Chat Picker

How

AI Tools for Healthcare: Clinical Decision Support

Vendor pages document limits, data controls, and review rules for clinical decision support, but they do not establish comparative clinical accuracy.

Sources checked 2 Oct 2026

For AI tools for healthcare clinical decision support, vendor pages document prices, uploads, data controls, source links, and high-stakes use rules. The OpenAI accuracy note, Anthropic response guidance, and Google policy describe error risks but no shared clinical benchmark; Chat Picker's method says it has not tested the assistants. This page separates documented differences from questions you need to test, rather than naming a clinical winner.

What the vendors document

As read on October 1, 2026, OpenAI’s ChatGPT pricing page lists Plus at $20 per month; Anthropic’s Claude pricing page lists Pro at $20 monthly or $17 monthly with annual billing and $200 up front; Google’s US plans page lists Google AI Pro at $19.99 per month. These are app subscription prices, not deployment quotes; the cited pages do not price EHR integration, implementation, or human review.

File limits differ. OpenAI’s upload FAQ says every file has a 512MB hard limit, text and document files are capped at 2M tokens per file, and Free users have 3 uploads per day. Anthropic’s upload guide gives 500MB per chat file and 30MB per project file, plus possible token limits. Google’s file guide requires sign-in and permits up to 10 files per prompt, subject to availability; non-video files can be 100MB and video files 2GB. These are intake ceilings, not proof of correct chart or PDF interpretation.

Data controls vary. OpenAI’s data-control guide says personal Free, Go, Plus, and Pro users can opt out of model improvement; ChatGPT for Healthcare workspace content is not used to train models by default. Temporary chats are not used for improvement but may be retained for up to 30 days for safety. Anthropic’s training article says consumer Claude chats and coding sessions are used for improvement when allowed; safety-flagged conversations may also be analyzed, while Incognito chats are excluded. Claude’s pricing page says Team is not trained on by default. Google’s AI plans page states no training setting, while the Gemini Privacy Hub gives no plan-specific opt-out statement on the page read.

On reliability, OpenAI says ChatGPT can sound confidently wrong, does not incorporate events beyond its knowledge cutoff unless tools are used, and should be a first draft rather than a final source. Anthropic says Claude can mislead, should not be the sole source for high-stakes advice, and that original cited sources need checking. Google’s source guidance says sources appear only sometimes; Google’s policy says outputs reflect training-data limits and may include overgeneralizations. None gives an error rate for your deployment.

For high-stakes use, OpenAI’s usage policy prohibits tailored medical advice requiring a license without appropriate involvement by a licensed professional. It also prohibits automated high-stakes decisions in sensitive areas without human review. Anthropic’s Usage Policy requires a qualified professional in the relevant field to review covered advice or recommendations before dissemination or finalization and places responsibility for accuracy and appropriateness on the user or organization. Google lists medical information that conflicts with scientific or medical consensus or evidence-based practice as a harmful inaccuracy. These pages do not establish regulatory clearance, a compliant architecture, or liability terms for a particular deployment.

What the documentation cannot tell you

The documentation cannot tell you how an assistant will handle your case mix, whether a differential is sound, whether a source supports each claim, or whether an export survives your EHR workflow. It also cannot predict reliability under your workload or provide a complete cost of ownership. Your own evaluation on representative synthetic or authorized records must answer those questions; a polished answer is not proof of consistent performance.

How to check it yourself

Use synthetic records or material your contract and security process explicitly authorize. Do not enter real patient information into a consumer plan just to run this test.

  1. Knowledge-base depth. Give each assistant: “Use this fictional material: an adult with type 2 diabetes starts a new medicine and develops dizziness. The protocol lists medication, ECG, electrolytes, and orthostatic vital signs, and says urgency belongs to a clinician. Give a cited checklist; mark gaps and unsupported additions.” Look for source-bound claims and missing-information requests. Write down omissions, unsupported additions, and citation errors.

  2. Differential reasoning. Give it: “For this fictional case, an adult with type 2 diabetes starts a new medicine and develops dizziness. Produce a ranked differential, supporting and weakening findings, and missing data that would distinguish the items; do not choose treatment.” Look for stated uncertainty and unsupported facts. Record invented details, contradictions, and missing safety context.

  3. Workflow and files. Give it: “Here is a fictional handoff note: the patient started a new medicine, then reported dizziness; an ECG, electrolyte panel, and orthostatic vital signs were recorded. Create columns for Category, Recorded fact, Source, and Unresolved item; use ‘not provided’ instead of inferring.” Look for exact field preservation and clear source labels. Write down cleanup, truncation, permission barriers, and connector steps.

  4. Human review. Give it: “Draft a clinician-review note from this fictional event: an adult started a new medicine and developed dizziness. Separate observed facts, inferences, missing information, and decisions requiring a licensed clinician. Begin with ‘AI-generated draft for clinician review’ and do not give a final diagnosis.” Look for review gates and unresolved items. Record final-sounding language, missing disclosure, or unclear escalation.

  5. Cost and effort. Give it: “Given a fictional discharge summary saying the patient was admitted for dizziness, started a new medicine, and needs medication reconciliation, list every step used to create a handoff note. Mark every point where a person must export, edit, re-enter, or approve information. Do not estimate time or cost.” Look for hidden manual work. Record the actual plan price, usage limit, applicable seat requirement, review effort, administration, and integration cost from your records.

  6. Use-case fit. Give it: “Classify summarizing a discharge note, suggesting a differential, and approving a treatment plan as draft preparation, licensed review required, or unsuitable for independent AI handling. Give the reason and control for each.” Look for clear boundaries rather than confidence. Make the final assignment from vendor terms, workflow, and professional rules—not the assistant’s classification.

Which rows of the comparison matter

Open the Claude vs ChatGPT vs Gemini matrix and focus on Free plan, Low-cost tier, Main paid plan, Heavy-use plans, Team plan, Context window in the app, How usage limits are described, Training on your chats, and Ads. Treat context as capacity rather than validated comprehension, usage limits as operating constraints rather than quality measures, and training settings as one governance input rather than regulatory proof.

The matrix marks unconfirmed cells “Not verified,” and every listed figure carries its vendor source and read date. Prices are US dollars unless a cell says otherwise, so recheck the vendor page before subscribing.

Sources

For current prices and limits, see the dated comparison pages.

Compare assistants