AI in Healthcare: Tools Transforming Patient Care in 2026

Writing about AI in healthcare carries a responsibility that most tool categories do not. A mediocre translation is embarrassing; a mediocre clinical decision is not. So before the picks, let me be clear about what I evaluated and what I did not. I am a tool reviewer, not a clinician, and my testing covered the things I can judge honestly: how the products turn a recorded encounter into a note, how the decision-support logic is presented and sourced, how the workflow attaches to an EHR, and what the commercial terms look like. Anything that touches a patient decision needs clinical validation by qualified people at your own institution. Where I could not verify a claim — pricing, rankings, integration depth — I say so in the pick rather than smoothing it over.

The audience I wrote this for is clinicians and healthcare administrators drowning in documentation — the category's actual problem. My criteria were output accuracy and edit distance (how much fixing a note needs), EHR and workflow fit, compliance posture (HIPAA, BAAs, data retention), and pricing transparency. That last one matters more here than anywhere: three of the five products below will not tell you what they cost without a meeting, and I find that worth saying plainly.

Nabla

Nabla is an ambient AI scribe: it listens to the consultation and drafts the note — SOAP notes, specialty templates, referral letters — for the clinician to review. What I noticed first in my walkthroughs is how little ceremony there is. The clinician talks to the patient, and a structured note appears, with a clean section layout and, for straightforward single-problem visits, very little to fix. On messier encounters — multiple problems, corrections mid-sentence, a patient who changes topic three times — the note needed reordering and some merging of duplicated sections, but the substance was there. The specialty templates are the quiet win: a dermatology note and a psychiatry note come out shaped differently, the way clinicians actually write them.

At time of writing, third-party sources report a free tier covering up to 30 consultations per month and a paid tier around $119 per user per month for unlimited consultations, with enterprise pricing for larger deployments; confirm current figures with Nabla. It integrates with major EHRs and is used across specialties. Best for: individual clinicians and small practices who want documentation help without a procurement project. The honest limitation: every note is a draft requiring clinician review, and the free tier's consultation cap means real usage lands on the paid plan quickly.

Suki

Suki is the enterprise-leaning ambient assistant: it does the listening-and-drafting job, then adds voice commands on top — ask it to generate a referral letter or summarize the encounter and it produces the document from the visit it just heard. That command layer is the differentiator I tested most, because it targets the after-visit work that ambient scribing alone does not cover. The draft quality in my walkthroughs was consistent with the rest of this field: structured, clinically formatted, and requiring a careful read. Medication lists and dosing details are exactly where I would insist on that read — the parts a language model is most likely to garble are the parts that matter most clinically.

Suki integrates with the major EHR systems (vendor-listed integrations include Epic, Oracle Health, athenahealth, and MEDITECH — check current support for your version). Pricing is not public; third-party reports at time of writing put it in the range of $299–399 per clinician per month, negotiated at the organization level. Best for: health systems standardizing documentation support across departments with IT backing. The limitation: this is a sales conversation, not a download, and smaller practices will feel the friction — and the price — long before they see the enterprise features.

Glass Health

Glass Health sits in a different corner: clinical decision support rather than documentation. It generates differential diagnoses and drafts assessment-and-plan options from a case description, with reasoning a clinician can inspect. I entered a set of case vignettes and found the differentials well-organized and the reasoning legible — which is what makes a tool like this usable, since a bare list without rationale teaches you nothing and catches nothing. The assessment-and-plan drafts give you a starting structure to argue with rather than a blank page, which is a modest claim that it actually delivers. It also does ambient scribing now, so the boundary between it and the pure scribes above is blurring.

Pricing is unusually transparent for this field: Glass's own pages list a free Lite tier and paid plans that at time of writing ran around $20 per month (Starter), $90 per month (Pro), and $200 per month (Max), with annual billing lower. Best for: clinicians who want thinking support — students, trainees, and generalists working outside their usual domain. The honest limitation: this is decision support, not diagnosis, the evidence behind any given suggestion varies, and a smaller vendor means fewer enterprise assurances than the incumbents offer.

Viz.ai

Viz.ai works at the hospital level rather than the desk. Its FDA-cleared algorithms analyze medical imaging for suspected time-critical findings — the stroke pathway is the best-known — and notify the specialist care team through a shared communication workflow, cutting the phone-tree delay between scan and treatment decision. That coordination layer, not just the detection model, is the product, and it is why hospitals buy it. Watching the alert land on every care team member's phone at once shows the design intent: the algorithm is only useful if the right specialist sees it in the first minutes. More recently the company launched Viz Assist, a suite of AI agents that summarize patient records and prior imaging for clinicians ahead of the visit.

Viz.ai cites a top ranking in a 2026 Black Book clinical decision support survey (vendor-published, so read it as marketing evidence, not mine). Pricing is quote-only enterprise contracting. Best for: health systems with specialty service lines where time-to-treatment drives outcomes — stroke, cardiology, vascular. The limitation: a clinician cannot buy this; it lives or dies on institutional rollout and radiology workflow integration, and outside the established pathways the value is less proven.

Tempus AI

Tempus AI is precision medicine infrastructure: genomic testing, a large clinical data platform, and AI tools aimed mostly at oncology — matching patients to therapies and clinical trials, and helping tumor boards work from molecular data instead of only morphology. The company went public in 2024 and works with health systems, labs, and pharma rather than selling to individuals. What I looked at is the data-and-matching layer, where the interesting claim is that structured clinical data plus molecular profiling can surface options a busy oncology team might not reach unaided.

There is no per-seat price; the relationship is organizational and often runs through testing services. The trial-matching workflow is the piece I could evaluate most concretely: pulling trial eligibility signals from structured records is mechanical work that clinicians otherwise do by hand against inclusion criteria nobody has time to re-read. Best for: oncology programs building molecular tumor boards or trial-matching capability. The honest limitation: this is not a bedside tool you turn on, and the value depends heavily on how much of your patient data flows into the platform — an institutional decision with privacy trade-offs to weigh.

How to choose

Start with the burden you are actually carrying. If it is note-taking time, pick an ambient scribe — Nabla for low-friction individual adoption, Suki for a health-system rollout with command-driven follow-up work. If it is clinical reasoning support, Glass Health is the one you can try this week. If it is pathway speed on imaging findings, that is Viz.ai and a procurement conversation. If it is molecular oncology data, Tempus is a partnership, not a purchase. Across all five: ask about the BAA, data retention, and whether your recordings train models before you sign anything, and run a pilot on real clinic days before trusting any time-savings claim — including the vendors'. Match the buying motion to your size too: a solo clinician has exactly one product here they can start using without a meeting, and pretending otherwise wastes everyone's time.

FAQ

Do ambient AI scribes actually save time?
The vendors publish large time savings, and clinician reports I have read consistently describe faster note completion — but every source is either the vendor or self-report. The realistic picture: the note gets drafted faster, the review still takes minutes per encounter, and the savings appear over a full clinic day rather than per patient.

Are these tools HIPAA compliant?
The established vendors sign business associate agreements and describe compliant data handling, but compliance is a shared responsibility. Ask specifically about where audio is stored, whether recordings train models, and retention windows — the answers differ more than the marketing suggests.

Can any of these tools diagnose a patient?
No. The detection products flag findings for specialists to confirm, and the decision-support tools present suggestions for clinicians to judge. The diagnostic decision remains with the clinician in every one of these workflows — that is not a disclaimer, it is how the products are designed.