Healthcare LLM Landscape

Healthcare AI Market

Healthcare AI market reached $26.57 billion in 2024 and will reach $187.69 billion by 2030. 79% of healthcare organizations already use AI technology in some form!

Categories of LLMs

Large Language Models (LLMs) are transforming clinical and administrative workflows. The LLM ecosystem is quite diverse, offering various architectural approaches, each with its own set of trade-offs on cost, data control, and use cases. Wrote this article to share my thoughts on how I look at them. They fall into the three broad categories.

1. Proprietary models via commercial cloud APIs (Fastest Time to Value)

Example vendors: Microsoft (via Azure Foundry): GPT-4, GPT-4o, Anthropic, Google: Gemini, MedLM etc.

Characteristics

• General purpose models trained on broad datasets, medical specific AI models such as Google MedLM and MS MedImageInsight

• Accessed via APIs

• Strong general reasoning capabilities

• No direct HIPAA compliance - require cloud provider deployment

Trade-offs

• Data control: While some vendors offer limited controls, major vendors offer BAA covering both state of the art (SOTA) LLM models and medical domain specific models

• Customization: Low, prompt engineering with RAG and limited fine tuning

• Technical expertise: Low

2. Open-Source/Medically-Tuned Models

Examples: Meditron-70B, BioMistral-7B, ClinicalBERT, OpenBioLLM

Characteristics

• Built on Llama, Mistral, or BERT architectures

• Fine-tuned on PubMed, clinical guidelines, medical texts

• Deployable on-premises or private cloud

• Apache or similar open licenses

Performance

• Meditron-70B: 77.6% accuracy on MedQA

• BioMistral-7B: 57.3% average accuracy across 10 medical benchmarks

• OpenBioLLM: 86.06% average on biomedical datasets

Trade-offs

• Data control: Full, complete organizational control

• Customization: High, can fine-tune for specific needs

• Cost: No licensing fees, but infrastructure required

• Technical expertise: High, needs ML engineering team

Limitations

• Lack clinical safety validation

• Most warn against production use without extensive testing

3. Customer-Specific/Platform Models

Examples: Aidoc CARE/aiOS, Epic Cosmos, Microsoft Healthcare AI, AWS HealthScribe

Characteristics

• Complete platforms with models, orchestration, and governance

• Pre-built healthcare workflows

• FDA clearances (where applicable)

• Enterprise support

Platform examples

• Aidoc (Purpose build LLM models and a workflow platform)

• Microsoft DAX Copilot (Ambient AI transcription)

• Siemens AI-Rad (AI powered workflows)

Trade-offs

• Data control: Moderate, HIPAA-compliant hosting

• Customization: Moderate, pre-built modules

• Cost: Subscription-based, enterprise pricing

• Technical expertise: Moderate

  
  

• Some of the popular medical models

LLM Models I have looked into

Regulatory Status

• Note, as of Sep 2025 no LLM has FDA clearance as a medical device yet

• 950+ AI-enabled medical devices FDA-approved (221 in 2024)

• HIPAA compliance requires cloud provider deployment for proprietary models

• Clinical validation remains primary barrier

Selection Criteria

1. Use case complexity: Simple tasks use APIs; complex workflows need platforms

2. Data sensitivity: PHI requires HIPAA compliance

3. Integration needs: Consider EHR/PACS compatibility

4. Customization requirements: Open-source for maximum flexibility

5. Total cost: Balance API costs vs infrastructure investment

6. Clinical validation: Prioritize validated models for patient-facing applications

Implementation Recommendations

Start with

• Administrative use cases (lower risk)

• Cloud deployment (Azure OpenAI, AWS Bedrock, Google Vertex)

• Human-in-the-loop workflows

• Comprehensive testing protocols

Build toward

• Clinical applications

• Multimodal integration

• Agentic AI systems

• Real-time decision support

Architecture Guidelines

Infrastructure

Cloud-first with HIPAA BAAs

• FHIR-compliant data architecture

• TLS 1.2+, OAuth 2.0

• Comprehensive audit logging

Governance

• AI oversight committee

• Risk management framework

• Bias assessment protocols

• Continuous monitoring

Selection framework (HIMSS Five Rights)

• Right patient

• Right time

• Right information

• Right format

• Right purpose

Conclusion

Healthcare organizations must choose between three paths: proprietary models for quick deployment, open-source for control and customization, or platforms for integrated solutions. Success depends on matching technology to organizational capabilities, regulatory requirements, and clinical needs. Start with low-risk applications, establish governance, then expand to clinical use cases.

References

1. Grand View Research. (2024). AI In Healthcare Market Size, Share & Industry Report, 2030.

2. Aisera. (2024). Large Language Models in Healthcare: Medical LLM Use Cases.

3. Analytics Vidhya. (2024). Llama-3-Based OpenBioLLM Models Outperform GPT-4 and Med-PaLM.

4. CDO Magazine. (2024). Google Announces Med-Gemini AI Models for Healthcare Use.

5. Fierce Healthcare. (2024). Google unveils MedLM generative AI models for healthcare.

6. Harvard Medical School. (2025). Open-Source AI Matches Top Proprietary LLM in Solving Tough Medical Cases.

7. HIPAA Journal. (2025). Is ChatGPT HIPAA Compliant? Updated for 2025.

8. Hugging Face. (2024). BioMistral-7B Model Card.

9. Hugging Face. (2024). Meditron-70B Model Card.

10. MedTech Dive. (2024). The number of AI medical devices has spiked in the past decade.

11. Meta AI. (2024). Meditron: An LLM suite for low-resource medical settings leveraging Meta Llama.

12. Microsoft. (2024). DAX Copilot: Healthcare innovation that refocuses on the clinician-patient connection.

13. Microsoft. (2024). Unlocking next-generation AI capabilities with healthcare AI models.

14. Nature Digital Medicine. (2025). Implementing large language models in healthcare while balancing control, collaboration, costs and security.

15. Nebuly. (2024). OpenAI GPT-4 API Pricing Guide.

16. OSF HealthCare/Fabric Health. (2024). Case Study: $2.4 million ROI with AI-powered virtual assistant.

17. PubMed Central. (2024). Beyond ChatGPT: What does GPT-4 add to healthcare? The dawn of a new era.

18. PubMed Central. (2024). Evaluating multimodal AI in medical diagnostics.

19. Stanford HAI. (2024). Large Language Models in Healthcare: Are We There Yet?

20. The Lancet Digital Health. (2024). A future role for health applications of large language models depends on regulators enforcing safety standards.