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Digital Innovations in Obstetrics and Gynecology: Artificial Intelligence, Diagnostic Advances, Minimally Invasive Surgery, and Clinical Decision-Making

A Special Issue of Journal of Clinical Medicine (ISSN 2077-0383) belonging to the section "Machine Learning and Artificial Intelligence in Clinical Medicine".

Deadline for manuscript submissions: 20 March 2027 | Viewed by 1009

Editor

Dr. Iason Psilopatis

E-Mail Website
Guest Editor
Department of Gynecology and Obstetrics, University Hospital of Basel, 4031 Basel, Switzerland
Interests: gynecology; oncology; obstetrics; breast; uterus; endometriosis; fibroid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Obstetrics and gynecology is undergoing a transformative shift driven by digital innovations, artificial intelligence (AI), and minimally invasive surgical techniques. This Special Issue focuses on the clinical application and real-world implementation of these technologies to improve patient outcomes, surgical safety, and clinical decision-making.

AI and machine learning have demonstrated considerable potential in risk prediction for pregnancy complications (preeclampsia, gestational diabetes, preterm birth), automated fetal biometry, and gynecologic oncology screening. Clinical decision support systems are increasingly integrated into routine care, aiding early diagnosis and treatment planning. However, gaps persist between algorithmic development and clinical implementation. Addressing these gaps requires robust external validation, real-world effectiveness studies, and attention to algorithmic fairness across diverse populations—topics that fall within the scope of this Special Issue.

Minimally invasive surgery continues to evolve, with robot-assisted platforms, novel laparoscopic techniques, and enhanced recovery protocols reshaping gynecologic and obstetric surgical care. In this Special Issue, comparative effectiveness studies, surgical training innovations, and quality assurance measures are welcome.

We invite high-quality original research articles and reviews addressing the full spectrum of digital innovations in obstetrics and gynecology. Topics of interest include, but are not limited to:

  • AI‑powered risk prediction models for pregnancy complications;
  • Deep learning for automated fetal biometry and anomaly detection;
  • Machine learning in gynecologic oncology screening;
  • AI applications in assisted reproductive technology;
  • Robot‑assisted versus conventional laparoscopic surgery: comparative effectiveness;
  • Novel minimally invasive surgical techniques and technologies;
  • Clinical outcomes of digital health interventions in obstetrics and gynecology.

You may choose our Joint Special Issue in Diagnostics.

Dr. Iason Psilopatis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • obstetrics
  • gynecology
  • artificial intelligence
  • machine learning
  • minimally invasive surgery
  • digital health

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Published Papers (2 papers)

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Research

16 pages, 6855 KB  
Article
Saliency-Curated Deep Learning for Predicting Receptor Status in Breast Cancer Brain Metastases
by Rafail C. Christodoulou, Giorgos Christofi, Constantinos Theofylaktou, Rafael Pitsillos, Iliana Aristokleous, Elena E. Solomou, Evros Vassiliou and Michalis F. Georgiou
J. Clin. Med. 2026, 15(17), 6501; https://doi.org/10.3390/jcm15176501 - 22 Aug 2026
Viewed by 291
Abstract
Background: Breast cancer brain metastases (BCBMs) exhibit notable receptor discordance between primary tumors and metastases, yet obtaining intracranial biopsies is rarely practical. We created an interpretable deep learning radiogenomic model to predict estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth [...] Read more.
Background: Breast cancer brain metastases (BCBMs) exhibit notable receptor discordance between primary tumors and metastases, yet obtaining intracranial biopsies is rarely practical. We created an interpretable deep learning radiogenomic model to predict estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status from MRI scans in BCBM cases. Methods: A total of 241 post-contrast T1-weighted MRIs from 142 patients were analyzed. We developed a mask-free 3D Residual Neural Network (ResNet) ensemble trained directly on cropped, bias-corrected brain images, with comprehensive 3D geometric and intensity augmentations. The model was optimized in a multi-label setting using Asymmetric Focal Loss. Hyperparameters were fine-tuned via Bayesian optimization, and class probabilities were calibrated. Additionally, Integrated Gradients (IG) offered voxel-level saliency maps. Results: Our ResNet ensemble model achieved a micro-averaged AUROC of 0.74 and a macro-averaged AUROC of 0.67. Receptor-specific AUROCs were 0.57 for ER, 0.79 for PR, and 0.64 for HER2. The F1-scores were 0.56, 0.62, and 0.88 for ER, PR, and HER2, respectively. The held-out cohort contained only four HER2-negative patients, so threshold-dependent HER2 metrics are strongly prevalence-driven and AUROC is the more appropriate summary. Qualitatively inspected saliency maps were concentrated on enhancing metastases with limited background attribution. Conclusions: This proof-of-concept study shows that a segmentation-free, interpretable 3D Convolutional Neural Network (CNN) can be trained to capture receptor-associated patterns in post-contrast T1-weighted MRI of BCBMs. Accuracy was modest relative to published radiomics models, and the mask-free, single-sequence design is offered as a methodological contribution rather than a performance gain. These findings are preliminary, do not establish clinical utility, and require validation in larger, multi-center cohorts. Full article
(This article belongs to the Special Issue Digital Innovations in Obstetrics and Gynecology: Artificial Intelligence, Diagnostic Advances, Minimally Invasive Surgery, and Clinical Decision-Making)
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18 pages, 239 KB  
Article
The Role of Large Language Models in Hormonal Contraception Consultation
by Iason Psilopatis, Julius Emons and Tibor A. Zwimpfer
J. Clin. Med. 2026, 15(14), 5543; https://doi.org/10.3390/jcm15145543 - 15 Jul 2026
Viewed by 524
Abstract
Background: Large Language Models (LLMs) demonstrate promise in medical applications, but their performance in hormonal contraception consultation remains underexplored. Objective: To evaluate the accuracy and comprehensiveness of LLM-generated hormonal contraception counseling compared to evidence-based guidelines. Methods: Ten fictitious clinical case scenarios representing common [...] Read more.
Background: Large Language Models (LLMs) demonstrate promise in medical applications, but their performance in hormonal contraception consultation remains underexplored. Objective: To evaluate the accuracy and comprehensiveness of LLM-generated hormonal contraception counseling compared to evidence-based guidelines. Methods: Ten fictitious clinical case scenarios representing common contraceptive counseling situations were presented to Chat-GPT, Google Gemini, and OpenEvidence. Cases assessed medical eligibility screening, contraindication recognition, drug interactions, side effect management, and emergency contraception guidance. Two board-certified obstetrician–gynecologists independently evaluated the responses based on international clinical guidelines. Results: Across the ten predefined clinical scenarios, all three LLMs achieved high accuracy in identifying contraindications according to Medical Eligibility Criteria (MEC) and provided appropriate alternative contraceptive recommendations. Models demonstrated high proficiency in managing drug interactions, particularly the lamotrigine–estrogen interaction, and provided evidence-based side effect management strategies. However, the communication styles differed. Chat-GPT emphasized structured consultation and shared decision-making, Gemini provided practical action-oriented guidance, and OpenEvidence delivered concise evidence-focused summaries. Conclusions: In this limited set of standardized fictitious cases, the evaluated LLMs generally provided responses that were broadly aligned with selected guideline recommendations. Current LLMs require cautious deployment, given limitations in individualized assessment and health literacy optimization, and are best positioned as complementary educational tools rather than replacements for professional contraceptive counseling. Full article
(This article belongs to the Special Issue Digital Innovations in Obstetrics and Gynecology: Artificial Intelligence, Diagnostic Advances, Minimally Invasive Surgery, and Clinical Decision-Making)
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