Healthcare organizations generate more data than any human team can process at scale. This includes medical records, diagnostic images, prescriptions, patient histories, test results, and ongoing communication between staff and patients.
The development of Large Language Models (LLMs) has unleashed the potential to make positive changes in healthcare data management. So, understanding how these models work is critical for healthcare leaders.
This article explains what LLMs are in the healthcare context, the risks that healthcare providers must address, best practices for integration, and what comes next as the technology evolves.
What is an LLM in Healthcare?
A Large Language Model is a machine learning system trained to predict and generate text that resembles human writing.
Healthcare LLMs go beyond general-purpose models. They often receive additional medical tuning on verified clinical sources. This helps them understand standard terminology, symptoms, diagnostics, and treatment guidelines used across electronic health records.
Their capabilities include:
The model works as a digital assistant that understands medical language and performs fast analysis at scale. Its output still requires human supervision, but it provides significant benefits. This includes reducing manual work, shortening the path to insights, and increasing the quality of service in patient-facing and internal systems.
According to Grand View Research, the global AI in healthcare market is growing. It’s projected to reach $505.59 billion by 2033, with a compound annual growth rate of 38.81% from 2025 to 2033. This shows great potential for LLMs in healthcare and proves that digital healthcare leaders shouldn’t ignore this trend.
LLMs in Healthcare Use Cases
Healthcare organizations are already experimenting with LLMs in their operations. This list includes hospitals, telemedicine services, insurance systems, and medical research platforms. Below are the most active and high-impact use cases with real-world examples.
Clinical Documentation Assistance
According to studies, physicians spent more than 35% of their time on documentation. This greatly impacts the quality of patient care. At the same time, we can’t underestimate the importance of such files for maintaining accurate medical histories. That’s where LLMs come into play.
LLMs can help generate structured medical records from patient interviews, transcripts, voice notes, telemedicine calls, and uploaded documentation. When implemented right, the model can format the content into standard medical templates such as SOAP notes, discharge summaries, referral letters, post-visit instructions, or clinical progress reports.
Many organizations report improved consistency across documentation and hours saved per clinician. For example, HCA Healthcare found that thousands of nurses spend up to 10 million hours a year working with documents. So in 2024, they partnered with Google Cloudto create an app based on Google’s gen AI foundation models that digitally collects and organizes patient records. Although the app is still in the testing phase, the developers expect optimistic results, as 90% of nurses who tested it called it helpful.
Clinical Decision Support
LLMs can assist physicians in clinical analysis. Of course, we don’t talk about granting AI the power to make decisions, but rather about using its data processing capabilities to quickly identify patterns. This way, clinicians can review information faster and evaluate more data than they could manually. The model can:
This improves the clinician’s ability to assess complex cases, especially in multi-factor chronic diseases where histories are long, and symptoms interact across conditions. However, we need to recognize that using LLM for clinical decision-making is a challenging task, and even big players in the market can fail.
This is exactly what happened with IBM Watson for Oncology. IBM promoted it as a revolutionary tool for cancer treatment, but it lacked transparency, adaptability and provided incorrect recommendations. In addition, the high cost without significant clinical benefit didn’t justify the investment.
Patient Communication and Engagement
As patients, we often feel overwhelmed by complex language in reports. LLMs can change it, making communication with doctors clearer and more accessible. For example, LLM-powered solutions can provide patients with:
For example, systems like Babylon Health or Ada Health take a diagnosis from a clinician and generate plain-language explanations for patients. This improves patient understanding and reduces patient support workload across digital channels.
Frontline Digital Support
Many telemedicine and hospital systems use AI models for patient support. This includes everyday operations like scheduling appointments, routing patient questions, determining urgency, redirecting cases to the correct specialist or department, invoicing, and more. Automating such routine tasks can reduce time spent by up to 40%.
This is, probably, the most feasible and efficient way of using LLM in healthcare with instant business value. That’s why medical organizations actively apply this in their operations. Mayo Clinic uses AI chatbots to handle patient queries and provide pre-visit instructions. Cleveland Clinic uses AI-powered virtual assistants to answer FAQ, provide billing information, and schedule follow-ups. And the list can go on and on.
Medical Research Assistance
Medical research teams use LLMs to analyze large bodies of literature. The models can summarize studies, extract findings, highlight methodological details, and track trends. This shortens the early stage of research and improves evidence review.
Plus, inside hospitals, LLMs can help with analyzing population health insights such as:
Hospitals can improve long-term care planning and public health strategy using consistent machine-generated analysis at scale.
Integration with Insurance Platforms
Studies show that 23% of people with insurance are still underinsured. This put them at financial risk despite having coverage. Integrating LLMs with insurance platforms can make plan selection more efficient. Here are some examples of what LLMs can do:
Basically, LLMs act as intelligent digital assistants that can answer user questions, simplify plan comparisons, and guide people toward the insurance option that best fits their needs.
The Key Risks and Challenges of LLMs in Healthcare
As you can see from the successful and failed case studies above, integrating LLM into healthcare is not an easy task. Even if your case has potential, everything can fall apart due to poor implementation or lack of trust. That is why it’s important to be aware of both the benefits and the risks of working with LLM in healthcare.
Data Security and Privacy
Patient healthcare data is highly sensitive information that falls under regulations such as GDPR and HIPAA. And LLMs introduce additional data exposure points. Plus, we must remember the ethical considerations of using patient data to train models. This puts security and patient consent at the pedestal of LLM projects.
Incorrect or Fabricated Medical Output
LLMs predict text based on probability. While models trained on medical datasets perform well in many situations, they can still produce incorrect conclusions or suggest medically unsafe actions. This risk is one of the primary reasons for human review requirements. Without medical oversight, the consequences of incorrect output can be serious.
Hallucination and Over-Confidence
A model may produce fluent medical reasoning even when its conclusions are wrong. This can mislead less experienced users or patients. Systems must be designed to make the AI role clear: it’s an assistant, not a decision-maker.
Bias in Medical Data
If a model is trained on biased data, it will provide biased outcomes. If not addressed, the model may produce uneven medical recommendations. Healthcare AI systems must consistently evaluate model output across diverse patient populations and pay a lot of attention to the quality of training data.
Integration Complexity
LLMs function best when connected to healthcare systems such as Electronic health records and Hospital information systems. But many hospitals still operate on legacy architecture. Introducing LLMs often requires significant modernization to achieve reliable performance.
Unclear Responsibility in Case of Error
If an AI system suggests a harmful clinical step, responsibility remains a point of legal discussion in many countries. This requires clear policies that define accountability between clinicians, healthcare institutions, and AI vendors.
Best Practices for Integrating LLMs in Healthcare
To mitigate these risks and increase your chances of succeeding, you must carefully research and plan your project. Here are the key aspects you should pay attention to.
Start with High-quality Data
The model’s performance depends heavily on the quality and representativeness of the training dataset. You must curate unstructured clinical notes, lab results, imaging reports, and structured EHR fields to remove errors, duplicates, and inconsistencies. Data labeling and normalization are crucial, as even minor discrepancies can lead to misleading outputs.
Additionally, it’s important to determine how important the diversity of data sets is to your solution. Many early failures in medical AI, such as IBM Watson for Oncology, demonstrate the risks of relying on a limited dataset. If a model is trained on one institution’s records, it may inherit local treatment patterns, demographic biases, or documentation styles that don’t generalize to other hospitals or patient populations.
Techniques like stratified sampling, data augmentation, and synthetic data generation can help fill gaps, but each method must be carefully validated to avoid introducing new biases.
Think About Integration
LLMs perform best when they can access EHRs, lab information systems, and imaging repositories via secure APIs. That’s why you should prioritize interoperability with existing hospital infrastructure.
Deployment strategies should consider latency, throughput, and redundancy to maintain performance under peak clinical loads. Many healthcare organizations adopt a hybrid approach, combining on-premise inference for sensitive data with cloud-based services for compute-intensive tasks. This helps balance security, scalability, and cost.
Implement Monitoring and Continuous Evaluation
Monitoring and regular evaluation remain essential when using LLMs in healthcare. Real-time logging of model answers helps teams check accuracy, spot unusual behavior, and review how the system makes decisions. Clinicians should be able to provide feedback that goes directly into retraining so repeated mistakes and hallucinations get fixed.
Teams can measure performance using practical indicators like precision, recall, and how well the output matches current clinical guidelines. Automated tests against a selected set of verified patient records also help confirm that the system works correctly before releasing new updates.
Introduce Explainability
Healthcare is a very sensitive field where you want to understand what stands behind every decision made by AI. LLM outputs should include structured references to source data and highlight reasoning pathways to assist clinicians in understanding why a particular explanation or summary was generated. Versioning of models, along with robust documentation of training datasets, fine-tuning parameters, and deployed APIs, ensures reproducibility and supports regulatory compliance.
The Future of LLMs in Healthcare
As LLMs continue to improve, their use in healthcare will grow rapidly. In the coming years, we can expect the rise of multimodal large language models. They understand not just text, but also images, time series data, and audio. This will make it easier to combine and interpret different types of medical data.
Alongside these technical advances, strong regulatory and frameworks will be essential for safe adoption. Analysts in The Lancet Digital Health warn that without enforceable standards, LLMs could produce hallucinations, leak sensitive data, or be misused. Current developments in this field are focused on defining best practices for auditing AI outputs and establishing liability in case of errors. These efforts aim to make LLMs more reliable, transparent, and safe for integration into everyday healthcare workflows.
Research also shows the need for consistent evaluation: by 2024, over 550 LLM‑healthcare studies had been published, yet standardized clinical testing is still limited. If future systems combine robust multimodal understanding with thorough validation and regulation, LLMs could move from being assistive tools to becoming central components of intelligent, patient-focused healthcare.
