From image biomarkers to treatment decisions: multimodal AI and clinical translation in gynecologic oncology
- 1Department of Medicine, College of Medicine, Alfaisal University , ,
- 2Department of Biostatistics, Epidemiology and Public Health, College of Medicine, Alfaisal University , ,
- 3Department of Anatomy, College of Medicine, Alfaisal University , ,
Abstract
Artificial intelligence (AI) is rapidly expanding across gynecologic oncology, but most applications remain focused on image interpretation and retrospective prediction rather than direct support of treatment decisions. This review examines how AI is progressing from imaging biomarkers toward multimodal decision-support systems that integrate radiologic, pathologic, molecular, and clinical information. We summarize current applications in ovarian, endometrial, and cervical cancers, with particular attention to preoperative risk stratification, surgical planning, radiotherapy, systemic treatment selection, treatment-response prediction, and multidisciplinary decision-making. Multimodal approaches are especially promising because they reflect the way clinical decisions are made and may provide greater value than models based on a single data source. However, the current evidence remains dominated by retrospective and often single-center studies, with limited external and prospective validation. Data heterogeneity, domain shift, incomplete multimodal datasets, bias, workflow integration, regulatory requirements, and model monitoring remain important barriers to routine implementation. We therefore outline a stepwise pathway for clinical translation, from robust multicenter validation and prospective human–AI evaluation to clinical-impact studies and post-deployment monitoring. The next phase of AI in gynecologic oncology should prioritize clinically meaningful decisions and measurable patient or workflow benefit rather than incremental gains in predictive performance alone.
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BibTeX
@article{bakir2026from,
title = {{From image biomarkers to treatment decisions: multimodal AI and clinical translation in gynecologic oncology}},
author = {Mohamad Bakir and Mohammad Khaldoun Al Masri and Yousef Mushtaha and Narmeen Shaikh and Mohammed Raddaoui and Khalid Said Mohammad},
journal = {Frontiers in oncology},
year = {2026},
month = oct,
doi = {10.3389/fonc.2026.1960963},
url = {https://europepmc.org/article/PMC/PMC13691476},
note = {CC BY},
}
RIS
TY - JOUR
TI - From image biomarkers to treatment decisions: multimodal AI and clinical translation in gynecologic oncology
AU - Mohamad Bakir
AU - Mohammad Khaldoun Al Masri
AU - Yousef Mushtaha
AU - Narmeen Shaikh
AU - Mohammed Raddaoui
AU - Khalid Said Mohammad
PY - 2026
DA - 2026/10/09
JO - Frontiers in oncology
DO - 10.3389/fonc.2026.1960963
UR - https://europepmc.org/article/PMC/PMC13691476
AB - Artificial intelligence (AI) is rapidly expanding across gynecologic oncology, but most applications remain focused on image interpretation and retrospective prediction rather than direct support of treatment decisions. This review examines how AI is progressing from imaging biomarkers toward multimodal decision-support systems that integrate radiologic, pathologic, molecular, and clinical information. We summarize current applications in ovarian, endometrial, and cervical cancers, with particular attention to preoperative risk stratification, surgical planning, radiotherapy, systemic treatment selection, treatment-response prediction, and multidisciplinary decision-making. Multimodal approaches are especially promising because they reflect the way clinical decisions are made and may provide greater value than models based on a single data source. However, the current evidence remains dominated by retrospective and often single-center studies, with limited external and prospective validation. Data heterogeneity, domain shift, incomplete multimodal datasets, bias, workflow integration, regulatory requirements, and model monitoring remain important barriers to routine implementation. We therefore outline a stepwise pathway for clinical translation, from robust multicenter validation and prospective human–AI evaluation to clinical-impact studies and post-deployment monitoring. The next phase of AI in gynecologic oncology should prioritize clinically meaningful decisions and measurable patient or workflow benefit rather than incremental gains in predictive performance alone.
SN - 2234-943X
ER -