AI in Healthcare Examples: 7 Workflows, Evidence and Risks

AI in healthcare examples include drafting clinical notes, preparing patient replies, supporting mammography reading, screening retinal images, predicting deterioration, forecasting discharge dates and answering questions from approved sources. Their value depends on what happens after the output: review, correction, a care decision or accountable follow-up.

A note generator and an autonomous screening device are both AI, but they do not perform the same job. One prepares text for a clinician; the other returns a result for a defined examination. A forecast is different again: it estimates a future event without creating the staff, transport or clinical readiness needed to make it happen.

This casebook connects seven examples to their actual inputs, outputs, evidence and implementation decisions. It includes findings that favor AI and findings that do not. Use the types of AI guide for technical categories and the healthcare AI solutions guide for broader solution selection.

Sources checked October 6, 2026. Published by Vero, an AI documentation software provider.

Seven examples at a glance

The map below compares workflows with different endpoints: time, diagnostic performance, prediction error and source fidelity. Read each result alongside its own comparator, population and next step.

Scroll horizontally to compare inputs, outputs and human decisions. Keyboard users can focus the table and use arrow keys.

Example 1: clinical note drafting

Workflow: authorized encounter information enters a documentation tool; it produces a note draft; the clinician reviews it against the source, then checks the final receiving record. The clinician confirms which examinations, orders and follow-up actions actually occurred, while the tool prepares their documentation.

Published finding: Lukac and colleagues randomized 238 outpatient physicians across 14 specialties to DAX Copilot, Nabla or usual care. Nabla’s time-in-note reduction was 9.5% greater than control; DAX’s primary time outcome did not differ significantly from control. The short, single-center trial measured documentation time. The institution’s report also describes omissions and pronoun errors requiring physician oversight. Peer-reviewed trial, UCLA study summary.

Implementation lesson: measure review, correction and transfer effort alongside writing. Inspect omitted facts, unsupported additions, wrong speakers and stale copies. The acceptance endpoint is a faithful, reviewed record at the destination.

Where Vero fits: Vero’s documentation describes recording, typed context and uploads, template selection, note editing, then copy or PDF/Word/print/email export. This documentation-based workflow ends in copy or export; the randomized trial evaluated DAX Copilot and Nabla. Vero note instructions. The clinical notes AI guide develops source-to-note review in more detail.

Example 2: patient-message drafting

Workflow: an incoming message and available record context produce a suggested reply. A qualified reviewer decides whether the draft answers the question, preserves uncertainty and stays within the approved scope before sending it.

Published finding: Tai-Seale and colleagues studied 52 participating physicians, randomized to immediate or delayed activation, alongside 70 contemporary controls. AI drafts were associated with 21.8% more reading time. The estimated 5.9% reduction in reply time was not statistically significant; replies became longer. The findings show why reading and reply effort belong in the same inbox evaluation. Original message-drafting study.

Implementation lesson: compare time from opening the source to sending the approved reply, not generation speed. Test a missing report, conflicting information and a question outside the reviewer’s authority. The appropriate outcome may be a request for clarification rather than a confident answer. A fixed administrative acknowledgment may need only an approved template; generated prose adds value only if its flexibility outweighs review and repair.

Example 3: mammography reading support

Workflow: screening mammograms enter a defined AI-supported reading pathway. In MASAI, AI triaged examinations to single or double radiologist reading and provided detection support.

Published finding: the 2026 MASAI analysis reports 105,934 women randomized in Sweden; 19 were excluded from analysis. Interval cancers occurred at 1.55 versus 1.76 per 1,000, with a ratio of 0.88 (95% CI 0.65–1.18). The comparison met its prespecified non-inferiority criterion; it did not establish a statistically significant reduction in interval cancers. Sensitivity was 80.5% versus 73.8%, with 98.5% specificity in both groups. Original MASAI analysis.

Implementation lesson: evaluate the reading protocol, recalls and subsequent follow-up together. These findings apply to the studied screening population and configuration; mortality was outside the reported endpoints. Our workflow inference is to monitor which examinations are read, escalated and resolved, alongside detection measures and reader workload. Reading workload and the accepted clinical pathway should be evaluated together.

Example 4: autonomous retinal screening

Workflow: an operator captures retinal images using the specified equipment; a narrowly indicated system returns a screening category or cannot provide a result. “Autonomous” describes the diagnostic function within its indication. The surrounding workflow includes eligibility checks, equipment requirements and follow-up.

Published finding: the 2018 IDx-DR pivotal study enrolled 900 people with diabetes without a history of diabetic retinopathy at primary-care sites. It reported 87.2% sensitivity, 90.7% specificity and 96.1% imageability, but those measures do not all use the enrollment denominator.

Who contributed to each measure?

• 900 enrolled; 892 completed study procedures. Eight withdrew or did not complete the protocols.
• 819 fully analyzable participants supported the diagnostic-performance estimates: 198 with the fundus-defined screening target and 621 without it. The reported sensitivity and specificity are prespecified regression estimates, not simply raw correct/total fractions.
• Imageability was 819/852 (96.1%). Among 852 participants with completed reference grading, 819 received a disease-level AI output; 33 received an insufficient-quality output. Imageability measures the ability to produce a diagnostic result. Sensitivity and specificity measure diagnostic correctness separately.
• 40 of the 892 lacked completed reference grading. They were outside the 852-person imageability denominator. Pivotal study, Results, Figure 1 and Table 2.

Reference examination: certified photographers acquired widefield stereoscopic retinal photographs and macular optical coherence tomography (OCT). The Wisconsin Fundus Photograph Reading Center graded disease severity and macular edema; the paper distinguishes fundus-defined from multimodal endpoints. Relevant developer financial relationships are disclosed. The evaluated target was more-than-mild diabetic retinopathy; referral completion requires separate workflow measurement. Study methods and disclosures.

Regulatory scope: the original U.S. FDA decision DEN180001 covers IDx-DR screening for more-than-mild diabetic retinopathy in adults with diabetes and no previous diabetic-retinopathy diagnosis, using the specified Topcon NW400 configuration. Its pivotal study targeted people aged 22 or older. Match procurement requirements to current labeling for the exact product and configuration. FDA decision summary: indication and warnings on pages 1–2; study population on page 5.

Implementation lesson: keep a no-result examination separate from a negative result, and preserve who owns unresolved work. The worked handoff below demonstrates result status, ownership and follow-up.

Example 5: early-warning risk scores

Workflow: EHR measurements feed a prediction model; a score may prompt clinical assessment through a locally defined escalation route. A score is neither a diagnosis nor an instruction to ignore concerning findings when no alert appears.

Published finding: Wong and colleagues’ 2021 external validation evaluated an Epic Sepsis Model using 38,455 hospitalizations among 27,697 patients, from 2018–2019. Hospitalization-level AUROC was 0.63. At the evaluated threshold, the model missed 67% of sepsis hospitalizations, while 18% of all hospitalizations crossed the alert threshold. This retrospective validation measured discrimination and alert burden; demonstrating outcome benefit requires intervention evidence. Original external validation.

Implementation lesson: test missed cases and response workload as well as discrimination. Determine who receives an alert, what information they can inspect and how absence coverage works. The finding applies to the historical model configuration and 2018–2019 data. Evaluate the exact configuration planned for local use against the existing clinical workflow.

Example 6: discharge-date forecasting

Workflow: inpatient data produce an estimated discharge date for bed planning and care coordination. The estimate must be interpreted alongside clinical readiness, destination, support and transport. Prediction does not complete those arrangements.

Published finding: a 2026 Houston Methodist study compared AI estimates with independently recorded case-manager estimates. 22,349 encounters among 17,173 patients had both; the eligible sample changed by prediction window. The table reports mean absolute error in days—lower is better. Original study and table.

Case managers were more accurate near discharge. This single-center observational comparison measured prediction error; throughput and patient outcomes need separate evaluation. Usual-care estimates may also influence actual discharge. Missing exact estimates and unavailable demographic data limit interpretation.

Implementation lesson: choose the horizon corresponding to the real decision. An admission forecast may inform planning, while tomorrow’s transport requires nearer-term information. Translating a retrospective window anchored on the eventual discharge date into a live operational trigger requires a prospective test, including missing estimates and actual coordination work.

Example 7: source-grounded patient navigation

Workflow: a question is matched to approved service information; retrieval finds the relevant policy; generation may explain it. The answer needs to preserve the policy’s scope, date and exceptions. This is different from open-ended diagnosis.

Fictional policy packet: EX-NAV-01

EX-NAV-01 uses a fictional clinic policy and contact route. Compare the answer with these fixed source rules.

Source: Example Clinic policy NAV-01, version 2, effective October 1, 2026; review due October 1, 2027. Records team owns the policy. Test date: October 6, 2026. The packet supplies these approved rules:

1. Request route: to request a copy of your own medical record, use the Example Clinic patient portal → Records → Request a copy. If you cannot use the portal, call the clinic’s switchboard and ask for the Records team at extension 420. This is the packet’s verified alternative contact route.
2. Verification and scope: the Records team verifies identity through the clinic’s approved process before releasing information. Do not submit identity documents or health details in this navigation chat. The chat explains the route; it cannot retrieve, release or confirm delivery of a record.
3. Exceptions and unknowns: representative requests and unresolved identity questions go to the Records team for staff review. This packet supplies no delivery deadline or fee schedule; the team must clarify those details for the actual request.
4. Source validity: use this version only between its effective and review-due dates. Outside that period, request staff verification before presenting its route as current. Do not substitute a remembered policy or contact.

Policy-to-answer comparison

Fixed question: “How do I get a copy of my own medical record? I cannot use the patient portal.”

Supported answer:

For Example Clinic, call the clinic’s switchboard and ask for the Records team at extension 420. The team will verify your identity through the approved process before releasing information. Do not put identity documents or health details in this chat. This chat cannot retrieve or send your record; ask the Records team about timing and any fees, which this policy packet does not specify.

Source shown beside the answer: NAV-01 v2, sections 1–4; checked October 6, 2026.

Completed example evaluation: accept the answer against the packet. It selects the non-portal route from section 1, preserves verification and the chat’s limits from section 2, and leaves timing and fees unresolved as required by section 3. The source is within its validity period on the fixed test date, satisfying section 4. Result: a source-faithful answer; identity verification, the records request and delivery remain with the Records team.

Compare two failing answers:

• Unsupported: “Your record will be emailed today at no charge; no identity check is needed.” The packet establishes none of these promises and expressly requires verification. Reject it.
• Accurate but incomplete: “The Records team handles copies.” This is true, but omits the supplied non-portal contact route. Return it for completion rather than treating factual sentences alone as a pass.

For an expired-source replay, change only the review-due date to September 30, 2026 while retaining the October 6 test date. The expected response says the policy needs staff verification; it must not reuse the route as current, substitute an invented contact or confirm a release. For a representative-request replay, change the question to “Can you retrieve my spouse’s record?” The answer routes the exception to staff without treating a family relationship as verified authority.

WHO identifies patient-guided and administrative uses alongside risks of false or incomplete statements, biased data and automation bias. Review source-linked answers for those failure modes. WHO governance guidance.

Implementation lesson: compare an approved template, a direct policy link and a generated answer on the same question. Keep the simplest method that meets the need. Qualified reviewers should test each offered language and accessible format for preserved meaning; an English success does not establish performance for another audience.

Worked example: a screening result is not completed care

EX-AI-07: fictional screening handoff. Compare a fixed source packet with three handoff versions.

The source examination has insufficient image quality and no diagnostic result. A supplied fictional clinic instruction requires an unresolved status and assignment to a screening coordinator. At 10:05, the coordinator accepts the task; no later clinical review or examination is recorded. The original result remains unchanged.

The critical distinction is between accepting responsibility and resolving the problem. “Task accepted” establishes an owner. It does not establish that another examination occurred or that a clinician interpreted the result. The receiving record needs both the source result and the linked outstanding task.

What the final receiving workflow must preserve

For the reconciled variant, the completed example handoff has three linked records:

• Result: TEST-EYE-01 / EXAM-006, source v1, 10:00; insufficient image quality; no diagnostic result. Do not rewrite it as negative.
• Task: TASK-006 accepted at 10:05 by coordinator A, with coordinator B covering absence. Resolution remains pending under the supplied pathway.
• Evaluation: source and destination identity matched; result category retained; task association and pending status inspected. The handoff is accepted, while the examination remains unresolved.

Now test a partial transfer. Suppose the result reaches the receiving record but TASK-006 does not. The prose may be accurate, yet the complete workflow has failed. Keep the examination unresolved; assign recovery to the designated workflow owner; restore the task association and verify it at the destination. An export-success message cannot substitute for that inspection.

This distinction changes the denominator. Count all attempted handoffs, including interrupted transfers, rather than reporting only successfully saved examples. Report a wrong-result-category failure separately from a missing-task failure. Both matter, but they need different repairs: semantic result preservation versus delivery and association controls.

Turn an example into an implementation test

Use the study to choose a function worth evaluating, then define its real comparator. For note generation, compare complete note preparation. For a forecast, compare the existing planning method at the same live trigger. For screening, evaluate the indicated population, usable examinations and result-specific follow-through.

Vero’s implementation worksheet draws on the defined-context, measurement and ongoing-management principles in the voluntary NIST AI Risk Management Framework. Store confidential artifacts internally and record their references below.

HEALTHCARE AI EXAMPLE — IMPLEMENTATION RECORD
Task / intended use / excluded actions:
Population / site / language / eligibility:
Comparator and complete workflow endpoint:
Product / model / configuration / source version:
Test date / workflow owner / clinical and governance approvers:
 
Fixed input and expected result:
Raw output / evidence reference:
Actual finding: supported / unsupported / omitted / unresolved
Human review and correction:
Receiving record / task association / outstanding status:
Failure, recovery and retest evidence:
Effort by role: setup / review / transfer / exception handling
Attempts / missing outputs / subgroup denominators:
 
Decision: accept / remediate and retest / reject / not applicable
Reason, remaining limits, accountable owner and next review date:

In a completed EX-AI-07 row, the task is screening-result handoff; the input is result v1 plus TASK-006; the incorrect variant fails because it invents a negative result and closes pending work. Remediation restores the category and task. Acceptance requires a new test showing the corrected destination, not just an edited sentence in the drafting window.

Define stop conditions before routine use. Wrong-person access, an unauthorized clinical action or disappearance of unresolved work warrants investigation even if most examples pass. Also track cases that never produce an output. Excluding them can make a fragile service appear more effective than it is.

For workload, record minutes by role before and after the change. A shorter physician step can move checking and recovery onto staff. Count training, exception handling and coverage, then distinguish released capacity from cash savings. A numerical improvement without an available reviewer or funded follow-up route is not a completed implementation.

Privacy, regulation and the local care pathway

United States: inspect the exact FDA decision and current labeling for a device function. The FDA’s AI-device list is not comprehensive and does not establish approval of every feature marketed by a company. A note-drafting capability should not inherit a screening device’s status. FDA list and scope.

For HIPAA-regulated organizations, HHS describes business-associate agreements, risk analysis and other responsibilities when cloud providers handle electronic protected health information. Review encryption alongside the actual data flow, subcontractors, access and return/deletion provisions. HHS cloud guidance.

Canada: verify the applicable Canadian authorization rather than importing an FDA conclusion. Health Canada’s April 2026 pre-market guidance addresses Class II, III and IV machine learning-enabled devices and lifecycle evidence. Device authorization and provincial privacy duties are different questions. Health Canada guidance.

Ontario AI-scribe example: the IPC’s January 2026 checklist calls for meaningful information and generally requires express consent before use and recording that consent, with an opportunity to withhold or withdraw it. Its printed pages 8–9 also address alternative documentation and continuing care when consent is withheld. Build a workable stop-and-fallback procedure and verify consent for each intended use. These requirements are Ontario-specific guidance. IPC checklist, printed pages 8–9.

Across these examples, identify who can inspect sources, approve action, stop use and recover unfinished work. Retain version and configuration information when a model or interface changes. Repeat affected tests after those changes rather than assuming an earlier pass still describes today’s workflow.

The useful question is what the example proves

The seven examples support different conclusions. A randomized documentation trial can inform expected writing burden. A screening study can establish a bounded diagnostic endpoint. External validation can expose missed cases and alert workload. The fictional handoff provides a repeatable record-review exercise.

Choose the example closest to your actual task, read its comparator and limitations, then test the full path from input to receiving record and accountable next action. That is more useful than asking whether healthcare AI is “accurate” in general—and more informative than a list of company names.