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Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology

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

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 15663

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Dr. Antonio Jorge Forte

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Division of Plastic Surgery, Department of Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
Interests: plastic surgery; hand surgery; peripheral nerve surgery; migraine surgery; lymphatic surgery; aesthetic surgery; hand rejuvenation; facial reanimation; microsurgery; breast surgery

Special Issue Information

Dear Colleagues,

This groundbreaking Special Issue delves deep into the profound impact of artificial intelligence (AI) on clinical medicine, showcasing its revolutionary potential to transform healthcare practices. From early disease detection to precision treatment strategies, AI-powered solutions are changing the landscape of patient care. Cutting-edge research articles explore the integration of AI algorithms in medical imaging, assisting radiologists with faster and more accurate diagnoses. Moreover, AI-driven predictive models are being leveraged to identify at-risk patients and optimize treatment plans, resulting in improved patient outcomes and reduced healthcare costs. The Special Issue also delves into AI-enabled clinical decision support systems that aid healthcare professionals in making evidence-based choices, and ensuring safer and more efficient patient care. By highlighting the rapid advancements in AI technology, this special issue paves the way for a more patient-centric and data-driven future in clinical medicine.

Dr. Antonio Jorge Forte
Guest Editor

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Keywords

artificial intelligence
clinical medicine
medical imaging
precision medicine
predictive models
clinical decision sup-port systems
patient care
healthcare practices
early disease detection
patient outcomes
remote patient monitoring
telehealth
wearables in healthcare

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Published Papers (9 papers)

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Editorial

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5 pages, 601 KiB

Open AccessEditorial

From Promise to Practice: Harnessing AI’s Power to Transform Medicine

by Ariana Genovese, Sahar Borna, Cesar A. Gomez-Cabello, Syed Ali Haider, Srinivasagam Prabha, Maissa Trabilsy and Antonio Jorge Forte

J. Clin. Med. 2025, 14(4), 1225; https://doi.org/10.3390/jcm14041225 - 13 Feb 2025

Viewed by 2648

Abstract

Artificial intelligence (AI) is not merely a tool for the future of clinical medicine; it is already reshaping the landscape, challenging traditional paradigms, and expanding the horizons of what is achievable in healthcare [...] Full article

(This article belongs to the Special Issue Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology)

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Research

Jump to: Editorial, Other

20 pages, 10573 KiB

Open AccessArticle

A Validity Analysis of Text-to-Image Generative Artificial Intelligence Models for Craniofacial Anatomy Illustration

by Syed Ali Haider, Srinivasagam Prabha, Cesar A. Gomez-Cabello, Sahar Borna, Sophia M. Pressman, Ariana Genovese, Maissa Trabilsy, Andrea Galvao, Keith T. Aziz, Peter M. Murray, Yogesh Parte, Yunguo Yu, Cui Tao and Antonio Jorge Forte

J. Clin. Med. 2025, 14(7), 2136; https://doi.org/10.3390/jcm14072136 - 21 Mar 2025

Viewed by 389

Abstract

Background: Anatomically accurate illustrations are imperative in medical education, serving as crucial tools to facilitate comprehension of complex anatomical structures. While traditional illustration methods involving human artists remain the gold standard, the rapid advancement of Generative Artificial Intelligence (GAI) models presents a new opportunity to automate and accelerate this process. This study evaluated the potential of GAI models to produce craniofacial anatomy illustrations for educational purposes. Methods: Four GAI models, including Midjourney v6.0, DALL-E 3, Gemini Ultra 1.0, and Stable Diffusion 2.0 were used to generate 736 images across multiple views of surface anatomy, bones, muscles, blood vessels, and nerves of the cranium in both oil painting and realistic photograph styles. Four reviewers evaluated the images for anatomical detail, aesthetic quality, usability, and cost-effectiveness. Inter-rater reliability analysis assessed evaluation consistency. Results: Midjourney v6.0 scored highest for aesthetic quality and cost-effectiveness, and DALL-E 3 performed best for anatomical detail and usability. The inter-rater reliability analysis demonstrated a high level of agreement among reviewers (ICC = 0.858, 95% CI). However, all models showed significant flaws in depicting crucial anatomical details such as foramina, suture lines, muscular origins/insertions, and neurovascular structures. These limitations were further characterized by abstract depictions, mixing of layers, shadowing, abnormal muscle arrangements, and labeling errors. Conclusions: These findings highlight GAI’s potential for rapidly creating craniofacial anatomy illustrations but also its current limitations due to inadequate training data and incomplete understanding of complex anatomy. Refining these models through precise training data and expert feedback is vital. Ethical considerations, such as potential biases, copyright challenges, and the risks of propagating inaccurate information, must also be carefully navigated. Further refinement of GAI models and ethical safeguards are essential for safe use. Full article

(This article belongs to the Special Issue Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology)

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18 pages, 2687 KiB

Open AccessArticle

A Robust Blood Vessel Segmentation Technique for Angiographic Images Employing Multi-Scale Filtering Approach

by Agne Paulauskaite-Taraseviciene, Julius Siaulys, Antanas Jankauskas and Gabriele Jakuskaite

J. Clin. Med. 2025, 14(2), 354; https://doi.org/10.3390/jcm14020354 - 8 Jan 2025

Viewed by 1378

Abstract

Background: This study focuses on the critical task of blood vessel segmentation in medical image analysis, essential for diagnosing cardiovascular diseases and enabling effective treatment planning. Although deep learning architectures often produce very high segmentation results in medical images, coronary computed tomography angiography (CTA) images are more challenging than invasive coronary angiography (ICA) images due to noise and the complexity of vessel structures. Methods: Classical architectures for medical images, such as U-Net, achieve only moderate accuracy, with an average Dice score of 0.722. Results: This study introduces Morpho-U-Net, an enhanced U-Net architecture that integrates advanced morphological operations, including Gaussian blurring, thresholding, and morphological opening/closing, to improve vascular integrity, reduce noise, and achieve a higher Dice score of 0.9108, a precision of 0.9341, and a recall of 0.8872. These enhancements demonstrate superior robustness to noise and intricate vessel geometries. Conclusions: This pre-processing filter effectively reduces noise by grouping neighboring pixels with similar intensity values, allowing the model to focus on relevant anatomical structures, thus outperforming traditional methods in handling the challenges posed by CTA images. Full article

(This article belongs to the Special Issue Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology)

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30 pages, 4591 KiB

Open AccessArticle

Machine Learning Classification of Pediatric Health Status Based on Cardiorespiratory Signals with Causal and Information Domain Features Applied—An Exploratory Study

by Maciej Rosoł, Jakub S. Gąsior, Kacper Korzeniewski, Jonasz Łaba, Robert Makuch, Bożena Werner and Marcel Młyńczak

J. Clin. Med. 2024, 13(23), 7353; https://doi.org/10.3390/jcm13237353 - 2 Dec 2024

Viewed by 962

Abstract

Background/Objectives: This study aimed to evaluate the accuracy of machine learning (ML) techniques in classifying pediatric individuals—cardiological patients, healthy participants, and athletes—based on cardiorespiratory features from short-term static measurements. It also examined the impact of cardiorespiratory coupling (CRC)-related features (from causal and information domains) on the modeling accuracy to identify a preferred cardiorespiratory feature set that could be further explored for specialized tasks, such as monitoring training progress or diagnosing health conditions. Methods: We utilized six self-prepared datasets that comprised various subsets of cardiorespiratory parameters and applied several ML algorithms to classify subjects into three distinct groups. This research also leveraged explainable artificial intelligence (XAI) techniques to interpret model decisions and investigate feature importance. Results: The highest accuracy, over 89%, was obtained using the dataset that included most important demographic, cardiac, respiratory, and interrelated (causal and information) domain features. The dataset that comprised the most influential features but without demographic data yielded the second best accuracy, equal to 85%. Incorporation of the causal and information domain features significantly improved the classification accuracy. The use of XAI tools further highlighted the importance of these features with respect to each individual group. Conclusions: The integration of ML algorithms with a broad spectrum of cardiorespiratory features provided satisfactory efficiency in classifying pediatric individuals into groups according to their actual health status. This study underscored the potential of ML and XAI in advancing the analysis of cardiorespiratory signals and emphasized the importance of CRC-related features. The established set of features that appeared optimal for the classification of pediatric patients should be further explored for their potential in assessing individual progress through training or rehabilitation. Full article

(This article belongs to the Special Issue Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology)

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22 pages, 3586 KiB

Open AccessArticle

Investigation of Relationship between Hemodynamic and Morphometric Characteristics of Aortas in Pediatric Patients

by Olga V. Doroshenko, Alex G. Kuchumov, Mikhail V. Golub, Irina O. Rakisheva, Nikita A. Skripka, Sergey P. Pavlov, Yulija A. Strazhec, Petr V. Lazarkov, Nikita D. Saychenko and Roman M. Shekhmametyev

J. Clin. Med. 2024, 13(17), 5141; https://doi.org/10.3390/jcm13175141 - 29 Aug 2024

Viewed by 1114

Abstract

Background: The utilization of hemodynamic parameters, whose estimation is often cumbersome, can fasten diagnostics and decision-making related to congenital heart diseases. The main goal of this study is to investigate the relationship between hemodynamic and morphometric features of the thoracic aorta and to construct corresponding predictive models. Methods: Multi-slice spiral computed tomography images of the aortas of patients with coarctation diagnoses and patients without cardiac or vascular diseases were evaluated to obtain numerical models of the aorta and branches of the aortic arch. Hemodynamic characteristics were estimated in key subdomains of the aorta and three branches using computational fluid dynamics methods. The key morphometric features (diameters) were calculated at locations in proximity to the domains, where hemodynamic characteristics are evaluated. Results: The functional dependencies for velocities and pressure on the corresponding diameters have been fitted, and a metamodel has been constructed employing the predicted values from these models. Conclusions: The metamodel demonstrated high accuracy in classifying aortas into their respective types, thereby confirming the adequacy of the predicted hemodynamic characteristics by morphometric characteristics. The proposed methodology is applicable to other heart diseases without fundamental changes. Full article

(This article belongs to the Special Issue Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology)

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10 pages, 3665 KiB

Open AccessArticle

Development and Validation of a Prediction Model for Acute Hypotensive Events in Intensive Care Unit Patients

by Toshiyuki Nakanishi, Tatsuya Tsuji, Tetsuya Tamura, Koichi Fujiwara and Kazuya Sobue

J. Clin. Med. 2024, 13(10), 2786; https://doi.org/10.3390/jcm13102786 - 9 May 2024

Viewed by 1454

Abstract

Background: Persistent hypotension in the intensive care unit (ICU) is associated with increased mortality. Predicting acute hypotensive events can lead to timely intervention. We aimed to develop a prediction model of acute hypotensive events in patients admitted to the ICU. Methods: We included adult patients admitted to the Nagoya City University (NCU) Hospital ICU between January 2018 and December 2021 for model training and internal validation. The MIMIC-III database was used for external validation. A hypotensive event was defined as a mean arterial pressure < 60 mmHg for at least 5 min in 10 min. The input features were age, sex, and time-series data for vital signs. We compared the area under the receiver-operating characteristic curve (AUROC) of three machine-learning algorithms: logistic regression, the light gradient boosting machine (LightGBM), and long short-term memory (LSTM). Results: Acute hypotensive events were found in 1325/1777 (74.6%) and 2691/5266 (51.1%) of admissions in the NCU and MIMIC-III cohorts, respectively. In the internal validation, the LightGBM model had the highest AUROC (0.835), followed by the LSTM (AUROC 0.834) and logistic regression (AUROC 0.821) models. Applying only blood pressure-related features, the LSTM model achieved the highest AUROC (0.843) and consistently showed similar results in external and internal validation. Conclusions: The LSTM model using only blood pressure-related features had the highest AUROC with comparable performance in external validation. Full article

(This article belongs to the Special Issue Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology)

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12 pages, 523 KiB

Open AccessArticle

Automated Ischemic Stroke Classification from MRI Scans: Using a Vision Transformer Approach

by Wafae Abbaoui, Sara Retal, Soumia Ziti and Brahim El Bhiri

J. Clin. Med. 2024, 13(8), 2323; https://doi.org/10.3390/jcm13082323 - 17 Apr 2024

Cited by 4 | Viewed by 1871

Abstract

Background: This study evaluates the performance of a vision transformer (ViT) model, ViT-b16, in classifying ischemic stroke cases from Moroccan MRI scans and compares it to the Visual Geometry Group 16 (VGG-16) model used in a prior study. Methods: A dataset of 342 MRI scans, categorized into ‘Normal’ and ’Stroke’ classes, underwent preprocessing using TensorFlow’s tf.data API. Results: The ViT-b16 model was trained and evaluated, yielding an impressive accuracy of 97.59%, surpassing the VGG-16 model’s 90% accuracy. Conclusions: This research highlights the ViT-b16 model’s superior classification capabilities for ischemic stroke diagnosis, contributing to the field of medical image analysis. By showcasing the efficacy of advanced deep learning architectures, particularly in the context of Moroccan MRI scans, this study underscores the potential for real-world clinical applications. Ultimately, our findings emphasize the importance of further exploration into AI-based diagnostic tools for improving healthcare outcomes. Full article

(This article belongs to the Special Issue Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology)

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16 pages, 3984 KiB

Open AccessArticle

Development and Validation of Deep-Learning-Based Sepsis and Septic Shock Early Prediction System (DeepSEPS) Using Real-World ICU Data

by Taehwa Kim, Yunwon Tae, Hye Ju Yeo, Jin Ho Jang, Kyungjae Cho, Dongjoon Yoo, Yeha Lee, Sung-Ho Ahn, Younga Kim, Narae Lee and Woo Hyun Cho

J. Clin. Med. 2023, 12(22), 7156; https://doi.org/10.3390/jcm12227156 - 17 Nov 2023

Cited by 7 | Viewed by 2457

Abstract

Background: Successful sepsis treatment depends on early diagnosis. We aimed to develop and validate a system to predict sepsis and septic shock in real time using deep learning. Methods: Clinical data were retrospectively collected from electronic medical records (EMRs). Data from 2010 to 2019 were used as development data, and data from 2020 to 2021 were used as validation data. The collected EMRs consisted of eight vital signs, 13 laboratory data points, and three demographic information items. We validated the deep-learning-based sepsis and septic shock early prediction system (DeepSEPS) using the validation datasets and compared our system with other traditional early warning scoring systems, such as the national early warning score, sequential organ failure assessment (SOFA), and quick sequential organ failure assessment. Results: DeepSEPS achieved even higher area under receiver operating characteristic curve (AUROC) values (0.7888 and 0.8494 for sepsis and septic shock, respectively) than SOFA. The prediction performance of traditional scoring systems was enhanced because the early prediction time point was close to the onset time of sepsis; however, the DeepSEPS scoring system consistently outperformed all conventional scoring systems at all time points. Furthermore, at the time of onset of sepsis and septic shock, DeepSEPS showed the highest AUROC (0.9346). Conclusions: The sepsis and septic shock early warning system developed in this study exhibited a performance that is worth considering when predicting sepsis and septic shock compared to other traditional early warning scoring systems. DeepSEPS showed better performance than existing sepsis prediction programs. This novel real-time system that simultaneously predicts sepsis and septic shock requires further validation. Full article

(This article belongs to the Special Issue Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology)

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Other

Jump to: Editorial, Research

18 pages, 1464 KiB

Open AccessSystematic Review

Predicting Cardiopulmonary Arrest with Digital Biomarkers: A Systematic Review

by Gioacchino D. De Sario Velasquez, Antonio J. Forte, Christopher J. McLeod, Charles J. Bruce, Laura M. Pacheco-Spann, Karla C. Maita, Francisco R. Avila, Ricardo A. Torres-Guzman, John P. Garcia, Sahar Borna, Christopher L. Felton, Rickey E. Carter and Clifton R. Haider

J. Clin. Med. 2023, 12(23), 7430; https://doi.org/10.3390/jcm12237430 - 30 Nov 2023

Cited by 1 | Viewed by 2221

Abstract

(1) Background: Telemetry units allow the continuous monitoring of vital signs and ECG of patients. Such physiological indicators work as the digital signatures and biomarkers of disease that can aid in detecting abnormalities that appear before cardiac arrests (CAs). This review aims to identify the vital sign abnormalities measured by telemetry systems that most accurately predict CAs. (2) Methods: We conducted a systematic review using PubMed, Embase, Web of Science, and MEDLINE to search studies evaluating telemetry-detected vital signs that preceded in-hospital CAs (IHCAs). (3) Results and Discussion: Out of 45 studies, 9 met the eligibility criteria. Seven studies were case series, and 2 were case controls. Four studies evaluated ECG parameters, and 5 evaluated other physiological indicators such as blood pressure, heart rate, respiratory rate, oxygen saturation, and temperature. Vital sign changes were highly frequent among participants and reached statistical significance compared to control subjects. There was no single vital sign change pattern found in all patients. ECG alarm thresholds may be adjustable to reduce alarm fatigue. Our review was limited by the significant dissimilarities of the studies on methodology and objectives. (4) Conclusions: Evidence confirms that changes in vital signs have the potential for predicting IHCAs. There is no consensus on how to best analyze these digital biomarkers. More rigorous and larger-scale prospective studies are needed to determine the predictive value of telemetry-detected vital signs for IHCAs. Full article

(This article belongs to the Special Issue Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology)

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