ReviewESMO real world data and digital oncology2024
Artificial intelligence for cancer screening and surveillance.
Review in ESMO real world data and digital oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Use of Artificial Intelligence in prostate MRI: A rapid scoping review highlighting limited evidence in screening context.European journal of radiology · 2026Article
- What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education.NPJ digital medicine · 2026Article
- Hallmarks of artificial intelligence contributions to precision oncology.Nature cancer · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.