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Compare 10+ LLMs in Healthcare

Cem Dilmegani
Cem Dilmegani
updated on Aug 15, 2025

Large language models (LLMs) are increasingly being applied in healthcare to support clinical tasks such as medical question answering, patient communication, and summarizing medical records. After analyzing on platforms like the Open Medical LLM Leaderboard and in peer-reviewed papers, I listed leading models in healthcare:1

Healthcare LLMs benchmark

Benchmark methodology: This benchmark evaluates the supervised fine-tuning performance of healthcare LLMs vs large general purpose models (GPT-4) on medical question answering tasks. See benchmark data sources.

MedQA:

Multiple-choice medical exam questions based on United States Medical Licensing Examination.

MedMCQA:

Large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions.

PubMedQA: Biomedical question-answering benchmark using yes/no/maybe answers.

Healthcare LLM examples

BERT-like (Encoder-only)

Optimized for encoding and representing biomedical text, these models excel at extracting features for tasks such as classification.

Model
Developer
Year
Parameters (B)
Open Source
BioLinkBERT
2022
0.34
MedBERT
Stanford University
2021
0.017
Health Acoustic Representations (HeAR)
Google
2024
0.31

ChatGPT / LLaMA-like (Decoder, instruction/chat-tuned)

Based on LLaMA-style architectures and optimized for interactive tasks and clinical dialogues.

Model
Developer
Year
Parameters (B)
Open Source
Polaris 3.0
Hippocratic AI
2025
4200
MEDITRON-70B
EPFL (Swiss AI Lab)
2023
70
Me-LLaMA
PhysioNet (multi-institution)
2024
70
OpenBioLLM
2024
70
Radiology-Llama2
Meta
2023
70
PMC-LLaMA
Shanghai AI Lab & SJTU
2024
13
ChatDoctor
UT Southwestern & collaborators
2023
13
Asclepius
KAIST & Yonsei Univ.
2023
13
MedAlpaca
Technical University of Munich
2023
13
Clinical Camel
University of Toronto (Vector Institute)
2023
13

GPT / PaLM-like (Decoder-only, generative)

Built similarly to GPT-3 or PaLM, these models are fine-tuned for general-purpose text generation and summarization.

Model
Developer
Year
Parameters (B)
Open Source
Med-PaLM 2
Google
2023
340
BioMedLM
Stanford CRFM (MosaicML)
2022
2.7
PubMedGPT
Stanford CRFM
2023
2.7
BioGPT
Microsoft Research
2022
0.35

General-purpose LLMs in healthcare

Key takeaways:

  • o1 – Best performing model
  • 03 mini – Best budget option
  • GPT 4.1 – Best speed and response time

Benchmark methodology: This benchmark evaluates 9 popular general LLMs on graduate-level medical questions using the MedQA dataset, which draws its content from the United States Medical Licensing Examination (USMLE). Each question includes a clinical scenario and multiple-choice answer options.

LLM outputs: Each model was prompted to return a structured answer (e.g., “Answer: C”).2

Latency: The average time a model takes to generate a response to a single MedQA prompt. For example, if 100 questions take 1,115 seconds total to complete, the average latency is 11.15 seconds per question.

Fine-tuning medical LLMs

“Here, the performance of the default ChatGPT (4o model) is compared with the existing ‘Clinical Medicine Handbook’ assistant. Both models are given the same prompt, and their responses are analyzed:

GPT 4o

Fine-tuned medical LLM

Guides on fine-tuning LLMs

  • Fine-tuning large language models for improved health communication5
  • Fine-tuning large language models for specialized use cases6

For more: LLM fine-tuning and LLM training.

Use cases of general purpose LLMs

Model
Healthcare use case example
Method used
Open source
GPT‑4
Summarizing patient histories from healthcare notes for clinical decision support
RAG (Retrieval-Augmented Generation)
Claude 3
Head and neck cancer diagnosis and treatment planning in oncology board simulations
RAG + Prompt Engineering
Qwen 3
Medical task reasoning tasks
Continual pretraining + Fine-tuning
Command R+
Retrieval-augmented pipelines for clinical Q&A and literature review
RAG
LLaMA 3
Hospital discharge summary generation and question answering data
Continual pretraining + Fine-tuning

These models are general fine-tuned models that need domain adaptation to perform clinical tasks accurately. You can use these models in healthcare by leveraging:

  • Continual pretraining on medical data to help the model better identify medical language by exposing it to clinical notes and biomedical literature (like PubMed).
  • RAG to pull data from verified clinical documents to produce accurate responses at runtime.
  • Instruction fine-tuning to enable the model learn how to actually answer clinical questions or extract symptoms from text.
A general workflow of LLM fine-tuning for specialized use cases

Source: MCP Digitalhealth12

Use cases of LLMs in clinical settings

1- Medical transcription

LLMs can help create medical transcriptions by:

  • Listening to the organic dialogue between a patient and clinician
  • Extracting important medical details
  • Condensing medical data into compliant medical records that align with the relevant sections of an EHR

Real-life use case – Google’s MedLM can capture &transform the patient-clinician conversation into a medical transcription.13

2- Electronic health records (EHR) enhancement

The proliferation of electronic health records (EHR) has accumulated a vast repository of patient data, which, if mined effectively, can become a goldmine for healthcare improvement.

Real-life use case – Google’s MedLM is also used by BenchSci, Accenture, and Deloitte for electronic health records enhancement (EHR).

  • BenchSci has integrated MedLM into its ASCEND platform to improve the quality of preclinical research.
  • Accenture uses MedLM to organize unstructured data from numerous sources, automating human operations that were previously time-consuming and error-prone.
  • Deloitte works with MedLM to minimize friction in finding treatment. They use an interactive chatbot that helps health plan participants better understand the provider alternatives.14

3- Clinical decision support

Large language models can summarize complex medical concepts allowing them to support valuable insights in the decision-making process.

Real-life use case – Memorial Sloan Kettering Cancer Center uses IBM Watson Oncology to assist oncologists by analyzing patient data and medical literature to recommend evidence-based treatment options.15

4- Medical research assistance

LLMs can parse and summarize vast amounts of data, can extract key findings from new research, providing synthesized insights. For example, one of the most famous LLMs, ChatGPT, is used for text summarization.

Real-life use case – John Snow’s healthcare chatbot helps researchers find relevant scientific papers, extract key insights, and identify research trends. It is particularly valuable for navigating the vast amount of biomedical literature.16

Real-life use case – TidalHealth Peninsula Regional clinicians used the Micromedex with Watson solution for healthcare research, claiming that, clinicians received their answers in less than one minute ~70% of the time.17

5- Automated patient communication

Large language models in healthcare can draft informative and compassionate responses to patients’ queries.

Some examples include:

  • Medication management and reminders: A chatbot provides patients regular reminders to take their diabetic medication and requests confirmation.
  • Health monitoring and follow-up care: A post-operative patient sends their pain and wound status to a chatbot, which determines if the healing process is progressing.
  • Informational and educational communication: A patient asks a chatbot how to manage high blood pressure, and the chatbot responds with nutrition and lifestyle tips. 

Real-life use case – Boston Children’s Hospital uses Buoy Health, an AI-driven online symptom checker chatbot, that provides patients with instant answers to health-related questions and initial consultations.

The chatbot can triage patients by analyzing their symptoms and advising whether they need to see a doctor.18

6- Predictive health outcomes

LLMs can assist in predictive analysis by discerning patterns within data.

Real-life use case – WVU Pharmacists using AI to reduce patient readmission rates: WVU pharmacists use a predictive algorithm to leverage LLMs to determine readmission risk. This approach will examine data from electronic health records (EHRs), which include patient demographics, clinical history, and socioeconomic determinants of health. 

Based on this research, the WVU pharmacists identify patients at high risk of readmission and assign care coordinators to follow up with them after discharge. This can help reduce readmission rates.19

7- Personalized treatment plans

LLMs can suggest treatment plans tailored to an individual’s medical history and specific needs. Their ability to distill complex patient narratives into actionable insights can ensure that each patient receives a care plan that’s as unique as their health journey.

Real-life use case – Babylon Health: Babylon Health’s AI chatbot provides individualized health recommendations based on the user’s symptoms and medical history. It engages users in a conversation by asking relevant questions to analyze their issues better and giving tailored recommendations.20

8- Medical coding and billing

Large language models can automate audit processes by analyzing patient records and EHRs. 

For example, Epic Systems, a major EHR provider, integrates LLMs into its software to assist with coding and billing. The LLMs can monitor for anomalies in access patterns to sensitive patient information or inconsistencies in coding and billing practices.21

However, LLMs are not ready for medical coding but promising: Researchers examined how frequently four LLMs (GPT-3.5, GPT-4, Gemini Pro, and Llama2-70b Chat) issued the correct CPT, ICD-9-CM, and ICD-10-CM codes. 

Their findings show that there is a significant opportunity for improvement. Researchers discovered that LLMs frequently create codes that transmit inaccurate information, with a maximum accuracy of 50%.22

9- Training and education

Large language models and generative AI in general can be leveraged as interactive educational tools, elucidating complex concepts or offering clarifications on perplexing topics.

Real-life use case – Oxford Medical Simulation uses LLMs integrated with VR technology to create immersive virtual patient simulations. 

These simulations allow students to experience high-pressure scenarios, such as handling a cardiac arrest patient without any real-world consequences. 

The LLMs power the virtual patients’ responses, making them more realistic and unpredictable, preparing students for the variability of real clinical environments.23

Challenges of LLMs in healthcare

Privacy concerns

Using an LLM-based health application that has not been properly developed, tested, or approved for medical use could present significant risks to users. One of the primary concerns is related to privacy. LLMs and associated tools process health-related data input by users as part of their services. However, how this data is handled and whether these applications comply with data protection laws and principles remains uncertain.24

Accuracy and reliability

LLMs are also prone to hallucinations, plausible-sounding but incorrect or misleading information.

For example, when given a medical query, GPT-3.5 incorrectly recommended tetracycline for a pregnant patient, despite correctly explaining its potential harm to the fetus.25

Generalization vs. specialization

Healthcare encompasses a wide range of specialties, each with its nuances. An LLM trained in general medical data might not have the detailed expertise needed for specific medical specialties.

Biases and ethical considerations

Beyond accuracy, there are ethical concerns, like the potential for LLMs to perpetuate biases in the training data. This could result in unequal care recommendations for different demographic groups.

For more details on the challenges of large language models in healthcare, you can check our articles on the risks of generative AI and generative AI ethics.

The future of LLMs in healthcare

A Stanford’s analysis indicates that there is significant untapped potential for LLMs in healthcare.26

While many LLMs have been used for tasks like augmenting diagnostics or patient communication, fewer have focused on addressing the administrative tasks that contribute to clinician burnout.

In the future, LLMs may evolve to interact with behavior, more context, and emotions, enabling them to provide more personalized and empathetic support.

Benchmark data sources

  • Me-LLaMA 70B results27
  • Meditron 70B results28
  • Med-PaLM 2 results29
  • ChatGPT & GPT-430

Reference Links

1
Open Medical-LLM Leaderboard - a Hugging Face Space by openlifescienceai
Open Life Science AI
2
https://www.vals.ai/benchmarks/medqa-04-15-2025
3
Generative Medical AI: A Journey with Fine-Tuned Language Models | by Eluney Hernandez | Medium
Medium
4
Generative Medical AI: A Journey with Fine-Tuned Language Models | by Eluney Hernandez | Medium
Medium
5
https://www.sciencedirect.com/science/article/pii/S0169260725000720?ref=pdf_download&fr=RR-2&rr=96f93fbc6ad1f957
6
Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health
7
https://medium.com/llmed-ai/summarizing-patient-histories-with-gpt-4-9df42ba6453c
8
https://arxiv.org/abs/2403.12140
9
https://www.datacamp.com/tutorial/fine-tuning-qwen3
10
https://cohere.com/blog/command-r-plus
11
https://arxiv.org/abs/2404.04110
12
Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health
13
”Google Launches A Healthcare-Focused LLM”. Forbes. 2024. Retrieved on September 1, 2024.
14
”Google is rolling out new AI models for health care. Here’s how doctors are using them”. CNBC. 2023. Retrieved on September 1, 2024.
15
”Artificial Intelligence in Urologic oncology: the actual clinical practice results of IBM Watson for Oncology in South Korea”. ReseachGate. 2023. Retrieved on September 1, 2024.
16
”Medical ChatBot”. John Snow LABS. 2024. Retrieved on September 1, 2024.
17
”IBM Micromedex: Improving efficiency and care with AI”. ReseachGate. 2023. Retrieved on September 1, 2024.
18
”Buoy Health”. Children’s Hospital. 2024. Retrieved on September 1, 2024.
19
”WVU pharmacists using AI to help lower patient readmission rates”. Children’s Hospital. 2024. Retrieved on September 1, 2024.
20
”Babylon’s AI-enabled symptom checker added to recently acquired Higi’s app”. mobilehealthnews. 2022. Retrieved on September 1, 2024.
21
”Artificial Intelligence”. EPIC. 2024. Retrieved on September 1, 2024.
22
”Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying”. NEJM AI. 2024. Retrieved on September 1, 2024.
23
”Oxford Medical Simulation”. OMS. 2024. Retrieved on September 1, 2024.
24
The Challenges for Regulating Medical Use of ChatGPT and Other Large Language Models - PubMed
25
https://arxiv.org/pdf/2307.15343
26
Large Language Models in Healthcare: Are We There Yet? | Stanford HAI
27
Medical foundation large language models for comprehensive text analysis and beyond | npj Digital Medicine
Nature Publishing Group UK
28
[2311.16079] MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
29
[2305.09617] Towards Expert-Level Medical Question Answering with Large Language Models
30
[2305.09617] Towards Expert-Level Medical Question Answering with Large Language Models
Principal Analyst
Cem Dilmegani
Cem Dilmegani
Principal Analyst
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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Mert Palazoğlu
Mert Palazoğlu
Industry Analyst
Mert Palazoglu is an industry analyst at AIMultiple focused on customer service and network security with a few years of experience. He holds a bachelor's degree in management.
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