLP task, available data, performance requirements, and organizational resources. A simpler solution may be preferable when it consistently meets the required performance, avoiding the additional cost and maintenance of more complex customization. For healthcare organizations, the ideal approach in choosing a healthcare LLM would be to start with the workflow and its requirements.
What HIPAA compliance actually requires
When an LLM application handles protected health information (PHI), healthcare organizations need to evaluate the entire flow of that information, from the point it enters the application to where it is processed, stored, and ultimately used. HHS identifies risk analysis as a foundational requirement for protecting electronic PHI, and organizations are expected to assess potential threats and vulnerabilities and implement appropriate safeguards.
Data handling & access
Organizations should define what PHI the application can access, where that information is processed and stored, who can access it, and how access is controlled and monitored.
Third-party & cloud providers
If a vendor or cloud service creates, receives, maintains, or transmits ePHI on behalf of a covered entity, it generally qualifies as a business associate. A business associate agreement (BAA) and appropriate safeguards are important parts of the relationship.
Security & governance
An LLM deployment should have defined policies for data use, access permissions, security monitoring, incident response, and ongoing risk assessment. Security measures should also be reviewed regularly as technologies, systems, and risks evolve.
Model & workflow controls
Health Insurance Portability and Accountability Act (HIPAA) compliance also depends on how the LLM is used, including output review, audit trails, and data access. Healthcare AI consulting can help organizations address these requirements as part of the overall solution and workflow.
How an LLM connects to clinical systems
An LLM needs access to the right clinical context to be useful in a healthcare workflow. Depending on the application, that may mean retrieving information from electronic health records (EHRs), clinical databases, medical knowledge bases, or internal documents before generating a response. RAG is one of the ways to connect an LLM to authoritative information outside its original training data, giving the model additional context for healthcare tasks.
Retrieval-augmented generation (RAG) connects an LLM to external sources such as clinical guidelines, EHR data, or organizational knowledge bases. When a user makes a request, the model retrieves relevant information, allowing it to work with current or organization-specific information without retraining the underlying model. RAG can also give organizations greater control over the sources used to generate responses and support source attribution.
This requires secure healthcare software integration across data sources, APIs, identity and access controls, and the systems where the output will ultimately be used. The integration layer also needs to support auditability, human review, and handling errors or incomplete information.
Risks & limitations of LLMs in healthcare
LLMs can be highly capable in healthcare, but their limitations become important when their outputs influence clinical or operational decisions. Research across multiple medical specialties points to recurring challenges in output quality and transparency.
Hallucinations & incorrect information
LLMs can generate medically incorrect information that sounds convincing, potentially affecting clinical decisions or patient communication.
Incomplete outputs
A model may leave out clinically relevant information, particularly when handling complex patient records.
Poor clinical reasoning
LLMs can struggle with multi-step medical reasoning and applying general knowledge to a specific patient or clinical situation.
Bias & uneven performance
Performance can vary across patient populations, specialties, and clinical scenarios, creating risks of inconsistent outcomes.
Lack of transparency
It can be difficult to understand how an LLM arrived at a particular response, which can complicate clinical validation and accountability.
Inconsistent outputs
The same model can produce different responses to similar inputs, making consistency and reproducibility harder to establish.
Outdated or unsupported information
An LLM may rely on outdated knowledge or provide information without reliable sources, which can be problematic when medical guidance changes.
Privacy & security
LLM applications handling patient information require strong controls around data access, storage, processing, and transmission.
Limited medical-domain performance
Healthcare is complex and clinical tasks involve specialized terminology, heterogeneous patient histories, contextual reasoning, changing evidence, and population-specific variation. A general-purpose LLM may lack the medical knowledge or context needed for specialized healthcare tasks.
Getting from pilot to production
A promising LLM pilot can still fail to deliver value once it enters a live healthcare environment. Production deployment brings new questions around workflow fit, data access, system integration, security, user adoption, and ongoing model performance. Before moving forward, healthcare organizations should assess whether the surrounding environment can support the technology.
Start with a real workflow
Choose a specific problem with a clear owner, measurable outcome, and manageable risk. A focused use case makes it easier to demonstrate value and identify gaps early.
Establish clinical & operational oversight
Decide who owns the system, who reviews its outputs, and when human intervention is required. Clinical stakeholders should help evaluate applications that affect patient care.
Prepare the data & integrations
Validate the information the LLM will use and determine how it will connect with EHRs, knowledge bases, and other systems. Data quality and reliable integration can impact the final application as much as model selection.
Deploy in a controlled environment
Start with a limited group or workflow, monitor real-world performance, and give users a clear way to flag and correct problematic outputs.
Monitor & maintain after deployment
Monitor accuracy, errors, user feedback, and system performance after launch. Update the application as models, data, workflows, or requirements change.
With more than two decades of digital engineering experience in highly regulated industries, including healthcare, 10Pearls understands what it takes to move healthcare technology from concept to production. We’ve built applications that integrate directly with EHR systems and combine healthcare AI consulting with hands-on healthcare software development.
Ready to move your healthcare LLM from pilot to production?
FAQs about LLM in healthcare
What is an LLM in healthcare?
LLMs in healthcare are large language models adapted or used for tasks involving medical and healthcare information. They can help with clinical documentation, EHR summarization, medical coding, patient communication, research, and clinical decision support. Their ability to work with large volumes of unstructured information makes them useful across clinical and administrative workflows, though higher-risk applications require appropriate review.
Are LLMs HIPAA compliant?
HIPAA compliance depends on how healthcare LLMs are deployed and how protected health information is handled. Data access, storage, security, vendor agreements, and the surrounding application all need to meet applicable requirements.
What is the difference between a medical LLM and a general-purpose LLM?
A medical LLM has been trained or adapted with healthcare-specific data, terminology, or tasks. General-purpose models are built for a much wider range of applications. The choice comes down to the task, available data, performance requirements, and level of medical specialization.
Can LLMs diagnose patients?
LLMs in healthcare can help clinicians review patient information, summarize records, and find relevant medical information. Since their responses can be incomplete or incorrect, it is best that a clinician reviews the patient and their diagnosis.
How accurate are LLMs on medical questions?
There is no single accuracy rate for LLMs in healthcare. Results differ by model, specialty, question, and clinical setting. Testing the model on realistic scenarios and representative data provides a better indication of how it will perform in practice.
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