Evidence map›Paper›PMID 42270078›Full record

ArticleAnnals of oncology : official journal of the European Society for Medical Oncology2026

Prompt-directed ambient artificial intelligence for automated multidisciplinary tumor board documentation.

R Philips, A Mahajan, L Estephan, S Auger, W Haque, D Palmer, J McQueen, E MacCracken, P P Argyris, A Trzcinska and 18 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of oncology : official journal of the European Society for Medical Oncology, 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

28 authors.

R PhilipsDepartment of Surgery, Section of Otolaryngology-Head & Neck Surgery, University of Chicago, Chicago, USA; Department of Otolaryngology-Head & Neck Surgery, Thomas Jefferson University Hospital, Philadelphia, USA.
A MahajanDepartment of Surgery, Section of Otolaryngology-Head & Neck Surgery, University of Chicago, Chicago, USA.
L EstephanDepartment of Otolaryngology-Head & Neck Surgery, Thomas Jefferson University Hospital, Philadelphia, USA.
S AugerDepartment of Surgery, Section of Otolaryngology-Head & Neck Surgery, University of Chicago, Chicago, USA.
W HaqueDepartment of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, USA.
D PalmerDepartment of Surgery, Section of Speech and Swallowing, University of Chicago, Chicago, USA.
J McQueenDepartment of Surgery, Section of Speech and Swallowing, University of Chicago, Chicago, USA.
E MacCrackenDepartment of Surgery, Section of Speech and Swallowing, University of Chicago, Chicago, USA.
P P ArgyrisDepartment of Pathology, University of Chicago, Chicago, USA.
A TrzcinskaDepartment of Pathology, University of Chicago, Chicago, USA.
N A CiprianiDepartment of Pathology, University of Chicago, Chicago, USA.
M LingenDepartment of Pathology, University of Chicago, Chicago, USA.
I HussainDepartment of Radiology, University of Chicago, Chicago, USA.
M MuzaffarDepartment of Radiology, University of Chicago, Chicago, USA.
O Pasternak-WiseDepartment of Radiology, University of Chicago, Chicago, USA.
J CurryDepartment of Otolaryngology-Head & Neck Surgery, Thomas Jefferson University Hospital, Philadelphia, USA.
R KatipallyDepartment of Radiation Oncology, University of Chicago, Chicago, USA.
A JulooriDepartment of Radiation Oncology, University of Chicago, Chicago, USA.
D HarafDepartment of Radiation Oncology, University of Chicago, Chicago, USA.
P HeinlenDepartment of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, USA.
N J ChoudhuryDepartment of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, USA.
A RosenbergDepartment of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, USA.
E VokesDepartment of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, USA.
H ArshadDepartment of Surgery, Section of Otolaryngology-Head & Neck Surgery, University of Chicago, Chicago, USA.
E BlairDepartment of Surgery, Section of Otolaryngology-Head & Neck Surgery, University of Chicago, Chicago, USA.
N AgrawalDepartment of Surgery, Section of Otolaryngology-Head & Neck Surgery, University of Chicago, Chicago, USA.
S ShahDepartment of Medicine, Section of General Internal Medicine, University of Chicago, Chicago, USA.
A T PearsonDepartment of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, USA; Chan Zuckerberg Biohub Chicago LLC, Chicago, USA. Electronic address: alexander.pearson@bsd.uchicago.edu.

Funding

PROSPECT: Premalignant Oral Lesions Pathology and Epigenetic Risk Prediction ToolR01CA293995 · NCI · LOMA LINDA UNIVERSITY · PI AOUIZERAT, BRADLEY E, PEARSON, ALEXANDER THOMAS · 2024 to 2025
$1.4M
NCI NIH HHS R01 CA293995
6 · The paper itself

Abstract

backgroundTumor board documentation is challenging due to complexity of multidisciplinary input. Ambient artificial intelligence (AI) for medical dictation uses generative methods to summarize voice data. Strategic prompting can improve model output. We evaluated prompt-directed ambient AI for automated tumor board documentation and compared ambient AI with manual documentation. PATIENTS AND

methodsWe carried out a prospective quality improvement study evaluating ambient AI for documentation of multidisciplinary head and neck cancer tumor board discussions at a tertiary institution in 2025. The primary intervention was verbal prompting during case discussions to improve documentation quality. Primary cohorts were unprompted and prompt-directed ambient AI tumor boards. A paired comparator subanalysis included manual and ambient AI documentation from the same meetings. We validated our prompted ambient AI workflow with additional tumor boards. Documentation was evaluated using a modified Metric for Observation of Decision Making (MODe)-based rubric across five domains: demographics, tumor characteristics, radiology findings, pathology findings, and discussion, with criteria rated on a 1-3 scale (1 = incorrect/omitted, 2 = partial, 3 = complete documentation).

resultsWe analyzed results of 12 tumor boards across four cohorts: unprompted ambient AI, prompt-directed ambient AI, manual documentation, and internal validation. In the primary cohorts, prompt-directed ambient AI improved tumor characteristics domain score (q = 0.042) and total score (P = 0.005) versus unprompted ambient AI, with no difference in per-patient discussion time (P = 0.158). In the paired comparator analysis, ambient AI improved total score (P < 0.001) and discussion domain score (q < 0.001) versus manual documentation, while reducing administrative time per patient (P < 0.001). The internal validation cohort showed statistically comparable scores across all domains.

conclusionsDirected prompting during head and neck tumor board improved generated summaries, without increasing discussion times. Compared with manual documentation, ambient AI improved documentation quality, while diminishing administrative burden.

Indexed as

Artificial IntelligenceDocumentationHead and Neck NeoplasmsAgedFemaleHumansMaleMiddle AgedProspective StudiesQuality Improvementambient intelligenceartificial intelligencedocumentationhead necktumor board

Identifiers

PMID42270078
PMCPMC13557418

What OpenQuestion holds

Textmetadata
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.