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Advancing Healthcare Through Intelligent Clinical Decision Support Systems: Techniques, Applications,...
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Advancing Healthcare Through Intelligent Clinical Decision Support Systems: Techniques, Applications, and Future Directions

A special issue of Applied System Innovation (ISSN 2571-5577). This special issue belongs to the section "Medical Informatics and Healthcare Engineering".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 10589

Special Issue Editors

Dr. Alberto Comesaña-Campos

E-Mail Website
Guest Editor
1. Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
2. Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
Interests: intelligent systems; expert systems; decision-support systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jorge Cerqueiro Pequeño

E-Mail Website
Guest Editor
1. Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
2. Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
Interests: intelligent systems; expert systems; decision support systems
Special Issues, Collections and Topics in MDPI journals
Dr. María Luísa Torres-Durán

E-Mail Website
Guest Editor
1. Pulmonary Department, Hospital Álvaro Cunqueiro, 36213 Vigo, Spain
2. NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
Interests: sleep apnea; sleep disordered breathing; sleep medicine; epidemiology; AI in healthcare
Dr. Alberto Fernandez-Villar

E-Mail Website
Guest Editor
1. Pulmonary Department, Hospital Álvaro Cunqueiro, 36213 Vigo, Spain
2. NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
Interests: medical diagnosis; pulmonary medicine; respiratory diseases; epidemiology; AI in healthcare
Dr. Manuel Casal-Guisande

E-Mail Website
Guest Editor
1. Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
2. NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
Interests: intelligent systems; expert systems; decision-support systems; artificial intelligence in medicine

Special Issue Information

Dear Colleagues,

In healthcare, a variety of complex and continuous decision making processes play key roles in ensuring the delivery of accurate and timely medical care. Healthcare professionals are often faced with a constant flow of decisions across the entire spectrum of healthcare delivery, from initial patient assessment to diagnosis, treatment and subsequent follow-up, in high-pressure and uncertain environments. To address these challenges, the application of artificial intelligence models through different intelligent systems has been integrated with traditional decision support frameworks to provide enhanced, predictive and consistent solutions.

Recognizing the increasing demand for advanced tools to help healthcare professionals navigate this complex landscape of decision making, we propose the creation of a Special Issue dedicated to the exploration and application of intelligent clinical decision support systems (ICDSS) in healthcare. This Special Issue aims to explore the integration of cutting-edge AI technologies and models to facilitate more effective and informed clinical decision making, ultimately improving the quality of patient care.

The development and implementation of intelligent clinical decision support systems (ICDSS) has become essential in supporting healthcare providers' ability to make informed decisions. These systems harness the power of artificial intelligence models to seamlessly handle large volumes of patient data while integrating expertise and knowledge. ICDSS can assist healthcare professionals in several critical aspects of care, including diagnosing diseases, determining appropriate treatment modalities, predicting patient outcomes, and ensuring compliance with best practices. In doing so, they make a significant contribution to improving the overall quality of patient care.

This Special Issue aims to gather contributions that advance the understanding and application of ICDSS in clinical settings. We invite researchers to submit papers on various aspects of ICDSS in the context of healthcare. Potential topics include, but are not limited to:

  • Using ICDSS in clinical practice: Presenting case studies and real-world applications of ICDSS to improve patient care, streamline clinical workflows, and enhance medical decision making.
  • ICDSS techniques and models: Exploration of the underlying models, algorithms and technologies used in the development of ICDSS, with a focus on their adaptability to the healthcare domain.
  • Machine learning and deep learning in ICDSS: Investigating the role of machine learning and deep learning techniques in medical data analysis, image recognition, natural language processing and predictive modelling within clinical decision support systems.
  • Expert systems in ICDSS: Discuss the integration of expert knowledge and clinical guidelines into ICDSS to facilitate reasoning and decision making.
  • Hybrid systems for ICDSS: Exploring hybrid approaches that combine multiple AI techniques, knowledge and data sources to create more robust and versatile clinical decision support systems.
  • Testing and validation of ICDSS: Addressing the challenges and best practices in assessing the accuracy, reliability and safety of ICDSS, including clinical trials and validation studies.
  • ICDSS use cases: Highlighting specific scenarios and use cases where ICDSS has demonstrated significant clinical benefit, such as disease diagnosis, treatment recommendation, patient risk assessment and resource allocation.
  • Future directions and advances in ICDSS: Explore emerging trends, opportunities and challenges in the development and adoption of ICDSS, including ethical considerations, user acceptance and scalability.

We invite researchers, clinicians and healthcare technology experts to contribute their research, insights and experiences to this Special Issue. Our collective goal is to advance the field of intelligent clinical decision support systems and promote their wider integration into clinical practice, ultimately improving patient outcomes and healthcare delivery.

We look forward to receiving your valuable contributions to this Special Issue as we work together to advance our understanding of ICDSS and their critical role in modern healthcare.

Dr. Alberto Comesaña-Campos
Prof. Dr. Jorge Cerqueiro Pequeño
Dr. María Luísa Torres-Durán
Dr. Alberto Fernandez-Villar
Dr. Manuel Casal-Guisande
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. Applied System Innovation 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 1400 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

  • intelligent clinical decision support systems (ICDSS)
  • Healthcare decision-making
  • artificial intelligence in healthcare
  • machine learning and deep learning in ICDSS
  • expert systems in the definition of ICDSS
  • expert systems in the definition of ICDSS
  • hybrid systems applied in ICDSS
  • testing and validation of ICDSS
  • future work lines and advances in ICDSS

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

28 pages, 6705 KiB  
Article
Multimodal AI and Large Language Models for Orthopantomography Radiology Report Generation and Q&A
by Chirath Dasanayaka, Kanishka Dandeniya, Maheshi B. Dissanayake, Chandira Gunasena and Ruwan Jayasinghe
Appl. Syst. Innov. 2025, 8(2), 39; https://doi.org/10.3390/asi8020039 - 17 Mar 2025
Viewed by 646
Abstract
Access to high-quality dental healthcare remains a challenge in many countries due to limited resources, lack of trained professionals, and time-consuming report generation tasks. An intelligent clinical decision support system (ICDSS), which can make informed decisions based on past data, is an innovative [...] Read more.
Access to high-quality dental healthcare remains a challenge in many countries due to limited resources, lack of trained professionals, and time-consuming report generation tasks. An intelligent clinical decision support system (ICDSS), which can make informed decisions based on past data, is an innovative solution to address these shortcomings while improving continuous patient support in dental healthcare. This study proposes a viable solution with the aid of multimodal artificial intelligence (AI) and large language models (LLMs), focusing on their application for generating orthopantomography radiology reports and answering questions in the dental domain. This work also discusses efficient adaptation methods of LLMs for specific language and application domains. The proposed system primarily consists of a Blip-2-based caption generator tuned on DPT images followed by a Llama 3 8B based LLM for radiology report generation. The performance of the entire system is evaluated in two ways. The diagnostic performance of the system achieved an overall accuracy of 81.3%, with specific detection rates of 87.9% for dental caries, 89.7% for impacted teeth, 88% for bone loss, and 81.8% for periapical lesions. Subjective evaluation of AI-generated radiology reports by certified dental professionals demonstrates an overall accuracy score of 7.5 out of 10. In addition, the proposed solution includes a question-answering platform in the native Sinhala language, alongside the English language, designed to function as a chatbot for dental-related queries. We hope that this platform will eventually bridge the gap between dental services and patients, created due to a lack of human resources. Overall, our proposed solution creates new opportunities for LLMs in healthcare by introducing a robust end-to-end system for the automated generation of dental radiology reports and enhancing patient interaction and awareness. Full article
(This article belongs to the Special Issue Advancing Healthcare Through Intelligent Clinical Decision Support Systems: Techniques, Applications, and Future Directions)
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23 pages, 3855 KiB  
Article
Harnessing the Power of an Integrated Artificial Intelligence Model for Enhancing Reliable and Efficient Dental Healthcare Systems
by Samar M. Nour, Reem Salah Shehab, Samar A. Said and Islam Tharwat Abdel Halim
Appl. Syst. Innov. 2025, 8(1), 7; https://doi.org/10.3390/asi8010007 - 2 Jan 2025
Cited by 1 | Viewed by 1189
Abstract
Nowadays, efficient dental healthcare systems are considered significant for upholding oral health. Also, the ability to utilize artificial intelligence for evaluating complex data implies that dental X-ray image recognition is a critical mechanism to enhance dental disease detection. Consequently, integrating deep learning algorithms [...] Read more.
Nowadays, efficient dental healthcare systems are considered significant for upholding oral health. Also, the ability to utilize artificial intelligence for evaluating complex data implies that dental X-ray image recognition is a critical mechanism to enhance dental disease detection. Consequently, integrating deep learning algorithms into dental healthcare systems is considered a promising approach for enhancing the reliability and efficiency of diagnostic processes. In this context, an integrated artificial intelligence model is proposed to enhance model performance and interpretability. The basic idea of the proposed model is to augment the deep learning approach with Ensemble methods to improve the accuracy and robustness of dental healthcare. In the proposed model, a Non-Maximum Suppression (NMS) ensembled technique is employed to improve the accuracy of predictions along with combining outputs from multiple single models (YOLO8 and RT-DETR) to make a final decision. Experimental results on real-world datasets show that the proposed model gives high accuracy in miscellaneous dental diseases. The results show that the proposed model achieves 18% time reductions as well as 30% improvements in accuracy compared with other competitive deep learning algorithms. In addition, the effectiveness of the proposed integrated model, achieved 74% mAP50 and 58% mAP50-90, outperforming existing models. Furthermore, the proposed model grants a high degree of system reliability. Full article
(This article belongs to the Special Issue Advancing Healthcare Through Intelligent Clinical Decision Support Systems: Techniques, Applications, and Future Directions)
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15 pages, 2257 KiB  
Article
Deep Learning-Based Flap Detection System Using Thermographic Images in Plastic Surgery
by Răzvan Danciu, Bogdan Andrei Danciu, Luiz-Sorin Vasiu, Adelaida Avino, Claudiu Ioan Filip, Cristian-Sorin Hariga, Laura Răducu and Radu-Cristian Jecan
Appl. Syst. Innov. 2024, 7(6), 101; https://doi.org/10.3390/asi7060101 - 22 Oct 2024
Viewed by 1843
Abstract
In reconstructive surgery, flaps are the cornerstone for repairing tissue defects, but postoperative monitoring of their viability remains a challenge. Among the imagistic techniques for monitoring flaps, the thermal camera has demonstrated its value as an efficient indirect method that is easy to [...] Read more.
In reconstructive surgery, flaps are the cornerstone for repairing tissue defects, but postoperative monitoring of their viability remains a challenge. Among the imagistic techniques for monitoring flaps, the thermal camera has demonstrated its value as an efficient indirect method that is easy to use and easy to integrate into clinical practice. This provides a narrow color spectrum image that is amenable to the development of an artificial neural network in the context of current technological progress. In the present study, we introduce a novel attention-enhanced recurrent residual U-Net (AER2U-Net) model that is able to accurately segment flaps on thermographic images. This model was trained on a uniquely generated database of thermographic images obtained by monitoring 40 patients who required flap surgery. We compared the proposed AER2U-Net with several state-of-the-art neural networks used for multi-modal segmentation of medical images, all of which are based on the U-Net architecture (U-Net, R2U-Net, AttU-Net). Experimental results demonstrate that our model (AER2U-Net) achieves significantly better performance on our unique dataset compared to these existing U-Net variants, showing an accuracy of 0.87. This deep learning-based algorithm offers a non-invasive and precise method to monitor flap vitality and detect postoperative complications early, with further refinement needed to enhance its clinical applicability and effectiveness. Full article
(This article belongs to the Special Issue Advancing Healthcare Through Intelligent Clinical Decision Support Systems: Techniques, Applications, and Future Directions)
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14 pages, 7195 KiB  
Article
RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction
by Alaa Eleyan, Ebrahim AlBoghbaish, Abdulwahab AlShatti, Ahmad AlSultan and Darbi AlDarbi
Appl. Syst. Innov. 2024, 7(5), 77; https://doi.org/10.3390/asi7050077 - 29 Aug 2024
Cited by 2 | Viewed by 3625
Abstract
Heart disease, a global killer with many variations like arrhythmia and heart failure, remains a major health concern. Traditional risk factors include age, cholesterol, diabetes, and blood pressure. Fortunately, artificial intelligence (AI) offers a promising solution. We have harnessed the power of AI, [...] Read more.
Heart disease, a global killer with many variations like arrhythmia and heart failure, remains a major health concern. Traditional risk factors include age, cholesterol, diabetes, and blood pressure. Fortunately, artificial intelligence (AI) offers a promising solution. We have harnessed the power of AI, specifically deep learning and convolutional neural networks (CNNs), to develop Rhythmi, an innovative mobile ECG diagnosis device for heart disease detection. Rhythmi leverages extensive medical data from databases like MIT-BIH and BIDMC. These data empower the training and testing of the developed deep learning model to analyze ECG signals with accuracy, precision, sensitivity, specificity, and F1-score in identifying arrhythmias and other heart conditions, with performances reaching 98.52%, 98.55%, 98.52%, 99.26%, and 98.52%, respectively. Moreover, we tested Rhythmi in real time using a mobile device with a single-lead ECG sensor. This user-friendly prototype captures the ECG signal, transmits it to Rhythmi’s dedicated website, and provides instant diagnosis and feedback on the patient’s heart health. The developed mobile ECG diagnosis device addresses the main problems of traditional ECG diagnostic devices such as accessibility, cost, mobility, complexity, and data integration. However, we believe that despite the promising results, our system will still need intensive clinical validation in the future. Full article
(This article belongs to the Special Issue Advancing Healthcare Through Intelligent Clinical Decision Support Systems: Techniques, Applications, and Future Directions)
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12 pages, 499 KiB  
Article
Status and Challenges of Medical History Taking in Bangladesh and an Affordable Digital Solution to Tackle Them
by Forhad Hossain, Mohamed Mehfoud Bouh, Md Moshiur Rahman, Faiz Shah, Tsunenori Mine, Rafiqul Islam, Naoki Nakashima and Ashir Ahmed
Appl. Syst. Innov. 2024, 7(4), 69; https://doi.org/10.3390/asi7040069 - 14 Aug 2024
Cited by 1 | Viewed by 1705
Abstract
Capturing patients’ medical histories significantly influences clinical decisions. Errors in this process lead to clinical errors, which increase costs and dissatisfaction among physicians and patients. Physicians in developing countries are overloaded with patients and cannot always follow the proper history-taking procedure. The challenges [...] Read more.
Capturing patients’ medical histories significantly influences clinical decisions. Errors in this process lead to clinical errors, which increase costs and dissatisfaction among physicians and patients. Physicians in developing countries are overloaded with patients and cannot always follow the proper history-taking procedure. The challenges have been acknowledged; however, a comprehensive understanding of the status and the remedies has remained unexplored. This paper aims to investigate the workload, history-taking challenges, and the willingness of the physicians to accept digital solutions. A cross-sectional online survey was conducted on 104 physicians across Bangladesh, featuring 22 questions regarding their professional environment, workload, digitization status of health records, challenges in history taking, and attitudes toward adopting digital solutions for managing patient histories; 92.67% of the physicians face high workloads, 88.46% struggle in medical history taking, and only 4.81% use digital medical records. About 70% struggle to complete the necessary history-taking steps, emphasizing the urgent need for solutions. A novel visualization system, the Smart Health Gantt Chart (SHGC), has been introduced for their instant feedback. A total of 93.27% of physicians expressed their willingness to use such a system. The proposed SHGC has the potential to enhance healthcare efficiency in developing nations, benefit physicians, and improve patient-centered care. Full article
(This article belongs to the Special Issue Advancing Healthcare Through Intelligent Clinical Decision Support Systems: Techniques, Applications, and Future Directions)
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