AI Tools for Healthcare (2026)

Five Jobs, Not One Subscription

Most health systems do not have an AI problem. They have five different jobs and one subscription pretending to cover all of them. Split the work first and the decision gets cheaper, safer, and far easier to defend to a regulator.

Documentation, patient communication, back-office automation, literature review, and diagnosis carry completely different risk. The first four can be assisted today. The fifth needs regulatory clearance, and nothing on this page changes that.

The Shortlist, With Status

Each row links to a tracked status page carrying the last automated check, a confidence score, and any death signals we picked up. Never adopt from a vendor deck alone - open the status page and look at the date.

For adjacent stacks, see AI tools for research and citations, AI tools for transcription, and AI tools for lawyers - the compliance instincts transfer directly.

Which of These Could Disappear

Shutdown risk in health AI is rarely about product quality. It is about whether the vendor can survive the gap between a pilot and a paid contract. Point solutions that only summarise are most exposed, because general models now do that adequately at commodity prices.

Why Healthcare AI Tools Die

We track cause of death across every tool in the graveyard. In healthcare the distribution is unusual: money is rarely the first cause, it is the last one. Evidence gaps and procurement delays drain the runway, and the funding headline arrives afterwards.

Babylon Health is the clearest example. Enormous consumer reach, a fast public listing, and a triage product whose clinical claims drew sustained scrutiny - see what happened to Babylon Health. Olive AI shows the enterprise version of the same trap: automation sold across hundreds of hospitals, then wound down and split into pieces when the promised savings proved hard to evidence. Galactica shows the fastest version of all, withdrawn within days of launch after it produced confident, wrong science. Independent coverage of the wider pattern is worth reading at the Nature machine learning collection and the WHO guidance on ethics and governance of AI for health.

Pick a Tool in 60 Seconds

You do not need a committee for the first pass. You need to know which job you are buying for, then check the two things vendors do not lead with: the data clause and the export.

The 7-Day Migration Plan

If a tool you rely on is showing death signals, you do not need a project. You need one focused hour a day for a week. This is the same sequence we recommend after every shutdown we cover.

1. Inventory. List every AI tool in use, including personal accounts, with owner, plan tier, cost, and which of the five jobs it touches.
2. Terms. Paste each tool's training-exclusion and retention clauses, with the date read, into the same sheet. No clause means no patient data, starting today.
3. Status. Open each tracked status page and record the last check date and any death signals. Flag anything dormant or acquired.
4. Export. Pull full exports of notes, templates, and audit logs into systems you control. Clinically relevant content belongs in the record, not the vendor.
5. Shortlist. One tool per job. Cancel duplicates - most organisations pay twice for the same summarisation.
6. Pilot. One clinic, one month, a named clinical owner, and a written measure of success beyond "staff liked it".
7. Write it down. One page: approved tools, approved uses, prohibited uses, review requirement, next review date.

Five Mistakes Health Teams Keep Making

1. Treating a chatbot as decision support. A general model has no clearance and no duty of accuracy. It will produce a confident answer with no basis, which is precisely how Galactica ended.
2. Buying at system level before testing at clinic level. Run one real workflow first. Vendor pilots on curated samples flatter everyone.
3. Assuming consumer terms match enterprise terms. They rarely do, and the difference is exactly the clause protecting your patients.
4. Skipping consent for ambient capture. Recording a consultation is a patient-facing decision, not an IT one. Get the script agreed before rollout.
5. Ignoring death signals. Silent changelogs and slow support preceded both major collapses in this category. Check Olive AI's status for how that reads in hindsight.

What This Actually Costs

General models sit at roughly twenty to thirty dollars per seat per month. Clinical-specific platforms are negotiated annually and land far higher per seat, because you are buying validation, integration work, and an indemnity posture rather than a chat box.

The real cost is rework and risk. One incorrect summary reaching a patient record can consume more clinical time than a year of subscriptions, and it arrives with no warning. Judge spend against that number, not against the free tier.

Price the exit too. Exporting templates and audit logs is an afternoon if you have done it before and a fortnight if you have not. Knowing which it is for your organisation is most of the value of this page.

Keep Exploring the Graveyard

Regulated industries fail the same way everywhere: rented models, thin moats, and buyers who move slowly. If you are auditing a wider stack, see AI tools for the legal industry, AI tools for education, AI tools for research, and AI tools for transcription.

Want the underlying data? Browse every tracked tool from the homepage, compare survivors on the leaderboard, read the latest postmortems, or report a tool you believe is winding down.

Bottom line: choose per job, keep identifiable data behind a signed contract, never treat a general model as decision support, and keep an export you control. Do that and no shutdown can take your clinical work with it.