Evidence map›Paper›PMID 33918173›Full record

ReviewCells2021

Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology.

Cesare Lancellotti, Pierandrea Cancian, Victor Savevski, Soumya Rupa Reddy Kotha, Filippo Fraggetta, Paolo Graziano, Luca Di Tommaso

Open access · goldAbstract readReview
In one paragraph

Review in Cells, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
5.7field-weighted citation impact, top 3% of its field
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

28 citing papers in PubMed, 58 citations in OpenAlex.

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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

7 authors at 4 institutions in 1 country.

Cesare LancellottiDepartment of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Milan, Italy.ORCID 0000-0001-6465-5995
Pierandrea CancianArtificial Intelligence Center IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy.
Victor SavevskiArtificial Intelligence Center IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy.ORCID 0000-0002-3376-8740
Soumya Rupa Reddy KothaDepartment of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Milan, Italy.
Filippo FraggettaDepartment of Pathology, Cannizzaro Hospital, 95021 Catania, Italy.
Paolo GrazianoPathology Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, FG, Italy.ORCID 0000-0001-7066-7896
Luca Di TommasoDepartment of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Milan, Italy.ORCID 0000-0002-9013-4728
Humanitas University · ITIRCCS Humanitas Research Hospital · ITCasa Sollievo della Sofferenza · ITOspedale Cannizzaro · IT

Funding

Fondazione Humanitas per la Ricerca
6 · The paper itself

Abstract

Tissue Biomarkers are information written in the tissue and used in Pathology to recognize specific subsets of patients with diagnostic, prognostic or predictive purposes, thus representing the key elements of Personalized Medicine. The advent of Artificial Intelligence (AI) promises to further reinforce the role of Pathology in the scenario of Personalized Medicine: AI-based devices are expected to standardize the evaluation of tissue biomarkers and also to discover novel information, which would otherwise be ignored by human review, and use them to make specific predictions. In this review we will present how AI has been used to support Tissue Biomarkers evaluation in the specific field of Pathology, give an insight to the intriguing field of AI-based biomarkers and discuss possible advantages, risk and perspectives for Pathology.

Indexed as

Artificial IntelligencePathologyAnimalsBiomarkersHumansOrgan SpecificityRiskSoftwareBiomarkersartificial intelligencebiomarkerpathologypersonalized medicine

Identifiers

PMID33918173
PMCPMC8066881
OpenAlexW3144167624

What OpenQuestion 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 OpenQuestion graph.