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Artificial Intelligence and Machine Learning in Clinical Practice

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A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "General Surgery".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 1495

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Special Issue Editors

Dr. Theodosis Kalamatianos

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Guest Editor

Department of Biomedical Engineering, University of Werst Attica, 12243 Athens, Greece
Interests: neuroanatomy; neurological disease; biomarkers

Dr. Ioannis Kakkos

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Guest Editor

Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9, Iroon Polytechniou Street, Zografos, 15780 Athens, Greece
Interests: transmission of nerve stimuli; study of cognitive systems and processes; medical image and signal processing; AI for diagnosis and therapy
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Special Issue Information

Dear Colleagues,

The increasing adoption of artificial intelligence (AI) and machine learning (ML) in clinical practice marks a transformative shift in modern healthcare, promising enhanced diagnostic precision, treatment personalization, and operational efficiency. Despite growing enthusiasm, real-world integration remains challenged by issues such as clinical validation, interpretability, interoperability, ethical implications, and data bias. This Special Issue aims to address these pressing challenges by showcasing cutting-edge research that bridges AI/ML innovations with practical, patient-centered clinical applications.

The scope of this Special Issue includes, but is not limited to, the following:

AI-driven diagnostic and prognostic systems.
ML-enhanced decision support tools.
Clinical risk prediction models.
Automation in radiology and pathology.
Real-time monitoring using wearable devices.
Explainable AI in medical settings.

Dr. Theodosis Kalamatianos
Dr. Ioannis Kakkos
Guest Editors

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Keywords

artificial intelligence in healthcare
medical image/signal analysis
clinical decision support systems
predictive analytics
personalized medicine
explainable AI
diagnosis
prognosis

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Research

8 pages, 197 KB

Open AccessArticle

The Role of Large Language Models in the Promotion of Minimally Invasive Interventional Radiologic Methods in Gynecology and Obstetrics

by Iason Psilopatis, Julius Emons, Kleio Vrettou and Tibor A. Zwimpfer

J. Clin. Med. 2026, 15(9), 3234; https://doi.org/10.3390/jcm15093234 - 23 Apr 2026

Abstract

Background: Minimally invasive interventional radiology (IR) offers effective, uterus-preserving treatments for several gynecologic and obstetric conditions such as uterine fibroids, adenomyosis and postpartum hemorrhage. Despite their efficacy, these methods remain underused, partly to limited awareness among clinicians and patients. Large language models (LLMs) may help bridge this gap by providing accessible, reliable information. Objective: To evaluate how current LLMs address knowledge gaps and promote awareness of minimally invasive IR methods in gynecology and obstetrics. Methods: A structured ten-question instrument was used to query three publicly available LLMs (OpenEvidence, ChatGPT, and Google Gemini). Responses were analyzed for accuracy, completeness, safety considerations, and patient-centered communication. Results: All three models accurately identified a range of medical, minimally invasive, and surgical treatments for uterine fibroids, adenomyosis, and postpartum hemorrhage, with OpenEvidence and ChatGPT providing more detailed and clinically nuanced responses. OpenEvidence achieved the highest scores overall, closely followed by ChatGPT, while Google Gemini scored lower, particularly in completeness and patient-centered communication. In more complex scenarios, performance differences became more pronounced, with OpenEvidence again leading, ChatGPT performing strongly, and Google Gemini lagging behind. Overall, OpenEvidence and ChatGPT demonstrated higher accuracy, completeness, and safety considerations, whereas Google Gemini showed comparatively weaker and less consistent performance. Conclusions: LLMs may endorse the promotion of minimally invasive IR methods in gynecology and obstetrics, but their outputs vary considerably in quality. Ongoing refinement and integration of evidence-based sources are essential before routine use in clinical practice. Therefore, effective collaboration between artificial intelligence (AI) developers and medical professionals is essential to harness this technology’s full potential. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Clinical Practice)

13 pages, 636 KB

Open AccessArticle

Evaluating the Effectiveness of Robotic Process Automation for Cancer Registry Data Abstraction in a Production EHR Environment

by Se Young Jung, Jong Soo Han, Kihyuk Lee and Ho-Young Lee

J. Clin. Med. 2026, 15(7), 2657; https://doi.org/10.3390/jcm15072657 - 31 Mar 2026

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Abstract

Background/Objectives: Robotic Process Automation (RPA) offers a potential solution for reducing the manual burden of clinical data abstraction, yet empirical evidence of its effectiveness in real-world electronic health record (EHR)-integrated cancer registries remains limited. This study aimed to evaluate the post-implementation effectiveness of RPA for cancer registry data abstraction in a tertiary hospital and to explore multidisciplinary stakeholder perceptions regarding its deployment. Methods: We implemented RPA for gastric and breast cancer registries within a production EHR system. Quantitative effectiveness was evaluated by comparing per-patient data extraction time using descriptive statistics. To ensure data integrity, all RPA-extracted outputs were entirely verified manually by researchers against source records. Qualitatively, semi-structured interviews were conducted with 14 participants and analyzed via thematic analysis based on the Promoting Action on Research Implementation in Health Services (PARiHS) framework (Evidence, Context, and Facilitation). Results: RPA was applied to 70 gastric cancer variables and 83 breast cancer variables. For the gastric cancer registry, the mean abstraction time per patient decreased by 74% (19.5 ± 3.0 to 5.1 ± 1.8 min). For the breast cancer registry, time decreased by 30% (25.4 ± 6.9 to 17.8 ± 5.5 min). Based on 2024 surgical volumes, this translates to an estimated saving of over 260 h of manual labor per year. Qualitative findings revealed that while participants recognized RPA as ideal for repetitive tasks, successful implementation was contingent on clinician cooperation and continuous output monitoring. Conclusions: RPA implementation significantly improved data abstraction efficiency in a real-world clinical research workflow. The disparity in time savings highlights that efficiency gains are contingent upon registry complexity. While formal quantitative assessments of data accuracy were not performed, RPA is a readily deployable tool for enhancing clinical data workflows when aligned with organizational readiness and robust monitoring. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Clinical Practice)

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23 pages, 2679 KB

Open AccessArticle

Morphology-Aware Deep Features and Frozen Filters for Surgical Instrument Segmentation with LLM-Based Scene Summarization

by Adnan Haider, Muhammad Arsalan and Kyungeun Cho

J. Clin. Med. 2026, 15(6), 2227; https://doi.org/10.3390/jcm15062227 - 15 Mar 2026

Viewed by 340

Abstract

Background/Objectives: The rise of artificial intelligence is injecting intelligence into the healthcare sector, including surgery. Vision-based intelligent systems that assist surgical procedures can significantly increase productivity, safety, and effectiveness during surgery. Surgical instruments are central components of any surgical intervention, yet detecting and locating them during live surgeries remains challenging due to adverse imaging conditions such as blood occlusion, smoke, blur, glare, low-contrast, instrument scale variation, and other artifacts. Methods: To address these challenges, we developed an advanced segmentation architecture termed the frozen-filters-based morphology-aware segmentation network (FFMS-Net). Accurate surgical instrument segmentation strongly depends on edge and morphology information; however, in conventional neural networks, this spatial information is progressively degraded during spatial processing. FFMS-Net introduces a frozen and learnable feature pipeline (FLFP) that simultaneously exploits frozen edge representations and learnable features. Within FLFP, Sobel and Laplacian filters are frozen to preserve edge and orientation information, which is subsequently fused with learnable initial spatial features. Moreover, a tri-atrous blending (TAB) block is employed at the end of the encoder to fuse multi-receptive-field-based contextual information, preserving instrument morphology and improving robustness under challenging conditions such as blur, blood occlusion, and smoke. Datasets focused on surgical instruments often suffer from severe class imbalance and poor instrument visibility. To mitigate these issues, FFMS-Net incorporates a progressively structure-preserving decoder (PSPD) that aggregates dilated and standard spatial information after each upsampling stage to maintain class structure. Multi-scale spatial features from different encoder levels are further fused using light skip paths (LSPs) to project channels with task-relevant patterns. Results/Conclusions: FFMS-Net is extensively evaluated on three challenging datasets: UW-Sinus-surgery-live, UW-Sinus-cadaveric, and CholecSeg8k. The proposed method demonstrates promising performance compared with state-of-the-art approaches while requiring only 1.5 million trainable parameters. In addition, an open-source large language model is integrated for non-clinical summarization of the surgical scene based on the predicted mask and deterministic descriptors derived from it. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Clinical Practice)

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