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Artificial Intelligence in Gynecologic Oncology: Current Applications, Clinical Challenges, and Future Perspectives
Dimitrios Alefragkis1,2, George Mpourazanis3, Pietro Serra4,5
1Second Department of Critical Care, Attikon University General Hospital, Athens, GRC.
Abstract:
Cervical, endometrial, ovarian, vulvar, vaginal, fallopian tube, and gestational trophoblastic neoplasia (GTN) are major gynecologic cancers that significantly impact women's health globally. In spite of progress in surgery, chemotherapy, radiotherapy, and targeted therapies, results are still variable, and timely diagnosis frequently proves challenging. Artificial intelligence (AI) has progressively taken advantage of, in digital pathological conditions risk prognostication for gynecological pathologies like endometrial and ovarian cancers, and automated Pap smear clarification for cervical cancer. Multi-platform methods combining clinical approaches and imaging data may help with prognostic assessments and personalized therapies. Nevertheless, major clinical information arises from single-center retrospective studies with minimal external authentication, and challenges like data heterogeneity, the lack of systematized protocols, ethical concerns, algorithmic bias, transparency, and workflow integration must be addressed in light of wide-ranging clinical and scientific approval.
Insights
Artificial intelligence (AI) shows promise in diagnosing gynecologic cancers like cervical, endometrial, and ovarian cancers. Further research is needed to address challenges for widespread clinical adoption.
Area of Science:
- Gynecologic Oncology
- Medical Artificial Intelligence
- Digital Pathology
Background:
- Gynecologic cancers (cervical, endometrial, ovarian, etc.) significantly impact global women's health.
- Current treatments show variable results, and diagnosis remains a challenge.
Purpose of the Study:
- To explore the application of artificial intelligence (AI) in the diagnosis and prognostication of gynecologic cancers.
- To assess the potential of multi-platform methods combining clinical and imaging data for personalized therapies.
Main Methods:
- AI has been utilized for risk prognostication in endometrial and ovarian cancers using digital pathology.
- Automated Pap smear analysis for cervical cancer detection has been developed.
- Multi-platform approaches integrating clinical and imaging data are being explored.
Main Results:
- AI demonstrates potential in risk stratification and automated diagnostics for specific gynecologic cancers.
- Combining clinical data with imaging shows promise for prognostic assessment and personalized treatment strategies.
Conclusions:
- AI offers a promising avenue for improving the diagnosis and management of gynecologic cancers.
- Addressing challenges such as data heterogeneity, ethical concerns, and algorithmic bias is crucial for clinical integration and validation.
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