Measured
Medical knowledge
Exam-style questions, biomedical facts, and bounded clinical reasoning where represented.
Best AI for healthcare in 2026
Qwen3.7 Max currently leads the healthcare category, followed by Qwen3.7-Plus. The ranking compares published model evidence—not clinical fitness, diagnosis quality, or regulatory status.
- Models
- 244
- Benchmarks
- 41
- Refresh
- Hourly
- Ranking influence
- None for sale
Evidence before deployment
Four gates · no skipped review
01
Known-answer cases
Representative, de-identified inputs
02
Source verification
Every material claim checked
03
Safety gate
Critical omissions and unsafe actions
04
Clinician review
Named owner makes the final call
Benchmark rank → shortlistLocal evidence → decision
Reviewed by Jonathan Chavez · 244 models across 41 healthcare benchmarksSources & review
Review boundary
Data methodology reviewed internally; the editorial interpretation has not been independently reviewed by a physician, clinical informaticist, or regulator.
Scope and disclosure
This page ranks underlying model evidence, not healthcare products, regulated medical devices, diagnoses, or treatment systems. LLM Stats accepts no payment for ranking position. Read the methodology or report a correction.
Best AI models for healthcare
Compare healthcare category scores and benchmark coverage. These scores rank model performance on the listed tests; they do not measure clinical accuracy.
RankModelEvidence signalInput priceScore
02
Qwen3.7-Plus
Alibaba Cloud / Qwen Team
Category-relative
Input: Not listed
per 1M tokens
42.1
category score
03
Qwen3.6 Plus
Alibaba Cloud / Qwen Team
Category-relative
Input: $0.50/M
per 1M tokens
42.0
category score
04
Qwen3.5-397B-A17B
Alibaba Cloud / Qwen Team
Category-relative
Input: $0.45/M
per 1M tokens
39.2
category score
05
Qwen3.5-122B-A10B
Alibaba Cloud / Qwen Team
Category-relative
Input: $0.29/M
per 1M tokens
38.2
category score
What healthcare benchmarks measure
The category can reveal comparative model capability. It cannot establish whether a finished system is safe, current, private, equitable, usable, or appropriate for a particular clinical workflow.
Partly measured
Evidence use
Source-grounded reasoning varies by benchmark and by the product surrounding the model.
Not established
Real patient outcomes
A category score is not evidence of improved diagnosis, treatment, or patient outcomes.
Validate locally
Deployment safety
Privacy, bias, escalation, monitoring, human factors, and regulatory status need separate review.
AI for medical questions, clinical notes, and research
The highest-ranked model is not automatically the best deployment. Different healthcare jobs change what must be tested, what data may be used, and who must approve the output.
01
Medical research
Ranking signal: Knowledge, reasoning, long context
Verify separately: Citation validity, publication status, source retrieval, quantitative extraction
02
Patient education drafts
Ranking signal: Clarity, instruction following, multilingual ability
Verify separately: Reading level, uncertainty, local guidance, harmful omission, clinician sign-off
03
Clinical documentation
Ranking signal: Summarization, extraction, structured output
Verify separately: Hallucinated facts, missing negatives, note provenance, PHI handling, workflow fit
04
Coding and administration
Ranking signal: Classification, consistency, tool use
Verify separately: Current code set, payer rules, audit trail, false positives, human review cost
05
Clinical decision support
Ranking signal: Relevant medical reasoning only
Verify separately: Intended use, regulation, representative validation, bias, escalation, monitoring, accountable clinician
Full healthcare benchmark matrix
Open individual benchmark columns, compare model coverage, and inspect the evidence behind the summary rank. Sparse coverage should increase uncertainty, not confidence.
Test medical answers against reviewed cases
Use the leaderboard to create a shortlist, then test the actual product and workflow. Publish the case specification, rubric, settings, dates, failures, and limitations so another team can reproduce the comparison.
01
Freeze the system
Record the exact model, product version, settings, source pack, tools, prompt, and date.
02
Build known-answer cases
Use de-identified or synthetic cases that reflect specialty, language, acuity, ambiguity, and edge conditions.
03
Score the failures
Measure factual error, unsupported claim, critical omission, unsafe recommendation, citation validity, calibration, and escalation.
04
Run the safety gate
Exclude a candidate after any predefined severe unsupported action—regardless of its average score.
05
Measure the workflow
Track correction time, consistency, latency, cost, reviewer burden, and performance when evidence conflicts or is absent.
06
Monitor after change
Repeat after model, prompt, retrieval, policy, interface, or population changes; retain outputs and reviewer decisions.
What this ranking does not prove
It does not certify a product, establish regulatory status, replace medical judgment, or show improved patient outcomes. Medical knowledge benchmarks can also miss specialty, demographic, language, temporal, and workflow-specific failures.
No diagnosis or treatment recommendation+
This page is comparative model research, not medical advice. Patients should use qualified healthcare professionals for symptoms, diagnosis, medication, or treatment decisions.
No privacy assumption+
Do not enter identifiable patient information into a product until your organization has approved its data handling, contracts, access controls, retention, and jurisdictional requirements.
No product equivalence+
The same model can behave differently across interfaces, prompts, retrieval systems, guardrails, tools, and versions.
No universal winner+
A strong average can conceal a severe failure. Local, subgroup-aware, intended-use validation matters more than a narrow lead in aggregate rank.
Healthcare AI: clinical use, privacy, and limitations
Brief answers to the questions that matter before a benchmark turns into a procurement or deployment decision.
What is the best AI for healthcare?+
The live ranking at the top names the current leader in LLM Stats’ healthcare category. It is the strongest available category signal, not an automatic choice for patient care. The best deployment depends on the intended use, product controls, local evidence, and accountable clinical review.
Can the highest-ranked AI diagnose disease?+
No conclusion about diagnostic safety or authorization follows from this leaderboard. Diagnosis is an intended-use and deployment question that requires appropriate evidence, oversight, and applicable regulatory review.
Does this page rank healthcare products?+
No. It ranks underlying AI models using the available healthcare benchmark evidence. EHR copilots, ambient scribes, imaging systems, medical search products, and regulated devices require different product-level comparisons.
How should a hospital evaluate a model?+
Freeze the complete system, use representative de-identified or synthetic known-answer cases, predefine severe-failure gates, score subgroups and edge cases, retain raw outputs, involve qualified clinical and governance reviewers, and repeat after every meaningful change.