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Diagnostics
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Advancing Clinical Diagnosis with Artificial Intelligence: Applications, Challenges, and Future...
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Advancing Clinical Diagnosis with Artificial Intelligence: Applications, Challenges, and Future Directions

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 2783

Special Issue Editors

Dr. Haipeng Liu

E-Mail Website
Guest Editor
Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5RW, UK
Interests: computational simulation of the cardiovascular system; AI-assisted diagnosis; medical data analysis; wearable sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Rajesh K. Tripathy

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, BITS Pilani, Hyderabad 500078, India
Interests: healthcare data; machine learning; deep learning; signal processing; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Shonak Bansal

E-Mail Website
Guest Editor
Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, Mohali, India
Interests: computational simulation of photodetectors; graphene-based photodetectors; machine learning; antennas; wearable sensors; nanowires; deep learning; signal processing; IoT
Dr. Prince Jain

E-Mail Website
Guest Editor
Department of Mechatronics Engineering, Parul Institute of Technology, Parul University, Vadodara, Gujarat 391760, India
Interests: machine learning; artificial intelligence; biomedical signal processing; internet of things; nanotechnology

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed a rapid growth in generative artificial intelligence (AI) technologies and their clinical applications. Large language models (LLMs) have been applied in different aspects of modern medical science, including medical education, bibliographic analysis, pharmacological analysis, construction of knowledge maps, and simulated diagnosis. In the meantime, the development of electronic health records (EHRs), radiomics, wearable sensors, wireless communication, cloud computing, and advanced data fusion algorithms are generating more AI models based on multimodal data for the diagnosis and monitoring of diseases in the context of the Internet of Medical Things (IoMT). These advanced AI-enhanced technologies are reshaping the landscape of modern diagnosis, enabling the early screening of accurate diagnosis of acute and chronic diseases. Meanwhile, data protection, privacy, and other ethics issues are emerging in this new era, with efforts in regulatory and technical aspects, including cryptography, biometrics, watermarking, and Blockchain-based security techniques.

The aim of this Special Issue entitled “Advancing Clinical Diagnosis with Artificial Intelligence: Applications, Challenges, and Future Directions” is to share information on cutting-edge AI technologies and multimodal medical data analysis. The scope of this Special Issue will include studies on LLMs, AI-enhanced multimodal medical data analysis, IoMT, as well as data security in AI-enhanced diagnostics.

Dr. Haipeng Liu
Dr. Rajesh K. Tripathy
Dr. Shonak Bansal
Dr. Prince Jain
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics 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

  • deep learning
  • artificial intelligence (AI)
  • internet of medical things (IoMT)
  • AI-assisted diagnostics
  • multimodal clinical data
  • data-driven healthcare

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Published Papers (3 papers)

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Research

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12 pages, 2843 KB  
Article
Unsupervised Machine Learning to Identify Patient Clusters and Tailor Perioperative Care in Colorectal Surgery
by Philip Deslarzes, He Ayu Xu, Jean Louis Raisaro, Martin Hübner and Fabian Grass
Diagnostics 2025, 15(17), 2124; https://doi.org/10.3390/diagnostics15172124 - 22 Aug 2025
Viewed by 411
Abstract
Background: The aim of the present study was to apply machine learning (ML) techniques to define clusters relating patient demographics, compliance, and outcome variables in colorectal enhanced recovery after surgery (ERAS) patients and improve data-driven, predictive decision-making. Methods: To uncover inherent [...] Read more.
Background: The aim of the present study was to apply machine learning (ML) techniques to define clusters relating patient demographics, compliance, and outcome variables in colorectal enhanced recovery after surgery (ERAS) patients and improve data-driven, predictive decision-making. Methods: To uncover inherent patient subgroups from the data without pre-defined labels, the unsupervised K-means clustering algorithm was utilized. This technique was selected for its effectiveness in partitioning patients into distinct groups by iteratively assigning them to the nearest cluster mean, thereby minimizing within-cluster variance across key variables. The top five recovery goals and the top 10 clinical outcome variables were defined based on clinical considerations (incidence and importance). In a second step, the cluster transition was traced by monitoring the transitions between clusters from demographic through compliance to outcome variables. Results: A total of 1381 patients were available for final analysis, revealing three clusters (low risk, n = 490, 36%; intermediate risk, n = 157, 11%; and high risk, n = 734, 53%) for demographic, two clusters (high compliance, n = 1011, 73%, and low compliance n = 370, 27%) for perioperative, and two clusters (good and poor outcomes) for the top five recovery goals and the top 10 clinical outcomes, respectively. The cluster transition for the top five recovery goals and the top 10 clinical outcomes revealed that most patients (488/490, 99.6%) of the low-risk demographic cluster had high perioperative compliance, and over 90% of them had favorable functional and clinical outcomes. Of the 2/3 of intermediate risk patients who had poor perioperative compliance, over 40% had a poor functional recovery, whereas 83% had good clinical outcomes. Of the high-risk demographic group, 100% (734/734) had low perioperative compliance, and over 40% of them had poor functional recovery. Conclusions: This ML-based analysis of demographic, compliance, and recovery clusters and associated cluster transition allowed us to identify patient clusters as a first step to tailored ERAS protocols aiming to improve compliance and outcomes. Full article
(This article belongs to the Special Issue Advancing Clinical Diagnosis with Artificial Intelligence: Applications, Challenges, and Future Directions)
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17 pages, 1193 KB  
Article
Evaluating the Nuclear Reaction Optimization (NRO) Algorithm for Gene Selection in Cancer Classification
by Shahad Alkamli and Hala Alshamlan
Diagnostics 2025, 15(7), 927; https://doi.org/10.3390/diagnostics15070927 - 3 Apr 2025
Cited by 2 | Viewed by 804
Abstract
Background/Objectives: Cancer classification using microarray datasets presents a significant challenge due to their extremely high dimensionality. This complexity necessitates advanced optimization methods for effective gene selection. Methods: This study introduces and evaluates the Nuclear Reaction Optimization (NRO)—drawing inspiration from nuclear fission [...] Read more.
Background/Objectives: Cancer classification using microarray datasets presents a significant challenge due to their extremely high dimensionality. This complexity necessitates advanced optimization methods for effective gene selection. Methods: This study introduces and evaluates the Nuclear Reaction Optimization (NRO)—drawing inspiration from nuclear fission and fusion—for identifying informative gene subsets in six benchmark cancer microarray datasets. Employed as a standalone approach without prior dimensionality reduction, NRO was assessed using both Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN). Leave-One-Out Cross-Validation (LOOCV) was used to rigorously evaluate classification accuracy and the relevance of the selected genes. Results: Experimental results show that NRO achieved high classification accuracy, particularly when used with SVM. In select datasets, it outperformed several state-of-the-art optimization algorithms. However, due to the absence of additional dimensionality reduction techniques, the number of selected genes remains relatively high. Comparative analysis with Harris Hawks Optimization (HHO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Firefly Algorithm (FFA) shows that while NRO delivers competitive performance, it does not consistently outperform all methods across datasets. Conclusions: The study concludes that NRO is a promising gene selection approach, particularly effective in certain datasets, and suggests that future work should explore hybrid models and feature reduction techniques to further enhance its accuracy and efficiency. Full article
(This article belongs to the Special Issue Advancing Clinical Diagnosis with Artificial Intelligence: Applications, Challenges, and Future Directions)
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Review

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16 pages, 660 KB  
Review
The Potential of Artificial Intelligence in the Diagnosis and Prognosis of Sepsis: A Narrative Review
by George Țocu, Elena Lăcrămioara Lisă, Dana Tutunaru, Raul Mihailov, Cristina Șerban, Valerii Luțenco, Florentin Dimofte, Mădălin Guliciuc, Iulia Chiscop, Bogdan Ioan Ștefănescu, Elena Niculeț, Gabriela Gurău, Sorin Ion Berbece, Oana Mariana Mihailov and Loredana Stavăr Matei
Diagnostics 2025, 15(17), 2169; https://doi.org/10.3390/diagnostics15172169 - 27 Aug 2025
Viewed by 637
Abstract
Background/Objectives: Sepsis is a severe medical condition characterized by a dysregulated host response to infection, with potentially fatal outcomes, requiring early diagnosis and rapid intervention. The limitations of traditional sepsis identification methods, as well as the complexity of clinical data generated in intensive [...] Read more.
Background/Objectives: Sepsis is a severe medical condition characterized by a dysregulated host response to infection, with potentially fatal outcomes, requiring early diagnosis and rapid intervention. The limitations of traditional sepsis identification methods, as well as the complexity of clinical data generated in intensive care, have driven increased interest in applying artificial intelligence in this field. The aim of this narrative review article is to analyze how artificial intelligence is being used in the diagnosis and prognosis of sepsis, to present the most relevant current models and algorithms, and to discuss the challenges and opportunities related to integrating these technologies into clinical practice. Methods: We conducted a structured literature search for this narrative review, covering studies published between 2016 and 2024 in databases such as PubMed/Medline, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The review covered models based on machine learning (ML), deep neural networks (DNNs), Recurrent Neural Networks (RNNs), and clinical alert systems implemented in hospitals. The clinical data sources used, algorithms applied, system architectures, and performance outcomes are presented. Results: Numerous artificial intelligence models demonstrated superior performance compared to conventional clinical scores (qSOFA, SIRS), achieving AUC values above 0.90 in predicting sepsis and mortality. Systems such as Targeted Real-Time Early Warning System (TREWS) and InSight have been clinically validated and have significantly reduced the time to treatment initiation. However, challenges remain, such as a lack of model transparency, algorithmic bias, difficulties integrating into clinical workflows, and the absence of external validation in multicenter settings. Conclusions: Artificial intelligence has the potential to transform sepsis management through early diagnosis, risk stratification, and personalized treatment. A responsible, multidisciplinary approach is necessary, including rigorous clinical validation, enhanced interpretability, and training of healthcare personnel to effectively integrate these technologies into everyday practice. Full article
(This article belongs to the Special Issue Advancing Clinical Diagnosis with Artificial Intelligence: Applications, Challenges, and Future Directions)
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