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Artificial Intelligence (AI) in Healthcare

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 37579

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Prof. Dr. Dimitrios Dionisios Koutsouris

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Division of Information Transmission Systems and Material Technology, National Technical University of Athens, 10682 Athens, Greece
Interests: biomedical signal processing; clinical engineering; image processing
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Dr. Athanasios Anastasiou

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Biomedical Engineering Laboratory, School of Electrical Engineering, National Technical University of Athens, 10682 Athens, Greece
Interests: M-health; E-health; biomedical engineering; software engineering
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Special Issue Information

Dear Colleagues,

AI in healthcare is an umbrella term to describe the application of machine learning (ML) algorithms and other cognitive technologies in medical settings. In the simplest sense, AI is when computers and other machines mimic human cognition and are capable of learning, thinking, and making decisions or taking actions. AI in healthcare, then, is the use of machines to analyze and act on medical data, usually with the goal of predicting a particular outcome.
A significant AI use case in healthcare is the use of ML and other cognitive disciplines for medical diagnosis purposes. Using patient data and other information, AI can help doctors and medical providers to deliver more accurate diagnoses and treatment plans. Further, AI can help to make healthcare more predictive and proactive by analyzing big data to develop improved preventive care recommendations for patients.

Healthcare is one of the most critical sectors in the broader landscape of big data because of its fundamental role in a productive, thriving society. The application of AI to healthcare data can literally be a matter of life and death. AI can assist doctors, nurses, and other healthcare workers in their daily work. AI in healthcare can enhance preventive care and quality of life, produce more accurate diagnoses and treatment plans, and lead to better patient outcomes overall. AI can also predict and track the spread of infectious diseases by analyzing data from a government, healthcare, and other sources. As a result, AI can play a crucial role in global public health as a tool for combatting epidemics and pandemics.

Prof. Dr. Dimitris Dionissios Koutsouris
Dr. Athanasios Anastasiou
Guest Editors

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Keywords

NLP
ML
patient data
healthcare

Published Papers (14 papers)

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20 pages, 536 KiB

Open AccessArticle

Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights

by Eugenia Ipar, Leandro J. Cymberknop and Ricardo L. Armentano

Appl. Sci. 2023, 13(19), 10585; https://doi.org/10.3390/app131910585 - 22 Sep 2023

Viewed by 918

Abstract

With aging being a major non-reversible risk factor for cardiovascular disease, the concept of Vascular Age (VA) emerges as a promising alternate measure to assess an individual’s cardiovascular risk and overall health. This study investigated the use of frequency features and Supervised Learning (SL) models for estimating a VA Age-Group (VAAG), as a surrogate of Chronological Age (CHA). Frequency features offer an accessible alternative to temporal and amplitude features, reducing reliance on high sampling frequencies and complex algorithms. Simulated subjects from One-dimensional models were employed to train SL algorithms, complemented with healthy in vivo subjects. Validation with real-world subject data was emphasized to ensure model applicability, using well-known risk factors as a form of cardiovascular health analysis and verification. Random Forest (RF) proved to be the best-performing model, achieving an accuracy/AUC score of 66.5%/0.59 for the in vivo test dataset, and 97.5%/0.99 for the in silico one. This research contributed to preventive medicine strategies, supporting early detection and personalized risk assessment for improved cardiovascular health outcomes across diverse populations. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare)

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11 pages, 3707 KiB

Open AccessArticle

Detection of Elbow OCD in the Ultrasound Image by Artificial Intelligence Using YOLOv8

by Atsuyuki Inui, Yutaka Mifune, Hanako Nishimoto, Shintaro Mukohara, Sumire Fukuda, Tatsuo Kato, Takahiro Furukawa, Shuya Tanaka, Masaya Kusunose, Shunsaku Takigami, Yutaka Ehara and Ryosuke Kuroda

Appl. Sci. 2023, 13(13), 7623; https://doi.org/10.3390/app13137623 - 28 Jun 2023

Cited by 3 | Viewed by 2886

Abstract

Background: Screening for elbow osteochondritis dissecans (OCD) using ultrasound (US) is essential for early detection and successful conservative treatment. The aim of the study is to determine the diagnostic accuracy of YOLOv8, a deep-learning-based artificial intelligence model, for US images of OCD or normal elbow-joint images. Methods: A total of 2430 images were used. Using the YOLOv8 model, image classification and object detection were performed to recognize OCD lesions or standard views of normal elbow joints. Results: In the binary classification of normal and OCD lesions, the values from the confusion matrix were the following: Accuracy = 0.998, Recall = 0.9975, Precision = 1.000, and F-measure = 0.9987. The mean average precision (mAP) comparing the bounding box detected by the trained model with the true-label bounding box was 0.994 in the YOLOv8n model and 0.995 in the YOLOv8m model. Conclusions: The YOLOv8 model was trained for image classification and object detection of standard views of elbow joints and OCD lesions. Both tasks were able to be achieved with high accuracy and may be useful for mass screening at medical check-ups for baseball elbow. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare)

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22 pages, 1832 KiB

Open AccessArticle

Recent Advances of Artificial Intelligence in Healthcare: A Systematic Literature Review

by Fotis Kitsios, Maria Kamariotou, Aristomenis I. Syngelakis and Michael A. Talias

Appl. Sci. 2023, 13(13), 7479; https://doi.org/10.3390/app13137479 - 25 Jun 2023

Cited by 4 | Viewed by 7618

Abstract

The implementation of artificial intelligence (AI) is driving significant transformation inside the administrative and clinical workflows of healthcare organizations at an accelerated rate. This modification highlights the significant impact that AI has on a variety of tasks, especially in health procedures relating to early detection and diagnosis. Papers done in the past imply that AI has the potential to increase the overall quality of services provided in the healthcare industry. There have been reports that technology based on AI can improve the quality of human existence by making life simpler, safer, and more productive. A comprehensive analysis of previous scholarly research on the use of AI in the health area is provided in this research in the form of a literature review. In order to propose a classification framework, the review took into consideration 132 academic publications sourced from scholarly sources. The presentation covers both the benefits and the issues that AI capabilities provide for individuals, medical professionals, corporations, and the health industry. In addition, the social and ethical implications of AI are examined in the context of the output of value-added medical services for decision-making processes in healthcare, privacy and security measures for patient data, and health monitoring capabilities. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare)

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11 pages, 901 KiB

Open AccessArticle

A Medical Image Visualization Technique Assisted with AI-Based Haptic Feedback for Robotic Surgery and Healthcare

by Georgios M. Minopoulos, Vasileios A. Memos, Konstantinos D. Stergiou, Christos L. Stergiou and Konstantinos E. Psannis

Appl. Sci. 2023, 13(6), 3592; https://doi.org/10.3390/app13063592 - 11 Mar 2023

Cited by 9 | Viewed by 3856

Abstract

A lesson learned during the pandemic is that social distancing saves lives. As it was shown recently, the healthcare industry is structured in a way that cannot protect medical staff from possible infectious diseases, such as COVID-19. Today’s healthcare services seem anachronistic and not convenient for both doctors and patients. Although there have been several advances in recent years, especially in developed countries, the need for a holistic change is imperative. Evidently, future technologies should be introduced in the health sector, where Virtual Reality, Augmented Reality, Artificial Intelligence, and Tactile Internet can have vast applications. Thus, the healthcare industry could take advantage of the great evolution of pervasive computing. In this paper, we point out the challenges from the current visualization techniques and present a novel visualization technique assisted with haptics which is enhanced with artificial intelligent algorithms in order to offer remote patient examination and treatment through robotics. Such an approach provides a more detailed method of medical image data visualization and eliminates the possibility of diseases spreading, while reducing the workload of the medical staff. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare)

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11 pages, 1645 KiB

Open AccessArticle

Cognitive Reorganization Due to Mental Workload: A Functional Connectivity Analysis Based on Working Memory Paradigms

by Georgios N. Dimitrakopoulos, Ioannis Kakkos, Athanasios Anastasiou, Anastasios Bezerianos, Yu Sun and George K. Matsopoulos

Appl. Sci. 2023, 13(4), 2129; https://doi.org/10.3390/app13042129 - 07 Feb 2023

Cited by 3 | Viewed by 1527

Abstract

Mental workload has a major effect on the individual’s performance in most real-world tasks, which can lead to significant errors in critical operations. On this premise, the analysis and assessment of mental workload attain high research interest in both the fields of Neuroergonomics and Neuroscience. In this work, we implemented an EEG experimental design consisting of two distinct mental tasks (mental arithmetic task, n-back task), each with two conditions of complexity (low and high) to investigate the task-related and task-unrelated workload effects. Since mental workload is an intricate phenomenon involving multiple brain areas, we performed a graph theoretical analysis estimating the Phase Locking Index (PLI) in four frequency bands (delta, theta, alpha, beta). The brainwave-dependent network results show statistically significant reductions in clustering coefficient, characteristic path length, and small-worldness metrics with higher workload in both tasks across several bands. Moreover, functional connectivity analysis indicates a task-independent fashion of the brain topological re-organization with increasing mental load. These results revealed how the brain network is re-organized with increasing mental workload in a task-independent way. Finally, the network metrics were used as classification features, leading to high performance in workload level discrimination. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare)

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23 pages, 2745 KiB

Open AccessArticle

Cervical Cancer Diagnosis Based on Multi-Domain Features Using Deep Learning Enhanced by Handcrafted Descriptors

by Omneya Attallah

Appl. Sci. 2023, 13(3), 1916; https://doi.org/10.3390/app13031916 - 02 Feb 2023

Cited by 20 | Viewed by 2467

Abstract

Cervical cancer, among the most frequent adverse cancers in women, could be avoided through routine checks. The Pap smear check is a widespread screening methodology for the timely identification of cervical cancer, but it is susceptible to human mistakes. Artificial Intelligence-reliant computer-aided diagnostic (CAD) methods have been extensively explored to identify cervical cancer in order to enhance the conventional testing procedure. In order to attain remarkable classification results, most current CAD systems require pre-segmentation steps for the extraction of cervical cells from a pap smear slide, which is a complicated task. Furthermore, some CAD models use only hand-crafted feature extraction methods which cannot guarantee the sufficiency of classification phases. In addition, if there are few data samples, such as in cervical cell datasets, the use of deep learning (DL) alone is not the perfect choice. In addition, most existing CAD systems obtain attributes from one domain, but the integration of features from multiple domains usually increases performance. Hence, this article presents a CAD model based on extracting features from multiple domains not only one domain. It does not require a pre-segmentation process thus it is less complex than existing methods. It employs three compact DL models to obtain high-level spatial deep features rather than utilizing an individual DL model with large number of parameters and layers as used in current CADs. Moreover, it retrieves several statistical and textural descriptors from multiple domains including spatial and time–frequency domains instead of employing features from a single domain to demonstrate a clearer representation of cervical cancer features, which is not the case in most existing CADs. It examines the influence of each set of handcrafted attributes on diagnostic accuracy independently and hybrid. It then examines the consequences of combining each DL feature set obtained from each CNN with the combined handcrafted features. Finally, it uses principal component analysis to merge the entire DL features with the combined handcrafted features to investigate the effect of merging numerous DL features with various handcrafted features on classification results. With only 35 principal components, the accuracy achieved by the quatric SVM of the proposed CAD reached 100%. The performance of the described CAD proves that combining several DL features with numerous handcrafted descriptors from multiple domains is able to boost diagnostic accuracy. Additionally, the comparative performance analysis, along with other present studies, shows the competing capacity of the proposed CAD. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare)

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13 pages, 2385 KiB

Open AccessArticle

Applying Neural Networks with Time-Frequency Features for the Detection of Mental Fatigue

by Ioannis Zorzos, Ioannis Kakkos, Stavros T. Miloulis, Athanasios Anastasiou, Errikos M. Ventouras and George K. Matsopoulos

Appl. Sci. 2023, 13(3), 1512; https://doi.org/10.3390/app13031512 - 23 Jan 2023

Cited by 9 | Viewed by 1737

Abstract

The detection of mental fatigue is an important issue in the nascent field of neuroergonomics. Although machine learning approaches and especially deep learning designs have constantly demonstrated their efficiency to automatically detect critical features from raw data, the computational resources for training and predictions are usually very demanding. In this work, we propose a shallow convolutional neural network, with three convolutional layers, for fatigue detection using electroencephalogram (EEG) data that can alleviate the computational burden and provide fast mental fatigue detection. As such, a deep learning model was created utilizing time-frequency domain features, extracted with Morlet wavelet analysis. These features, combined with the higher-level characteristics learnt by the model, resulted in a resilient solution, able to attain very high prediction accuracy (97%), while reducing training time and computing costs. Moreover, by incorporating a subsequent SHAP values analysis on the characteristics that contributed in the model creation, indications of low frequency (theta and alpha band) brain wave characteristics were indicated as prominent mental fatigue detectors. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare)

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22 pages, 3545 KiB

Open AccessFeature PaperArticle

Cervical Cancer Diagnostics Using Machine Learning Algorithms and Class Balancing Techniques

by Matko Glučina, Ariana Lorencin, Nikola Anđelić and Ivan Lorencin

Appl. Sci. 2023, 13(2), 1061; https://doi.org/10.3390/app13021061 - 12 Jan 2023

Cited by 6 | Viewed by 3381

Abstract

Objectives: Cervical cancer is present in most cases of squamous cell carcinoma. In most cases, it is the result of an infection with human papillomavirus or adenocarcinoma. This type of cancer is the third most common cancer of the female reproductive organs. The risk groups for cervical cancer are mostly younger women who frequently change partners, have early sexual intercourse, are infected with human papillomavirus (HPV), and who are nicotine addicts. In most cases, the cancer is asymptomatic until it has progressed to the later stages. Cervical cancer screening rates are low, especially in developing countries and in some minority groups. Due to these facts, the introduction of a tentative cervical cancer screening based on a questionnaire can enable more diagnoses of cervical cancer in the initial stages of the disease. Methods: In this research, publicly available cervical cancer data collected on 859 female patients are used. Each sample consists of 36 input attributes and four different outputs Hinselmann, Schiller, cytology, and biopsy. Due to the significant unbalance of the data set, class balancing techniques were used, and these are the Synthetic Minority Oversampling Technique, the ADAptive SYNthetic algorithm (ADASYN), SMOTEEN, random oversampling, and SMOTETOMEK. To obtain the mentioned target outputs, multiple artificial intelligence (AI) and machine learning (ML) methods are proposed. In this research, multiple classification algorithms such as logistic regression, multilayer perceptron (MLP), support vector machine (SVM), K-nearest neighbors (KNN), and several naive Bayes methods were used. Results: From the achieved results, it can be seen that the highest performances were achieved if MLP and KNN are used in combination with Random oversampling, SMOTEEN, and SMOTETOMEK. Such an approach has resulted in mean area under the receiver operating characteristic curve (

\bar{A U C}

) and mean Matthew’s correlation coefficient (

\bar{M C C}

) scores of higher than

0.95

, regardless of which diagnostic method was used for output vector construction. Conclusions: According to the presented results, it can be concluded that there is a possibility for the utilization of artificial intelligence (AI) and machine learning (ML) techniques for the development of a tentative cervical cancer screening method, which is based on a questionnaire and an AI-based algorithm. Furthermore, it can be concluded that by using class balancing techniques, a certain performance boost can be achieved. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare)

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12 pages, 4030 KiB

Open AccessCommunication

FACES: A Deep-Learning-Based Parametric Model to Improve Rosacea Diagnoses

by Seungman Park, Anna L. Chien, Beiyu Lin and Keva Li

Appl. Sci. 2023, 13(2), 970; https://doi.org/10.3390/app13020970 - 11 Jan 2023

Cited by 2 | Viewed by 1954

Abstract

Rosacea is a chronic inflammatory skin disorder that causes visible blood vessels and redness on the nose, chin, cheeks, and forehead. However, visual assessment, the current standard method used to identify rosacea, is often subjective among clinicians and results in high variation. Recent advances in artificial intelligence have allowed for the effective detection of various skin diseases with high accuracy and consistency. In this study, we develop a new methodology, coined “five accurate CNNs-based evaluation system (FACES)”, to identify and classify rosacea more efficiently. First, 19 CNN-based models that have been widely used for image classification were trained and tested via training and validation data sets. Next, the five best performing models were selected based on accuracy, which served as a weight value for FACES. At the same time, we also applied a majority rule to five selected models to detect rosacea. The results exhibited that the performance of FACES was superior to that of the five individual CNN-based models and the majority rule in terms of accuracy, sensitivity, specificity, and precision. In particular, the accuracy and sensitivity of FACES were the highest, and the specificity and precision were higher than most of the individual models. To improve the performance of our system, future studies must consider patient details, such as age, gender, and race, and perform comparison tests between our model system and clinicians. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare)

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21 pages, 7087 KiB

Open AccessArticle

Retinal Nerve Fiber Layer Analysis Using Deep Learning to Improve Glaucoma Detection in Eye Disease Assessment

by Alifia Revan Prananda, Eka Legya Frannita, Augustine Herini Tita Hutami, Muhammad Rifqi Maarif, Norma Latif Fitriyani and Muhammad Syafrudin

Appl. Sci. 2023, 13(1), 37; https://doi.org/10.3390/app13010037 - 20 Dec 2022

Cited by 5 | Viewed by 2061

Abstract

Recently, the development of a rapid detection approach for glaucoma has been widely proposed to assist medical personnel in detecting glaucoma disease thanks to the outstanding performance of artificial intelligence. In several glaucoma detectors, cup-to-disc ratio (CDR) and disc damage likelihood scale (DDLS) play roles as the major objects that are used to analyze glaucoma. However, using CDR and DDLS is quite difficult since every person has different characteristics (shape, size, etc.) of the optic disc and optic cup. To overcome this issue, we proposed an alternative way to detect glaucoma disease by analyzing the damage to the retinal nerve fiber layer (RNFL). Our proposed method is divided into two processes: (1) the pre-treatment process and (2) the glaucoma classification process. We started the pre-treatment process by removing unnecessary parts, such as the optic disc and blood vessels. Both parts are considered for removal since they might be obstacles during the analysis process. For the classification stages, we used nine deep-learning architectures. We evaluated our proposed method in the ORIGA dataset and achieved the highest accuracy of 92.88% with an AUC of 89.34%. This result is improved by more than 15% from the previous research work. Finally, it is expected that our model could help improve eye disease diagnosis and assessment. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare)

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13 pages, 1720 KiB

Open AccessArticle

Machine-Learning-Based COVID-19 and Dyspnoea Prediction Systems for the Emergency Department

by Marco La Salvia, Emanuele Torti, Gianmarco Secco, Carlo Bellazzi, Francesco Salinaro, Paolo Lago, Giovanni Danese, Stefano Perlini and Francesco Leporati

Appl. Sci. 2022, 12(21), 10869; https://doi.org/10.3390/app122110869 - 26 Oct 2022

Cited by 1 | Viewed by 1212

Abstract

The COVID-19 pandemic highlighted an urgent need for reliable diagnostic tools to minimize viral spreading. It is mandatory to avoid cross-contamination between patients and detect COVID-19 positive individuals to cluster people by prognosis and manage the emergency department’s resources. Fondazione IRCCS Policlinico San Matteo Hospital’s Emergency Department (ED) of Pavia let us evaluate the exploitation of machine learning algorithms on a clinical dataset gathered from laboratory-confirmed rRT-PCR test patients, collected from 1 March to 30 June 2020. Physicians examined routine blood tests, clinical history, symptoms, arterial blood gas (ABG) analysis, and lung ultrasound quantitative examination. We developed two diagnostic tools for COVID-19 detection and oxygen therapy prediction, namely, the need for ventilation support due to lung involvement. We obtained promising classification results with F1 score levels meeting 92%, and we also engineered a user-friendly interface for healthcare providers during daily screening operations. This research proved machine learning models as a potential screening methodology during contingency times. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare)

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14 pages, 778 KiB

Open AccessArticle

Smart Scalable ML-Blockchain Framework for Large-Scale Clinical Information Sharing

by Anand Singh Rajawat, S. B. Goyal, Pradeep Bedi, Simeon Simoff, Tony Jan and Mukesh Prasad

Appl. Sci. 2022, 12(21), 10795; https://doi.org/10.3390/app122110795 - 25 Oct 2022

Cited by 1 | Viewed by 1437

Abstract

Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces an innovative blockchain-based electronic healthcare system that incorporates synchronous data backup and a highly encrypted data-sharing mechanism. Blockchain technology, which eliminates centralized organizations and reduces the number of fragmented patient files, could make it easier to use machine learning (ML) models for predictive diagnosis and analysis. In turn, it might lead to better medical care. The proposed model achieved an improved patient-centered CIS by personalizing the separation of information with an intelligent ”allowed list“ for clinician data access. This work introduces a hybrid ML-blockchain solution that combines traditional data storage and blockchain-based access. The experimental analysis evaluated the proposed model against the competing models in comparative and quantitative studies in large-scale CIS examples in terms of model viability, stability, protection, and robustness, with improved results. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare)

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9 pages, 3402 KiB

Open AccessArticle

Convolutional Neural Network Algorithm Trained with Anteroposterior Radiographs to Diagnose Pre-Collapse Osteonecrosis of the Femoral Head

by Jeoung Kun Kim, Gyu-Sik Choi, Seong Yeob Kwak and Min Cheol Chang

Appl. Sci. 2022, 12(19), 9606; https://doi.org/10.3390/app12199606 - 24 Sep 2022

Cited by 1 | Viewed by 1437

Abstract

A convolutional neural network (CNN) is a representative deep-learning algorithm that has a significant advantage in image recognition and classification. Using anteroposterior pelvic radiographs as input data, we developed a CNN algorithm to determine the presence of pre-collapse osteonecrosis of the femoral head (ONFH). We developed a CNN algorithm to differentiate between ONFH and normal radiographs. We retrospectively included 305 anteroposterior pelvic radiographs (right hip: pre-collapsed ONFH = 79, normal = 226; left hip: pre-collapsed ONFH = 62, normal = 243) as data samples. Pre-collapsed ONFH was diagnosed using pelvic magnetic resonance imaging data for each patient. Among the 305 cases, 69.8% of the included data samples were randomly selected as the training set, 21.0% were selected as the validation set, and the remaining 9.2% were selected as the test set to evaluate the performance of the developed CNN algorithm. The area under the curve of our developed CNN algorithm on the test data was 0.912 (95% confidence interval, 0.773–1.000) for the right hip and 0.902 (95% confidence interval, 0.747–1.000) for the left hip. We showed that a CNN algorithm trained using pelvic radiographs would help diagnose pre-collapse ONFH. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare)

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15 pages, 1027 KiB

Open AccessSystematic Review

Cutting-Edge Technologies for Digital Therapeutics: A Review and Architecture Proposals for Future Directions

by Joo Hun Yoo, Harim Jeong and Tai-Myoung Chung

Appl. Sci. 2023, 13(12), 6929; https://doi.org/10.3390/app13126929 - 08 Jun 2023

Cited by 4 | Viewed by 2724

Abstract

Digital therapeutics, evidence-based treatments delivered through software programs, are revolutionizing healthcare by utilizing cutting-edge computing technologies. Unlike conventional medical treatment methods, digital therapeutics are based on multiple information technologies, from data collection to analysis algorithms, and treatment support approaches. In this research, we provide a comprehensive overview of the latest technologies involved in the development of digital therapeutics and highlight specific technologies necessary for the future growth of the rapidly evolving digital therapeutics market. Furthermore, we present a system design of digital therapeutics for depression, currently being developed by our research team, to provide a detailed explanation of the technical process. Digital therapeutics require various technical supports, such as collecting user data in a security-enhanced medical environment, processing and analyzing the collected data, and providing personalized treatment methods to the user. The findings from this research will enable digital therapeutic companies to enhance their product performance, consequently bolstering their market competitiveness. Additionally, the research can be further extended to explore applicable methodologies at different stages of digital therapeutic environments. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare)

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