Evidence map›Paper›PMID 40421957›Full record

SynthesisThe oncologist2025

Concordance with SPIRIT-AI guidelines in reporting of randomized controlled trial protocols investigating artificial intelligence in oncology: a systematic review.

David Chen, Emily He, Keiran Pace, Matthew Chekay, Srinivas Raman

Abstract readSystematic Review
In one paragraph

Synthesis in The oncologist, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
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

5 authors.

David ChenPrincess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Ontario, Canada M5G 2C4.
Emily HeTemerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada M5S 3K3.
Keiran PaceTemerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada M5S 3K3.
Matthew ChekayTemerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada M5S 3K3.
Srinivas RamanPrincess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Ontario, Canada M5G 2C4.ORCID 0000-0001-5688-9628

Funding

CARO CROF StudentshipRobert L. Tundermann and Christine E. Couturier Philanthropic Funds
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is a promising tool used in oncology that may be able to facilitate diagnosis, treatment planning, and patient management. Transparency and completeness of protocols of randomized controlled trials (RCT) involving AI interventions is necessary to ensure reproducibility of AI tools across diverse clinical settings. The SPIRIT 2013 and SPIRIT-AI 2020 guidelines were developed as evidence-based recommendations for complete reporting of trial protocols. However, the concordance of AI RCT protocols in oncology to SPIRIT reporting guidelines remains unknown. This systematic review evaluates the concordance of protocols of RCTs evaluating AI interventions in oncology to the SPIRIT 2013 and SPIRIT-AI 2020 reporting guidelines.

methodsA systematic search of Ovid Medline and Embase was conducted on October 22, 2024 for primary, peer-reviewed RCT protocols involving AI interventions in oncology. Eligible studies were screened in duplicate and data extraction assessed concordance to SPIRIT 2013 and SPIRIT-AI 2020 guideline items. Item-specific concordance was measured as the proportion of studies that reported the item. Average concordance was measured as the median proportion of items reported for each study.

resultsTwelve RCT protocols met the inclusion criteria. The median concordance to SPIRIT 2013 guidelines was 81.92% (IQR 74.88-88.95) and the median concordance to SPIRIT-AI 2020 guidelines was 78.21% (IQR 67.21-89.20). For SPIRIT 2013 guidelines, high concordance was observed for items related to study objectives and ethics, but gaps were identified in reporting blinding procedures, participant retention, and post-trial care. For SPIRIT-AI 2020 guidelines, there remained gaps based on data quality management, performance error analysis, and accessibility of AI intervention code.

conclusionWhile concordance to reporting guidelines in oncology AI RCT protocols was moderately high, critical gaps in protocol reporting persist that may hinder reproducibility and clinical implementation. Future efforts should focus on increasing awareness and reinforcement to enhance reporting quality necessary to foster the responsible integration of AI into oncology practice.

Indexed as

Artificial IntelligenceMedical OncologyNeoplasmsRandomized Controlled Trials as TopicHumansPractice Guidelines as TopicAIartificial intelligenceprotocolrandomized controlled trialRCTSPIRITSPIRIT-AI

Identifiers

PMID40421957
PMCPMC12107541

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.