Evidence map›Paper›PMID 42005711›Full record

ArticleClinical and translational radiation oncology2026

Artificial intelligence (AI) for early identification of radiotherapy related toxicities from the electronic health records of patients with head and neck cancer.

Anne Liu, Faisal Alfadli, John Mohan Mathew, Samantha Parmelee, John Waldron, Jennifer Y Y Kwan, Steven Aviv, Michelle Ml Nguyen, Viktoriia Mokriak, Christopher Pettengell and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical and translational radiation 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

11 authors.

Anne LiuRadiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Faisal AlfadliRadiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
John Mohan MathewRadiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Samantha ParmeleeUniversity of Toronto, Temerty Faculty of Medicine, Toronto, Ontario, Canada.
John WaldronRadiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Jennifer Y Y KwanRadiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Steven AvivPentavere Research Group Inc., Toronto, ON, Canada.
Michelle Ml NguyenPentavere Research Group Inc., Toronto, ON, Canada.
Viktoriia MokriakPentavere Research Group Inc., Toronto, ON, Canada.
Christopher PettengellPentavere Research Group Inc., Toronto, ON, Canada.
Philip WongRadiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We evaluated artificial intelligence (AI) for detecting osteoradionecrosis, fibrosis, trismus, and dysphagia in 207 head and neck cancer patient electronic health records. After adjudication and fine-tuning, accuracy reached 87% (F1 = 0.92). The model processed 20,835 sentences within seconds, demonstrating feasibility and efficiency for automating the identification of radiation-related late toxicities.

Indexed as

Artificial intelligenceElectronic health recordsHead and neck cancerNatural language processingRadiotherapySide effects

Identifiers

PMID42005711
PMCPMC13091453

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.