Evidence map›Paper›PMID 41899575›Full record

ReviewCancers2026

Artificial Intelligence in

Andreas Koulouris, Christos Tsagkaris, Konstantinos Kalaitzidis, Georgios Tsakonas, Giannis Mountzios

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancers, 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

5 authors.

Andreas KoulourisThoracic Oncology Center, Karolinska University Hospital, 171 76 Stockholm, Sweden.ORCID 0000-0003-2334-444X
Christos TsagkarisFaculty of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.ORCID 0000-0002-4250-574X
Konstantinos KalaitzidisScience for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 21 Solna, Sweden.
Georgios TsakonasThoracic Oncology Center, Karolinska University Hospital, 171 76 Stockholm, Sweden.ORCID 0000-0003-4397-7391
Giannis MountziosFourth Department of Medical Oncology and Clinical Trials Unit, Henry Dunant Hospital Center, 11526 Athens, Greece.ORCID 0000-0002-7780-7836

Funding

Elena Iliopoulou Giama (EIG) Cancer Research & Scholarship Foundation N/AEuropean Society for Medical Oncology N/AHellenic Society of Medical Oncology (HeSMO) N/AOnassis Scholarship 2025-2026 N/ARadium Hemmets Research Funds N/AUniversity of Crete N/A
6 · The paper itself

Abstract

BACKGROUND/

objectivesThe management and prognosis of

methodsA systematic search was conducted for peer-reviewed studies published between 2020 and 2025. Eligible studies involved human subjects and applied AI, machine learning, or deep learning methods to predict

resultsThirteen studies met the inclusion criteria, most of which were retrospective and single-center. AI approaches were applied to radiologic, pathologic, molecular, or multimodal data. Models predicting

conclusionsAI shows promising potential to support diagnosis, prognostication, and treatment assessment in

Indexed as

ALK rearrangementartificial intelligencedigital pathologymachine learningNSCLCprognostic modelingradiomicsresistance mechanismssystematic reviewtreatment response

Identifiers

PMID41899575
PMCPMC13025099

What OpenQuestion holds

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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.