Evidence map›Paper›PMID 42192380›Full record

ArticleBMC public health2026

Artificial intelligence in population-based cancer screening: evidence from a national survey of Italian professionals.

Giovanni Fattore, Mattia Benato Cortecci, Renato Fasoli, Tiziano Maggino, Paola Mantellini, Silvia Deandrea

Abstract read
In one paragraph

Article in BMC public health, 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

6 authors.

Giovanni FattoreDepartment of Social and Political Sciences, Bocconi University, Via Röntgen n. 1, Milan, 20136, Italy. giovanni.fattore@unibocconi.it.ORCID http://orcid.org/0000-0001-6545-2110
Mattia Benato CortecciDepartment of Social and Political Sciences, Bocconi University, Via Röntgen n. 1, Milan, 20136, Italy.ORCID http://orcid.org/0009-0004-2845-2049
Renato FasoliDepartment of Gastroenterology and Digestive Endoscopy, Santa Croce e Carle Hospital, Cuneo, Italy.
Tiziano MagginoMultidisciplinary Oncological Group, President, University Hospital of Padua, GISCi, Padua, Italy.ORCID http://orcid.org/0000-0002-5177-8205
Paola MantelliniInstitute for Cancer Research and Prevention and the Oncology Network, Florence, Tuscany, Italy.
Silvia DeandreaPrevention Department, Pavia Health Protection Agency, Viale Indipendenza 3, Pavia, 27100, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial Intelligence (AI) is increasingly proposed to enhance population-based cancer screening. While several applications are under evaluation, there is limited evidence on professional and organisational perspectives regarding their value, risks, and implementation challenges.

methodsWe conducted a web-based survey targeting coordinators and reference professionals of Italian cancer screening programmes. The survey comprised 39 questions and explored prior experience with AI, perceived benefits and risks, organisational and legal barriers, and willingness to invest in AI applications. Recruitment combined a cascade procedure through regional and sub-regional coordinators with distribution via the mailing list of the Federation of Oncology Screening Associations (FASO). Descriptive analyses summarised responses, while multiple linear regression assessed determinants of willingness to invest in AI applications.

resultsA total of 305 professionals responded, representing all but one Italian region. About one-third reported AI experience, most frequently in colorectal polyp detection and breast imaging support. Overall, 74.8% expressed a strong interest in adopting AI applications, particularly for risk prediction and diagnostic support. The most valued benefits were improved diagnostic accuracy, workflow optimisation, and shorter reporting times. Concerns focused on legal liability (72.8%), lack of transparency and algorithmic bias (67.2%), and professional de-skilling (46.2%). Organisational barriers included inadequate IT infrastructure (77.0%), absence of guidelines (75.7%), and limited financial resources (77.4%). Regression confirmed that higher perceived benefits were positively associated with willingness to invest in AI applications, whereas risks were negatively associated.

conclusionsItalian screening professionals generally support the adoption of AI but emphasise the need for robust evidence, clear regulatory frameworks, and sustained investment in digital infrastructure to ensure its safe and effective implementation.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelEarly Detection of CancerAdultFemaleHumansItalyMaleMiddle AgedSurveys and QuestionnairesArtificial IntelligenceCancer ScreeningHealth PolicyItalySurveys and Questionnaires

Identifiers

PMID42192380
PMCPMC13386902

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.