AI for Doctors: Use Cases, Evidence and a Practical Review Workflow

AI for doctors includes software that drafts notes and messages, summarizes records, retrieves evidence or supports a defined clinical assessment. The useful question is not whether a tool is “medical AI,” but what task it performs, what evidence supports that task and who checks the result before it changes care.

A note draft, a suggested diagnosis and a medical-image result need different evidence and permissions. A tool that helps organize an encounter cannot automatically be trusted to interpret an investigation, authorize a prescription or send advice. Start with the output you need, then design the review around the consequences of an error.

This guide focuses on the doctor’s working day: choosing a bounded task, checking its sources, handling uncertainty and measuring the effort through completion. For supplier selection across a whole organization, use the healthcare AI companies guide.

Sources checked October 9, 2026. Published by Vero, an AI documentation software provider.

What can doctors use AI for?

It helps to separate generation, which produces wording, from retrieval, which finds existing material, and prediction or classification, which estimates a result for a defined task. One application may contain all three. Evaluate its functions separately rather than transferring confidence from one to another.

Match the tool to the task. For a repetitive message with fixed fields, an approved template may be simpler to inspect than a newly generated answer. For a current policy address or telephone number, retrieving the authoritative entry may be sufficient. Generation earns its place when it handles meaningful variation without making verification impractical.

For clinical interpretation, begin with evidence about the exact system and function. The FDA’s clinical decision-support explanation, checked October 9, 2026, makes clear that the U.S. non-device CDS exclusion depends on specific criteria. Having a physician involved does not, by itself, establish that a feature is outside device regulation.

Which tools fit the task?

Comparison based on vendor documentation, checked October 9, 2026. Compare self-serve drafting, notes plus evidence lookup, and an organization-managed EHR workflow by access, delivery and task fit.

Read the billing unit before comparing prices. Vero’s reviewed public page displays dollar amounts without naming the currency; confirm currency, taxes and billing total at checkout. Heidi’s pricing page localizes its offer; a paid rate in a specified currency was not established in this source check. Microsoft publishes a US$0.25 charge per additional AI-Assisted Session for Flex (25 units × US$0.01), beyond an included monthly pool based on 10 sessions per Flex license. That is a usage charge in addition to the subscription, not the total price or necessarily one charge per patient. Included sessions are shared across licensed users in the tenant and do not roll over. Microsoft’s consumption rules.

For standard notes on ongoing free access, Heidi is the clearest documented starting point here. For a note workspace with typed or uploaded context and explicit export options, evaluate Vero’s route. If delivery inside your organization’s EHR is essential, have IT establish the Dragon Copilot partner workflow before comparing licenses. For evidence lookup, Heidi documents an outside-session route that does not require a patient encounter; use the citation-checking example below to evaluate the answer. Assess diagnostic and imaging tools against their own intended-use evidence.

What do the studies actually show?

Read the endpoint before the headline. Faster note writing, a higher reasoning score and better patient outcomes are different findings. These three studies show why the evaluation should follow the task.

Documentation: an effect in one tool is not a result for every scribe

Lukac and colleagues’ 2025 randomized trial enrolled 238 outpatient physicians across 14 specialties at one health system. Participants received DAX Copilot, Nabla or usual care. Nabla reduced EHR-recorded time per note by 9.5% relative to control; DAX’s estimated 1.7% reduction was not statistically significant. The trial was short and used English-language visits; the tools were used in roughly one-third of eligible visits. These are findings about the tested products and implementation, not Vero. Peer-reviewed trial; full-text methods and participant criteria.

For a clinic, the practical follow-up is to measure review, correction and transfer as well as writing. An apparent improvement in one screen can be offset by work elsewhere. Keep unusable drafts and fallback sessions in the evaluation rather than counting only successful notes.

Inbox drafting: generating a reply did not mean less reply time

Tai-Seale and colleagues’ 2024 study randomized 52 primary care physicians to immediate or delayed access to EHR-integrated reply drafts; 70 additional physicians were contemporaneous controls, not randomized participants. Across 10,679 replies, draft access was associated with 21.8% longer reading time, with no statistically significant change in reply time. The study concerned an early implementation at one academic system and did not establish a patient-outcome benefit. Original message-drafting study.

That distinction matters when a vendor demonstrates how quickly a paragraph appears. A useful draft must also be economical to read and correct. A warm tone is valuable only if the wording remains faithful to the doctor’s decision and the patient’s circumstances.

Diagnostic support: a strong model does not automatically improve its user

In Goh and colleagues’ 2024 randomized trial, 50 physicians worked through clinical vignettes with conventional resources, with or without access to GPT-4. Median diagnostic-reasoning scores were 76% versus 74%; the adjusted difference was 2 percentage points, with a 95% confidence interval from −4 to +8. The improvement was not statistically significant. This was a small simulated-case study, not a trial of patient care or of every later model. Original diagnostic-reasoning trial.

The lesson is to test the doctor-plus-tool workflow, including how conflicting suggestions are handled. A benchmark for a model working alone cannot establish that a busy clinician will notice its omissions, reject its wrong answers or reach a better decision.

A worked example: prepare the visit without deciding the care

AD-01 · Fictional pre-visit example. Organize the supplied information into a source-linked brief for the doctor.

The complete source packet

A plausible brief that changes the meaning

Headaches remain stable. Outside scan normal. Patient declines referral. No urgent concerns. Routine follow-up only.

The draft contains five problems. “Stable” has no current assessment behind it. A normal scan result is invented. The old referral preference overrides the newer request. Urgency has not been assessed. “Routine follow-up only” supplies a decision the doctor has not made.

Merely adding citations would not repair this brief. A link to the June note could make the outdated refusal look authoritative while still misrepresenting the patient’s current preference. Verify both the cited passage and whether it is the right source for the present claim.

The source-aligned preparation brief

TEST-AD-01 · Prepared for the October 9 visit from S1–S5, checked at 08:30. In June, the patient reported occasional headaches and declined referral (S1). On October 8, the patient requested a new discussion and wanted to revisit referral (S2). The patient reports a scan on October 7; its report and result are unavailable in this packet (S2–S3). Records coordinator A owns the open report request; B covers absence (S5). Current assessment, urgency and next steps remain for the doctor at today’s visit (S4–S5).

The preparation questions are: What has changed since June? What does the patient want to discuss now? Is the outside report available from a verified source? What assessment and decisions does the doctor document today? These are questions to resolve, not findings to prefill.

What may happen next—and what must wait

The doctor can use the brief to locate source material and prepare questions. It does not establish a diagnosis, a negative investigation or an acceptable waiting interval. If new information arrives, update the source version and recheck the brief before relying on it.

For this exercise, the permitted destination is a clearly labeled preparation workspace, not a signed encounter note or a patient message. The report request stays open. No new referral, prescription or reassurance message is created. When the clinician later supplies an assessment and plan, those become new source material for a separate draft and review.

Example evaluation record: AD-01; configuration: read-only record summarizer; evidence: S1–S5 and the two briefs above; defects in the sample brief: unsupported result, outdated preference and invented assessment; correction: replace with the source-aligned brief; decision: original draft rejected, corrected brief suitable for preparation only; outstanding work: current clinical assessment and report retrieval.

Evidence lookup example: a real citation with the wrong conclusion

AD-02 · Fictional answers using a real paper. A practice manager asks: Does the Lukac trial justify saying that buying an AI scribe will improve our patients’ health outcomes?

Source packet and exact lookup task

Use the Lukac trial abstract, PMID 41497288 and its linked full text. For this exercise, keep these source anchors beside the answer:

• R1 · Methods: 238 outpatient physicians randomized to DAX Copilot, Nabla or usual care. The primary endpoint concerned writing time in notes.
• R2 · Results: Nabla’s time-in-note reduction was 9.5% versus control; DAX’s 1.7% reduction was not statistically significant.
• R3 · Endpoint boundary: clinician surveys and reported safety/accuracy concerns do not establish improved patient health outcomes. Vero was not a study arm.

The reusable request is: “Using only this paper, answer the manager’s question. Name the tested products and measured endpoint. Attach each conclusion to Methods or Results. Identify what the paper cannot answer, and do not convert a documentation measure into a clinical-outcome measure.” Keep this paper-specific task separate from a broader literature search.

The cited but unsupported answer

AI scribes improve patient outcomes by 9.5%, so the trial supports choosing Vero to deliver better care. Source: Lukac et al., 2025, DOI 10.1056/AIoa2501000.

The DOI is genuine. The conclusion is not supported. Check three claim-to-source links:

1. “Patient outcomes by 9.5%” → R1–R2: the answer has changed the endpoint. Restore the documentation-time measure; do not relabel it as a health benefit.
2. “AI scribes” → R2: a product-specific finding has become a category-wide promise. Preserve the difference between the two study arms.
3. “Choosing Vero” → R3: the answer transfers another product’s result to an untested product. Remove that recommendation from this paper-based answer.

The corrected answer and completed review

No. This trial supports a documentation-time finding for Nabla, not a patient-outcome claim or a Vero recommendation. DAX’s primary time result was not statistically significant. Improved patient health outcomes remain unestablished by this paper. That does not mean the study proves there is no patient benefit; it means the proposed claim needs different evidence. Methods and Results.

Example review record: AD-02; input: manager’s question and R1–R3; source checked: title, DOI and relevant endpoint/result passages; finding: wrong endpoint, overgeneralized product effect and unsupported product recommendation; correction: replace the initial answer with the bounded answer above; decision: suitable for an internal evidence brief, not a patient-benefit marketing claim or a purchase approval. Outstanding work: evidence relevant to the proposed product and patient outcome, plus local implementation evaluation.

To repeat the check, hide R2’s result paragraph while keeping the citation. The answer should identify missing result evidence rather than reproduce the percentage from memory. Then change the question to whether the study proved no patient benefit: the expected answer remains “not established,” not “no benefit.” If the paper is corrected or retracted, reopen the brief and its downstream uses. This tests support for a claim, not whether the answer merely contains a link.

Choose the task and its permissions

Use the planner to turn a broad idea into a task card: source, expected output, reviewer and approval step. Complete configuration, findings and approval fields in your internal workspace using synthetic or nonidentifying test references.

AI FOR DOCTORS — WORKFLOW TEST PLAN
Complete configuration, findings and sign-off in your internal workspace.
Use synthetic or nonidentifying test references.

Task ID: note-drafting
Task: Note drafting
Configuration: [product / model and version / template or prompt / integration / intended setting]
Evaluation date: [YYYY-MM-DD; not yet run]

Required input: An encounter source with speaker attribution, the clinician’s documented assessment and plan, and the approved note template. Start testing with synthetic encounters.
Allowed output: An unsigned note draft grounded in the supplied encounter. Missing facts stay missing; the draft does not create findings, diagnoses, orders or completed actions.
Human review role: The treating clinician responsible for the note checks the draft against the encounter source and corrects it before signing.
Release boundary: Keep the output in draft status until the clinician verifies content, destination chart and signature. No automatic signing or order placement.
Evidence needed: Record the product version, template and capture setup; compare drafts with a clinician-reviewed reference across representative visit types, including failed attempts.
Proposed failure test: Give a synthetic encounter a patient-reported symptom and a test that is only planned. Check whether the draft turns either into an observed finding or a completed test.
Proposed measure: Drafts with at least one unsupported assertion ÷ all drafts reviewed. Record generation failures separately so missing drafts cannot count as error-free.

Local test plan: [cases / inclusion and exclusion rules / reference reviewer / acceptance and stop criteria]
Actual findings: [not evaluated; record numerator, denominator, failures and limitations after testing]
Evidence record: [approved internal reference; no patient details or record identifiers]
Decision: [pending / hold / revise and retest / refer for separate governance review]
Decision rationale: [evidence, unresolved risks and action owner]
Approver: [named accountable role and internal sign-off reference; not yet assigned]
Recheck: [date / owner / triggers such as a model, workflow or population change]

For AD-01, select Pre-visit summary. Compare the brief with its sources, preserve missing information and keep the report request open. The doctor supplies the assessment and next clinical action.

For AD-02, select Evidence lookup. Retain the question, paper version, supporting passages and corrected answer with the internal brief. A useful answer makes its unsupported conclusions visible rather than hiding them behind a citation.

Make human oversight a real work step

“A clinician reviews it” is incomplete unless the workflow gives that clinician the sources, time and ability to reject the output. Before reading a generated summary, identify the essential facts or questions from the original material. Then compare the draft against that list. This helps expose omissions that fluent prose can hide.

WHO’s 2024 guidance identifies false or incomplete outputs, biased performance and automation bias as risks of large multimodal models. A practical response is to test the conditions in which the reviewer could be misled, rather than looking only for obviously absurd answers. WHO guidance.

Use these five checks using AD-01 and controlled variations:

For each attempt, retain the exact input, configuration, raw output, reviewer finding, correction and destination. A missing output is a failed attempt to investigate, not a perfect zero-error response. An unauthorized write or identity mix-up should trigger an immediate pause of the affected workflow and investigation under the clinic’s incident process.

Language and accessibility need their own evaluation. Use qualified language reviewers and clinicians to check meaning, and intended readers to assess usability. Report results by language or tested audience; a good English draft does not establish equivalent performance in another language. In a patient-facing explanation, check that shortening has not removed uncertainty or the action the patient needs to take.

Privacy and regulatory checks before patient data

United States, checked October 9, 2026: HHS explains that a cloud service creating, receiving, maintaining or transmitting ePHI on a covered entity’s behalf is a business associate. The applicable BAA and the organization’s risk analysis are required parts of that arrangement. Encryption, a paid subscription or a vendor’s healthcare label does not replace those steps. Confirm the contracted service, enabled features, permitted uses and responsibilities for access, retention and return of data. HHS cloud-computing guidance.

Ontario, checked October 9, 2026: CPSO’s AI advice says physicians remain accountable for their use of AI, including checking generated content. It advises explaining the use of AI and obtaining consent before recording a conversation. Determine the authority for collecting and sharing personal health information separately; recording consent alone does not settle every vendor data flow. CPSO AI advice and PHI policy. Other jurisdictions and professional roles require their own review.

For a clinical decision-support or device function, record the exact intended purpose, users, patient population and regulatory status. FDA’s January 29, 2026 CDS guidance includes the clinician’s ability to independently review the basis of recommendations among the criteria for the U.S. non-device CDS exclusion. Failure to meet one criterion is not, by itself, a complete regulatory classification. Have the responsible regulatory lead evaluate the specific function. FDA CDS guidance.

Make the privacy check concrete: list what leaves the clinic, where each copy lives, who can access it and what happens on deletion or contract exit. Include audio, transcripts, generated text, attachments, support tickets and downstream EHR copies. Keep public training exercises synthetic. For a deeper product-and-contract example, see the ChatGPT HIPAA workflow checklist.

Run a small pilot and count the whole job

Start with one task and a defined group of users. Run the fixed fictional cases before any approved patient-data pilot, then compare the proposed workflow with the clinic’s current method. Keep the source quality, output requirements and completion point comparable. The digital-scribe pilot worksheet provides a broader documentation study structure.

Record at least these outcomes:

• Quality: unsupported additions, omitted facts, incorrect attribution, outdated information and unresolved defects after review.
• Completion: eligible tasks, attempted tasks, acceptable completed outputs, fallbacks and failed transfers—with reasons for exclusions.
• Effort: source preparation, generation setup, reading, corrections, transfer and rechecking, separated by staff role. Record waiting time separately when it overlaps other work.
• Experience: whether the output helps the doctor prepare or communicate, and whether staff or patients encounter new burden.

A worked budget: review time can change the answer

Consider hypothetical USD inputs: 200 note tasks per month, a $120 subscription, $300 of setup spread across three months, and physician time valued at $120 per hour. Manual completion takes 6 minutes per task. With AI, source preparation takes 1 minute, review and correction 2.5 minutes, and transfer/checking 0.5 minutes: 4 minutes total.

During the three-month setup-amortization period, the direct cost is $220 per month, or $1.10 per task. Saving 2 minutes across 200 tasks releases 400 minutes, valued at $800 under the assumption above. Subtracting $220 gives a $580 estimated monthly capacity value during that period, before additional staff work, failures, taxes or other costs. It is not cash revenue unless the clinic can actually use that capacity.

Now change only review and correction from 2.5 to 4.5 minutes. AI-assisted work totals 6 minutes, the same as manual work. The time benefit disappears while the $220 monthly direct cost remains during the setup-amortization period. That is why a proposed purchase should specify the review workload it assumes. Report actual review time by task complexity rather than treating every encounter as interchangeable.

Choose release conditions before reviewing pilot results: which defects stop expansion, who decides on remediation, what evidence is needed for a retest and which changes trigger a new review. A new model, template, input source, language or integration permission may alter the task even if the product name stays the same.

Where Vero fits

Vero’s documentation, checked October 9, 2026, describes creating an encounter from recording, typed context or uploads, selecting a template, reviewing and editing the generated note, and copying or exporting it. Evaluate this drafting workflow alongside the receiving record’s controls. Vero’s note-creation instructions.

If documentation is your first task, use an approved synthetic encounter to check the whole route: permitted source → template → draft → corrections → saved destination. Confirm the destination contains the reviewed version and that clinical orders or messages have not been implied by note text. Explore the clinical-note drafting workflow for the documented product route; use the AI scribe guide for note-type and output selection.

The practical starting point is a task you can verify, not the most autonomous demonstration. Expand only when the people responsible for the workflow can explain its evidence, detect failures and keep unfinished work visible.

Sources and further reading

• Lukac et al., ambient-scribe randomized trial, 2025
• Tai-Seale et al., EHR-integrated reply drafts, 2024
• Goh et al., physician diagnostic-reasoning trial, 2024
• WHO, governance of large multimodal models, 2024
• HHS, HIPAA and cloud computing
• CPSO, using AI in clinical practice
• FDA, clinical decision-support guidance, January 29, 2026