Evidence map›Paper›PMID 41041822›Full record

ReviewThe Laryngoscope2026

Roadmap for Representative Artificial Intelligence Models for Thyroid Cancer.

Rashi Ramchandani, Eddie Guo, Sanaz G Biglou, Sami G Sabbah, Michael Mostowy, Donya Mahiny, Christian Hurtubise, Gift Anicho-Okereke, Risa Shorr, Lisa Caulley and 5 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The Laryngoscope, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

15 authors.

Rashi RamchandaniFaculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.ORCID 0000-0002-6144-6423
Eddie GuoCumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Sanaz G BiglouFaculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Sami G SabbahFaculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Michael MostowyOtolaryngology, University of Buffalo Jacobs School of Medicine, Buffalo, New York, USA.
Donya MahinyFaculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Christian HurtubiseFaculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Gift Anicho-OkerekeFaculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Risa ShorrLearning Services, The Ottawa Hospital, Ottawa, Ontario, Canada.
Lisa CaulleyThe Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
Evan J PropstDepartment Otolaryngology-Head and Neck Surgery, Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.
Nikolaus E WolterDepartment Otolaryngology-Head and Neck Surgery, Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-5196-6150
Jonathan D WassermanDivision of Endocrinology, Department of Pediatrics, Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0001-7088-8146
Antoine EskanderInstitute of Health Policy and Medical Education (IHPME), University of Toronto, Toronto, Ontario, Canada.
Jennifer M SiuDepartment Otolaryngology-Head and Neck Surgery, Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.ORCID 0009-0007-1040-725X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) models in thyroid cancer are often not generalizable due to biased or non-representative training and validation datasets. We propose a four-stage roadmap emphasizing representative data collection, inclusion of contextual variables, robust training/validation design, and post-deployment monitoring to improve fairness and clinical utility.

Indexed as

Artificial IntelligenceThyroid NeoplasmsHumansartificial intelligencedata representationequitymodel trainingthyroid cancer

Identifiers

PMID41041822
PMCPMC12793952

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.