Evidence map›Paper›PMID 41053747›Full record

ReviewCost effectiveness and resource allocation : C/E2025

Artificial intelligence applications in health insurances: a scoping review.

Maryam Ramezani-A, Ahad Bakhtiari, Mohammadreza Mobinizadeh, Rajabali Daroudi, Hamid R Rabiee, Alireza Olyaeemanesh, Ali Akbar Fazaeli, Hakimeh Mostafavi, Maryam Ramezani-B, Saharnaz Sazgarnejad and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cost effectiveness and resource allocation : C/E, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

12 authors.

Maryam Ramezani-ADepartment of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
Ahad BakhtiariDepartment of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
Mohammadreza MobinizadehNational Institute for Health Research, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
Rajabali DaroudiDepartment of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
Hamid R RabieeDepartment of Computer Engineering, Sharif University of Technology, Tehran, Iran.
Alireza OlyaeemaneshIranian Research Network for Social Determinants of Health (IRNSDH), Tehran, Iran.
Ali Akbar FazaeliDepartment of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
Hakimeh MostafaviHealth Equity Research Centre (HERC), Tehran University of Medical Sciences (TUMS), Tehran, Iran.
Maryam Ramezani-BDepartment of Computer Engineering, Sharif University of Technology, Tehran, Iran.
Saharnaz SazgarnejadSchool of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
Sanaz BordbarSchool of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
Amirhossein TakianDepartment of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences (TUMS), Tehran, Iran. takian@tums.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe rapid evolution of technology has reshaped the insurance industry, with artificial intelligence (AI) taking center stage as a key driver of innovation. This paper examines the transformative impact of AI in health insurance, focusing on its applications and potential to revolutionize the sector.

methodThis scoping review examines literature published between 2000 and 2024, focusing on the application of AI in health insurance. We used relevant keywords related to artificial intelligence and health insurance to search the PubMed, Scopus, and Web of Science databases.

findingsAI presents numerous opportunities in health insurance, including contributions to shaping international and national agendas, such as aligning goals, establishing indicators, and achieving objectives, financial management, fraud detection, monitoring capabilities, diagnostics and medical innovations, private insurance applications, risk management, technical analysis, and value creation. However, there are ethical challenges that must be addressed if AI is to be effectively implemented.

conclusionPolicies for AI applications in health insurance should prioritize the protection of personal health and medical data, address ethical concerns, and ensure robust data privacy and security. Additionally, these policies should promote the use of AI to enhance customer experiences, optimize risk selection, and generate revenue for both insurers and policyholders.

Indexed as

ApplicationsArtificial intelligenceHealth insurance

Identifiers

PMID41053747
PMCPMC12502125

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.