Annals of oncology : official journal of the European Society for Medical Oncology
June 10, 2026 · via Europe PMC
Prompt-directed ambient artificial intelligence for automated multidisciplinary tumor board documentation.
Philips R, Mahajan A, Estephan L, +24 more, Pearson AT.
AI summary — for doctors, generated from the abstract only
Verbal prompting during head and neck tumor boards improved the quality of ambient AI-generated documentation without lengthening discussions, and ambient AI outperformed manual documentation.
What was studied
A prospective quality improvement study at a single tertiary institution analyzing 12 head and neck tumor boards across unprompted ambient AI, prompt-directed ambient AI, manual documentation and validation cohorts, scored with a modified MODe rubric.
Key findings
- Prompt-directed AI improved tumor characteristics score (q=0.042) and total score (P=0.005) vs unprompted AI
- No difference in per-patient discussion time (P=0.158)
- Ambient AI improved total documentation score vs manual (specific statistic not fully captured in the abstract)
Clinical relevance
This small single-center study suggests structured prompting can improve AI-generated tumor board summaries, though broader validation is needed before adoption.
Who should care
Limitations
Small number of tumor boards at one institution; quality improvement design without randomization.
Saying the right cues aloud helps ambient AI write better tumor board notes.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Background
Tumor 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 methods
We 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).
Results
We 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.
Conclusions
Directed 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.
DOI
10.1016/j.annonc.2026.05.709
PMID
42270078
PMCID
PMC13557418
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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