Evidence map›Paper›PMID 42317592›Full record

ArticlePlastic and reconstructive surgery. Global open2026

Charting Tomorrow: A Practical Review of Artificial Intelligence in Medical Documentation.

Ferris Zeitouni, Nada E Botros, Alisha S Khosla, Marion McKinney, Jasmine N Craig, Luis Alamo-Irizarry, Venkat K Rao

Abstract read
In one paragraph

Article in Plastic and reconstructive surgery. Global open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ferris ZeitouniFrom the Division of Plastic and Reconstructive Surgery, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI.
Nada E BotrosFrom the Division of Plastic and Reconstructive Surgery, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI.
Alisha S KhoslaFrom the Division of Plastic and Reconstructive Surgery, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI.
Marion McKinneyFrom the Division of Plastic and Reconstructive Surgery, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI.
Jasmine N CraigFrom the Division of Plastic and Reconstructive Surgery, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI.
Luis Alamo-IrizarryFrom the Division of Plastic and Reconstructive Surgery, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI.
Venkat K RaoFrom the Division of Plastic and Reconstructive Surgery, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rising physician burnout, driven by increased administrative load and documentation burden, has led to the widespread adoption of artificial intelligence (AI)-powered transcription services in medical practices. These tools generate clinical notes from patient encounters and have shown the ability to significantly reduce documentation burden and streamline workflows. This article provides clinicians with a primer on the capabilities, limitations, and practical considerations for implementing AI transcription services in their practice. Across platforms, core features include encounter transcription, automated note generation, multilingual capabilities, custom templates, and Health Insurance Portability and Accountability Act compliance. Platforms with deep integration into electronic health records offer greater workflow automation, such as summarizing prior notes, drafting referral letters and after-visit summaries, and preparing orders, but require significant training and onboarding. More user-friendly options provide rapid note creation with minimal setup, though with limited electronic health record integration. Industry partnerships have facilitated the current development of novel features, including point-of-care prior authorization, a virtual assistant to schedule visits, answer patient questions related to appointments, and suggest relevant screenings. Physicians should evaluate the advantages and logistical challenges of implementing AI transcription tools while also considering liability. Each platform offers unique benefits and limitations, and although these tools may enhance physician workflow, they also present risks for both patients and providers.

Identifiers

PMID42317592
PMCPMC13275152

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.