Evidence map›Paper›PMID 40679640›Full record

SynthesisClinical and experimental medicine2025

Revolutionizing personalized medicine using artificial intelligence: a meta-analysis of predictive diagnostics and their impacts on drug development.

Amin Daemi, Sahar Kalami, Ruhiyya Guliyeva Tahiraga, Omid Ghanbarpour, Mohammad Reza Rahimi Barghani, Mohammad Hosseini Hooshiar, Gülüzar Özbolat, Zafer Yönden

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Clinical and experimental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

8 authors.

Amin DaemiDepartment of Medical Biochemistry, Faculty of Medicine, Cukurova University, Adana, Turkey. phd_bio@yahoo.com.
Sahar KalamiDepartment of Biology, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran.
Ruhiyya Guliyeva TahiragaInstitute of Biophysics, Ministry of Science and Education, Baku, Republic of Azerbaijan.
Omid GhanbarpourFaculty of Medicine, Shahid Beheshti University of Medical Sciences, Tajrish Hospital, Tehran, Iran.
Mohammad Reza Rahimi BarghaniCollege of Medicine, Gulf Medical University, Ajman, UAE.
Mohammad Hosseini HooshiarDepartment of Periodontics, School of Dentistry, Tehran University of Medical Sciences, Tehran, Iran.
Gülüzar ÖzbolatFaculty of Health Science, Sinop University, Sinop, Turkey.
Zafer YöndenDepartment of Medical Biochemistry, Faculty of Medicine, Cukurova University, Adana, Turkey. zyonden@cu.edu.tr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is transforming the landscape of laboratory medicine by enhancing diagnostic accuracy and enabling more personalized care. Given its growing use in clinical settings, evaluating the performance of AI models in diagnostic tasks is essential to inform evidence-based implementation strategies. This meta-analysis systematically assessed the diagnostic effectiveness of AI-based models. A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, and IEEE Xplore using predefined keywords related to AI and diagnostic accuracy. From 430 retrieved studies, 17 met the inclusion criteria. Data extracted included study design, AI model type, input modality, and performance metrics such as sensitivity, specificity, and area under the curve (AUC). Random-effects meta-analysis and subgroup analyses were performed to investigate heterogeneity and model-specific trends. The pooled analysis yielded a high combined AUC of 0.9025, indicating strong diagnostic capability of AI models. However, substantial heterogeneity was detected (I

Indexed as

Artificial IntelligenceDrug DevelopmentPrecision MedicineHumansArtificial intelligenceDiagnostic accuracyExplainable AIPersonalized laboratory medicineSubgroup analysis

Identifiers

PMID40679640
PMCPMC12274247

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.