ReviewHealth economics review2026
Health economics evaluation of artificial intelligence in the field of oncology: a scoping review.
Review in Health economics review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled 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.
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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Economic, ethical, and regulatory dimensions of artificial intelligence in healthcare: an integrative review.Frontiers in public health · 2025Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe ageing and growing population, rising cancer rate, and increased healthcare demand drive higher medical costs. Integrating Artificial Intelligence (AI) into oncology revolutionizes cancer diagnosis, treatment, and management, promising enhanced efficiency and precision. Despite potential for healthcare cost savings from implementing AI system in healthcare, its successful integration depends on stakeholder acceptance, particularly physician adoption and integration into routine clinical practice.
objectivesThis scoping review aimed to systematically assess recent literature on health economic evaluations (HEEs) of artificial AI applications in oncology and explore the economic risks and benefits of integrating AI systems into oncology. METHODOLOGY: A scoping review was conducted using PRISMA-ScR guidelines across databases, including PubMed, Scopus, and Google Scholar for articles published between January 1, 2019, and June 30, 2024, in English. Eligible studies focused on the economic aspects of AI applications in oncology, including cost-effectiveness analyses, budget impact studies, and evaluations of economic benefits in clinical practice. Two independent reviewers used CHEERS-AI and Philips checklists for data extraction, quality assessment and analysis.
resultsOut of 870 studies identified,12 studies were selected which focused on colorectal (5), breast (2), lung (2), cervical (1), and prostate (1) cancers. Most emphasis is on early-stage care (N = 10,83%) and cost-effectiveness analysis (N = 9,75%) was predominant along with Markov model being the most common approach. The studies used a healthcare system perspective (N = 6,50%) and examined AI's cost-effectiveness in medical imaging (N = 5,42%) and biomarkers (N = 4,33%). Time horizons varied from less than a year to a lifetime, with most applying a 3% discount rate. AI demonstrated economic benefits, improved diagnostic sensitivity,potential cost reduction, workflow efficiency, and treatment optimization while presenting risks like reimbursement challenges, data security concerns, and potential error costs.
conclusionThe scoping review highlights the necessity for thorough health economic evaluations of AI integration in oncology. Although AI technologies are cost-effective, there is a gap in user perspectives and considerations of health equity, regulation, and ethics. To maximize its benefits, future research should include a comprehensive economic evaluation of a more diverse population for the effective adoption of AI into the healthcare system.
Indexed as
Identifiers
What Socratic 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 Socratic graph.