Revolutionizing personalized medicine using artificial intelligence: a meta-analysis of predictive diagnostics and their impacts on drug development
Yazarlar (8)
Amin Daemi
Çukurova Üniversitesi Tip Fakültesi, Türkiye
Çukurova Üniversitesi Tip Fakültesi, Türkiye
Sahar Kalami
Islamic Azad University, Marvdasht Branch
Islamic Azad University, Marvdasht Branch
Ruhiyya Guliyeva Tahiraga
Institute Of Biophysics Ministry Of Science And Education Republic Of Azerbaijan, Azerbaycan
Institute Of Biophysics Ministry Of Science And Education Republic Of Azerbaijan, Azerbaycan
Omid Ghanbarpour
Sbums School Of Medicine
Sbums School Of Medicine
Mohammad Reza Rahimi Barghani
Gulf Medical University, Birleşik Arap Emirlikleri
Gulf Medical University, Birleşik Arap Emirlikleri
Mohammad Hosseini Hooshiar
School Of Dentistry
School Of Dentistry
Doç. Dr. Gülüzar ÖZBOLAT Sinop Üniversitesi, Türkiye
Prof. Dr. Zafer Yönden Çukurova Üniversitesi Tip Fakültesi, Türkiye
| Makale Türü |
Diğer (Teknik, not, yorum, vaka takdimi, editöre mektup, özet, kitap krıtiği,
araştırma notu, bilirkişi raporu ve benzeri)
(SCI, SSCI, AHCI, SCI-Exp dergilerinde yayınlanan teknik not,
editöre mektup, tartışma, vaka takdimi ve özet türünden makale)
|
||
| Dergi Adı | Clinical and Experimental Medicine (Q2) | ||
| Dergi ISSN | 1591-8890 Dergi Bilgileri (2025) | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2025 |
| Kabul Tarihi | 07-05-2025 | Yayınlanma Tarihi | 18-07-2025 |
| Cilt / Sayı / Sayfa | 25 / 1 / – | DOI | 10.1007/s10238-025-01723-x |
| Makale Linki | https://link.springer.com/content/pdf/10.1007/s10238-025-01723-x.pdf | ||
| UAK Araştırma Alanları |
Tıbbi Biyokimya
|
||
| Özet |
| 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 (I2 = 91.01%), attributed to differences in model architecture, diagnostic domains, and data quality. Subgroup analyses showed that convolutional neural networks and random forest models achieved higher AUC values, while domains like endocrinology demonstrated greater performance variability. Funnel plot inspection and sensitivity analysis indicated the presence of publication bias. AI shows strong potential to enhance diagnostic accuracy in personalized laboratory medicine. Nonetheless, methodological heterogeneity and publication bias remain significant challenges. Future research should prioritize standardized evaluation frameworks, transparency, and the development of explainable AI systems to ensure responsible clinical integration. |
| Anahtar Kelimeler |
| Artificial intelligence | Diagnostic accuracy | Explainable AI | Personalized laboratory medicine | Subgroup analysis |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları |