Artificial intelligence for cancer screening and surveillance
Open accessAbstract
Investing in cancer prevention can be cost-effective. However, this requires significant changes both inside and outside the health care system. The core of the preventive strategy is the assignment of an individual risk level of developing cancer. Artificial intelligence (AI), which has emerged as a tool to reduce errors and confusion in data collection and analysis, has helped accelerate recent advances in identifying circulating markers to generate predictive methods. With predictive models applied to increasingly less invasive and repeatable analytic tests, the risk is no longer assigned but profiled directly on the individual over time. On this basis, the probability of early cancer diagnosis is increased and at the same time, proactive preventive medicine transits from offering lifestyle recommendations to guiding specific treatments to reduce the risk. Despite these promises, AI-based predictive models also present challenges in clinical implementation. Addressing these challenges is crucial to minimizing the future burdens associated with fighting cancer.
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Models that may perform well during development can degrade in real-world settings due to dataset drift across diverse sites, devices, and populations.
“Models that may perform well during development can degrade in real-world settings due to dataset drift across diverse sites, devices, and populations15–18.”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [17]
The Secondary and tertiary cancer prevention section (page unavailable) says that applying retrospective findings in clinical practice depends on representative data and recommends validation using datasets from multiple sites. This supports the concern about real-world performance, but the paper does not specifically attribute performance degradation to dataset drift across devices and populations. (LLM, conf=0.83)
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- Authors
- Gentile, F., & Malara, N.
- Affiliations
- Magna Graecia University (IT)
- Corresponding author
- Malara, N.
- Venue
- ESMO Real World Data and Digital Oncology
- Publisher
- Elsevier BV
- Published
- 2024-09
- Volume
- 5
- Pages
- 100046
- ISSN
- 2949-8201 (ISSN-L)
- PMC
- PMC12836662
- OpenAlex
- W4400494189
- Cited by
- 13
- References
- 26
- Citations by year
- 2026: 6 · 2025: 7
- FWCI
- 1.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- ESMO Real World Data and Digital Oncology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, cancer screening, cancer surveillance, cancer prevention, prediction model, circulating biomarkers, early cancer detection, clinical implementation barriers, cost-effectiveness
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