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Artificial Intelligence in Healthcare: Current State and Future Perspectives
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Artificial Intelligence in Healthcare: Current State and Future Perspectives

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "Medical & Healthcare AI".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 84971

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Dr. Tim Hulsen

E-Mail Website
Guest Editor
Hospital Services & Informatics, Philips Research, Eindhoven, The Netherlands
Interests: bioinformatics; oncology; data science; data management; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is having a major impact on healthcare. While advances in the sharing and analysis of medical data result in better and earlier diagnoses and more patient-tailored treatments, data management is also affected by trends such as increased patient-centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The way in which health services are delivered is being revolutionized through the sharing and integration of health data across organizational boundaries. Via AI, researchers can provide new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at an individual and population level.

This Special Issue focuses on how AI is used in healthcare, and on related topics such as data management, data integration, data sharing, patient privacy and bioethical issues. For example, AI is gaining a leading role in data processing supporting clinical practice, but AI-based decisions might be biased. The increasing use of AI in healthcare provides many new and interesting possibilities, but also causes issues around trust (the “black box” problem) and privacy. This Special Issue intends to show how AI will impact healthcare and discuss both advantages and disadvantages, as well as what solutions there are to solve potential problems.

This Special Issue especially welcomes contributions that address (one of) the following topics:

  • AI algorithms aimed at improving healthcare;
  • Data management or data integration in AI healthcare applications, including current, emerging and future applications (e.g., medical visualization) and the FAIR Guiding Principles for scientific data management and stewardship;
  • Use of devices beyond the traditional healthcare system to aid data collection (e.g., wearables);
  • Barriers to the application of AI in healthcare, such as data bias from under-represented populations as well as policies around data sharing and open-access vs. proprietary platforms;
  • Regulatory, legal, and ethical issues related to using of AI such as data governance, data protection, privacy, and bioethics (e.g., GDPR);
  • Distributed learning and use of federated data systems.

Dr. Tim Hulsen
Guest Editor

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. AI is an international peer-reviewed open access monthly 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 1600 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

  • artificial intelligence
  • healthcare
  • machine learning
  • data science
  • medicine

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Published Papers (11 papers)

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Editorial

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5 pages, 174 KiB  
Editorial
Artificial Intelligence in Healthcare: ChatGPT and Beyond
by Tim Hulsen
AI 2024, 5(2), 550-554; https://doi.org/10.3390/ai5020028 - 19 Apr 2024
Cited by 3 | Viewed by 4899
Abstract
Artificial intelligence (AI), the simulation of human intelligence processes by machines, is having a growing impact on healthcare [...] Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Current State and Future Perspectives)

Research

Jump to: Editorial, Review, Other

22 pages, 2007 KiB  
Article
New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification
by Md Easin Hasan and Amy Wagler
AI 2024, 5(1), 342-363; https://doi.org/10.3390/ai5010017 - 1 Feb 2024
Cited by 10 | Viewed by 3809
Abstract
Neuroimaging experts in biotech industries can benefit from using cutting-edge artificial intelligence techniques for Alzheimer’s disease (AD)- and dementia-stage prediction, even though it is difficult to anticipate the precise stage of dementia and AD. Therefore, we propose a cutting-edge, computer-assisted method based on [...] Read more.
Neuroimaging experts in biotech industries can benefit from using cutting-edge artificial intelligence techniques for Alzheimer’s disease (AD)- and dementia-stage prediction, even though it is difficult to anticipate the precise stage of dementia and AD. Therefore, we propose a cutting-edge, computer-assisted method based on an advanced deep learning algorithm to differentiate between people with varying degrees of dementia, including healthy, very mild dementia, mild dementia, and moderate dementia classes. In this paper, four separate models were developed for classifying different dementia stages: convolutional neural networks (CNNs) built from scratch, pre-trained VGG16 with additional convolutional layers, graph convolutional networks (GCNs), and CNN-GCN models. The CNNs were implemented, and then the flattened layer output was fed to the GCN classifier, resulting in the proposed CNN-GCN architecture. A total of 6400 whole-brain magnetic resonance imaging scans were obtained from the Alzheimer’s Disease Neuroimaging Initiative database to train and evaluate the proposed methods. We applied the 5-fold cross-validation (CV) technique for all the models. We presented the results from the best fold out of the five folds in assessing the performance of the models developed in this study. Hence, for the best fold of the 5-fold CV, the above-mentioned models achieved an overall accuracy of 43.83%, 71.17%, 99.06%, and 100%, respectively. The CNN-GCN model, in particular, demonstrates excellent performance in classifying different stages of dementia. Understanding the stages of dementia can assist biotech industry researchers in uncovering molecular markers and pathways connected with each stage. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Current State and Future Perspectives)
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18 pages, 3623 KiB  
Article
Convolutional Neural Networks in the Diagnosis of Colon Adenocarcinoma
by Marco Leo, Pierluigi Carcagnì, Luca Signore, Francesco Corcione, Giulio Benincasa, Mikko O. Laukkanen and Cosimo Distante
AI 2024, 5(1), 324-341; https://doi.org/10.3390/ai5010016 - 29 Jan 2024
Cited by 3 | Viewed by 2607
Abstract
Colorectal cancer is one of the most lethal cancers because of late diagnosis and challenges in the selection of therapy options. The histopathological diagnosis of colon adenocarcinoma is hindered by poor reproducibility and a lack of standard examination protocols required for appropriate treatment [...] Read more.
Colorectal cancer is one of the most lethal cancers because of late diagnosis and challenges in the selection of therapy options. The histopathological diagnosis of colon adenocarcinoma is hindered by poor reproducibility and a lack of standard examination protocols required for appropriate treatment decisions. In the current study, using state-of-the-art approaches on benchmark datasets, we analyzed different architectures and ensembling strategies to develop the most efficient network combinations to improve binary and ternary classification. We propose an innovative two-stage pipeline approach to diagnose colon adenocarcinoma grading from histological images in a similar manner to a pathologist. The glandular regions were first segmented by a transformer architecture with subsequent classification using a convolutional neural network (CNN) ensemble, which markedly improved the learning efficiency and shortened the learning time. Moreover, we prepared and published a dataset for clinical validation of the developed artificial neural network, which suggested the discovery of novel histological phenotypic alterations in adenocarcinoma sections that could have prognostic value. Therefore, AI could markedly improve the reproducibility, efficiency, and accuracy of colon cancer diagnosis, which are required for precision medicine to personalize the treatment of cancer patients. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Current State and Future Perspectives)
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21 pages, 7476 KiB  
Article
A Flower Pollination Algorithm-Optimized Wavelet Transform and Deep CNN for Analyzing Binaural Beats and Anxiety
by Devika Rankhambe, Bharati Sanjay Ainapure, Bhargav Appasani and Amitkumar V. Jha
AI 2024, 5(1), 115-135; https://doi.org/10.3390/ai5010007 - 29 Dec 2023
Cited by 1 | Viewed by 2277
Abstract
Binaural beats are a low-frequency form of acoustic stimulation that may be heard between 200 and 900 Hz and can help reduce anxiety as well as alter other psychological situations and states by affecting mood and cognitive function. However, prior research has only [...] Read more.
Binaural beats are a low-frequency form of acoustic stimulation that may be heard between 200 and 900 Hz and can help reduce anxiety as well as alter other psychological situations and states by affecting mood and cognitive function. However, prior research has only looked at the impact of binaural beats on state and trait anxiety using the STA-I scale; the level of anxiety has not yet been evaluated, and for the removal of artifacts the improper selection of wavelet parameters reduced the original signal energy. Hence, in this research, the level of anxiety when hearing binaural beats has been analyzed using a novel optimized wavelet transform in which optimized wavelet parameters are extracted from the EEG signal using the flower pollination algorithm, whereby artifacts are removed effectively from the EEG signal. Thus, EEG signals have five types of brainwaves in the existing models, which have not been analyzed optimally for brainwaves other than delta waves nor has the level of anxiety yet been analyzed using binaural beats. To overcome this, deep convolutional neural network (CNN)-based signal processing has been proposed. In this, deep features are extracted from optimized EEG signal parameters, which are precisely selected and adjusted to their most efficient values using the flower pollination algorithm, ensuring minimal signal energy reduction and artifact removal to maintain the integrity of the original EEG signal during analysis. These features provide the accurate classification of various levels of anxiety, which provides more accurate results for the effects of binaural beats on anxiety from brainwaves. Finally, the proposed model is implemented in the Python platform, and the obtained results demonstrate its efficacy. The proposed optimized wavelet transform using deep CNN-based signal processing outperforms existing techniques such as KNN, SVM, LDA, and Narrow-ANN, with a high accuracy of 0.99%, precision of 0.99%, recall of 0.99%, F1-score of 0.99%, specificity of 0.999%, and error rate of 0.01%. Thus, the optimized wavelet transform with a deep CNN can perform an effective decomposition of EEG data and extract deep features related to anxiety to analyze the effect of binaural beats on anxiety levels. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Current State and Future Perspectives)
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23 pages, 1296 KiB  
Article
Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction
by Lauren M. Paladino, Alexander Hughes, Alexander Perera, Oguzhan Topsakal and Tahir Cetin Akinci
AI 2023, 4(4), 1036-1058; https://doi.org/10.3390/ai4040053 - 1 Dec 2023
Cited by 15 | Viewed by 5230
Abstract
Globally, over 17 million people annually die from cardiovascular diseases, with heart disease being the leading cause of mortality in the United States. The ever-increasing volume of data related to heart disease opens up possibilities for employing machine learning (ML) techniques in diagnosing [...] Read more.
Globally, over 17 million people annually die from cardiovascular diseases, with heart disease being the leading cause of mortality in the United States. The ever-increasing volume of data related to heart disease opens up possibilities for employing machine learning (ML) techniques in diagnosing and predicting heart conditions. While applying ML demands a certain level of computer science expertise—often a barrier for healthcare professionals—automated machine learning (AutoML) tools significantly lower this barrier. They enable users to construct the most effective ML models without in-depth technical knowledge. Despite their potential, there has been a lack of research comparing the performance of different AutoML tools on heart disease data. Addressing this gap, our study evaluates three AutoML tools—PyCaret, AutoGluon, and AutoKeras—against three datasets (Cleveland, Hungarian, and a combined dataset). To evaluate the efficacy of AutoML against conventional machine learning methodologies, we crafted ten machine learning models using the standard practices of exploratory data analysis (EDA), data cleansing, feature engineering, and others, utilizing the sklearn library. Our toolkit included an array of models—logistic regression, support vector machines, decision trees, random forest, and various ensemble models. Employing 5-fold cross-validation, these traditionally developed models demonstrated accuracy rates spanning from 55% to 60%. This performance is markedly inferior to that of AutoML tools, indicating the latter’s superior capability in generating predictive models. Among AutoML tools, AutoGluon emerged as the superior tool, consistently achieving accuracy rates between 78% and 86% across the datasets. PyCaret’s performance varied, with accuracy rates from 65% to 83%, indicating a dependency on the nature of the dataset. AutoKeras showed the most fluctuation in performance, with accuracies ranging from 54% to 83%. Our findings suggest that AutoML tools can simplify the generation of robust ML models that potentially surpass those crafted through traditional ML methodologies. However, we must also consider the limitations of AutoML tools and explore strategies to overcome them. The successful deployment of high-performance ML models designed via AutoML could revolutionize the treatment and prevention of heart disease globally, significantly impacting patient care. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Current State and Future Perspectives)
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14 pages, 2069 KiB  
Article
Design of an Educational Chatbot Using Artificial Intelligence in Radiotherapy
by James C. L. Chow, Leslie Sanders and Kay Li
AI 2023, 4(1), 319-332; https://doi.org/10.3390/ai4010015 - 2 Mar 2023
Cited by 30 | Viewed by 11800
Abstract
Context: In cancer centres and hospitals particularly during the pandemic, there was a great demand for information, which could hardly be handled by the limited manpower available. This necessitated the development of an educational chatbot to disseminate topics in radiotherapy customized for [...] Read more.
Context: In cancer centres and hospitals particularly during the pandemic, there was a great demand for information, which could hardly be handled by the limited manpower available. This necessitated the development of an educational chatbot to disseminate topics in radiotherapy customized for various user groups, such as patients and their families, the general public and radiation staff. Objective: In response to the clinical demands, the objective of this work is to explore how to design a chatbot for educational purposes in radiotherapy using artificial intelligence. Methods: The chatbot is designed using the dialogue tree and layered structure, incorporated with artificial intelligence features such as natural language processing (NLP). This chatbot can be created in most platforms such as the IBM Watson Assistant and deposited in a website or various social media. Results: Based on the question-and-answer approach, the chatbot can provide humanlike communication to users requesting information on radiotherapy. At times, the user, often worried, may not be able to pinpoint the question exactly. Thus, the chatbot will be user friendly and reassuring, offering a list of questions for the user to select. The NLP system helps the chatbot to predict the intent of the user so as to provide the most accurate and precise response to him or her. It is found that the preferred educational features in a chatbot are functional features such as mathematical operations, which should be updated and modified routinely to provide new contents and features. Conclusions: It is concluded that an educational chatbot can be created using artificial intelligence to provide information transfer to users with different backgrounds in radiotherapy. In addition, testing and evaluating the performance of the chatbot is important, in response to user’s feedback to further upgrade and fine-tune the chatbot. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Current State and Future Perspectives)
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Review

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19 pages, 1720 KiB  
Review
AI and Face-Driven Orthodontics: A Scoping Review of Digital Advances in Diagnosis and Treatment Planning
by Juraj Tomášik, Márton Zsoldos, Ľubica Oravcová, Michaela Lifková, Gabriela Pavleová, Martin Strunga and Andrej Thurzo
AI 2024, 5(1), 158-176; https://doi.org/10.3390/ai5010009 - 5 Jan 2024
Cited by 15 | Viewed by 9917
Abstract
In the age of artificial intelligence (AI), technological progress is changing established workflows and enabling some basic routines to be updated. In dentistry, the patient’s face is a crucial part of treatment planning, although it has always been difficult to grasp in an [...] Read more.
In the age of artificial intelligence (AI), technological progress is changing established workflows and enabling some basic routines to be updated. In dentistry, the patient’s face is a crucial part of treatment planning, although it has always been difficult to grasp in an analytical way. This review highlights the current digital advances that, thanks to AI tools, allow us to implement facial features beyond symmetry and proportionality and incorporate facial analysis into diagnosis and treatment planning in orthodontics. A Scopus literature search was conducted to identify the topics with the greatest research potential within digital orthodontics over the last five years. The most researched and cited topic was artificial intelligence and its applications in orthodontics. Apart from automated 2D or 3D cephalometric analysis, AI finds its application in facial analysis, decision-making algorithms as well as in the evaluation of treatment progress and retention. Together with AI, other digital advances are shaping the face of today’s orthodontics. Without any doubts, the era of “old” orthodontics is at its end, and modern, face-driven orthodontics is on the way to becoming a reality in modern orthodontic practices. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Current State and Future Perspectives)
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13 pages, 3770 KiB  
Review
Chat GPT in Diagnostic Human Pathology: Will It Be Useful to Pathologists? A Preliminary Review with ‘Query Session’ and Future Perspectives
by Gerardo Cazzato, Marialessandra Capuzzolo, Paola Parente, Francesca Arezzo, Vera Loizzi, Enrica Macorano, Andrea Marzullo, Gennaro Cormio and Giuseppe Ingravallo
AI 2023, 4(4), 1010-1022; https://doi.org/10.3390/ai4040051 - 22 Nov 2023
Cited by 13 | Viewed by 7091
Abstract
The advent of Artificial Intelligence (AI) has in just a few years supplied multiple areas of knowledge, including in the medical and scientific fields. An increasing number of AI-based applications have been developed, among which conversational AI has emerged. Regarding the latter, ChatGPT [...] Read more.
The advent of Artificial Intelligence (AI) has in just a few years supplied multiple areas of knowledge, including in the medical and scientific fields. An increasing number of AI-based applications have been developed, among which conversational AI has emerged. Regarding the latter, ChatGPT has risen to the headlines, scientific and otherwise, for its distinct propensity to simulate a ‘real’ discussion with its interlocutor, based on appropriate prompts. Although several clinical studies using ChatGPT have already been published in the literature, very little has yet been written about its potential application in human pathology. We conduct a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, using PubMed, Scopus and the Web of Science (WoS) as databases, with the following keywords: ChatGPT OR Chat GPT, in combination with each of the following: pathology, diagnostic pathology, anatomic pathology, before 31 July 2023. A total of 103 records were initially identified in the literature search, of which 19 were duplicates. After screening for eligibility and inclusion criteria, only five publications were ultimately included. The majority of publications were original articles (n = 2), followed by a case report (n = 1), letter to the editor (n = 1) and review (n = 1). Furthermore, we performed a ‘query session’ with ChatGPT regarding pathologies such as pigmented skin lesions, malignant melanoma and variants, Gleason’s score of prostate adenocarcinoma, differential diagnosis between germ cell tumors and high grade serous carcinoma of the ovary, pleural mesothelioma and pediatric diffuse midline glioma. Although the premises are exciting and ChatGPT is able to co-advise the pathologist in providing large amounts of scientific data for use in routine microscopic diagnostic practice, there are many limitations (such as data of training, amount of data available, ‘hallucination’ phenomena) that need to be addressed and resolved, with the caveat that an AI-driven system should always provide support and never a decision-making motive during the histopathological diagnostic process. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Current State and Future Perspectives)
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15 pages, 1146 KiB  
Review
Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare
by Tim Hulsen
AI 2023, 4(3), 652-666; https://doi.org/10.3390/ai4030034 - 10 Aug 2023
Cited by 100 | Viewed by 20109
Abstract
Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks, and deep learning. AI can be applied in many [...] Read more.
Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks, and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce, and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors make better decisions (“clinical decision support”), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a “black box”, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at some central concepts in XAI, describe several challenges around XAI in healthcare, and discuss whether it can really help healthcare to advance, for example, by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Current State and Future Perspectives)
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24 pages, 2779 KiB  
Review
Machine-Learning-Based Prediction Modelling in Primary Care: State-of-the-Art Review
by Adham H. El-Sherbini, Hafeez Ul Hassan Virk, Zhen Wang, Benjamin S. Glicksberg and Chayakrit Krittanawong
AI 2023, 4(2), 437-460; https://doi.org/10.3390/ai4020024 - 23 May 2023
Cited by 9 | Viewed by 6584
Abstract
Primary care has the potential to be transformed by artificial intelligence (AI) and, in particular, machine learning (ML). This review summarizes the potential of ML and its subsets in influencing two domains of primary care: pre-operative care and screening. ML can be utilized [...] Read more.
Primary care has the potential to be transformed by artificial intelligence (AI) and, in particular, machine learning (ML). This review summarizes the potential of ML and its subsets in influencing two domains of primary care: pre-operative care and screening. ML can be utilized in preoperative treatment to forecast postoperative results and assist physicians in selecting surgical interventions. Clinicians can modify their strategy to reduce risk and enhance outcomes using ML algorithms to examine patient data and discover factors that increase the risk of worsened health outcomes. ML can also enhance the precision and effectiveness of screening tests. Healthcare professionals can identify diseases at an early and curable stage by using ML models to examine medical pictures, diagnostic modalities, and spot patterns that may suggest disease or anomalies. Before the onset of symptoms, ML can be used to identify people at an increased risk of developing specific disorders or diseases. ML algorithms can assess patient data such as medical history, genetics, and lifestyle factors to identify those at higher risk. This enables targeted interventions such as lifestyle adjustments or early screening. In general, using ML in primary care offers the potential to enhance patient outcomes, reduce healthcare costs, and boost productivity. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Current State and Future Perspectives)
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Other

9 pages, 517 KiB  
Commentary
Predictive Analytics with a Transdisciplinary Framework in Promoting Patient-Centric Care of Polychronic Conditions: Trends, Challenges, and Solutions
by Thomas T. H. Wan and Hunter S. Wan
AI 2023, 4(3), 482-490; https://doi.org/10.3390/ai4030026 - 13 Jul 2023
Cited by 7 | Viewed by 3104
Abstract
Context. This commentary is based on an innovative approach to the development of predictive analytics. It is centered on the development of predictive models for varying stages of chronic disease through integrating all types of datasets, adds various new features to a theoretically [...] Read more.
Context. This commentary is based on an innovative approach to the development of predictive analytics. It is centered on the development of predictive models for varying stages of chronic disease through integrating all types of datasets, adds various new features to a theoretically driven data warehousing, creates purpose-specific prediction models, and integrates multi-criteria predictions of chronic disease progression based on a biomedical evolutionary learning platform. After merging across-center databases based on the risk factors identified from modeling the predictors of chronic disease progression, the collaborative investigators could conduct multi-center verification of the predictive model and further develop a clinical decision support system coupled with visualization of a shared decision-making feature for patient care. The Study Problem. The success of health services management research is dependent upon the stability of pattern detection and the usefulness of nosological classification formulated from big-data-to-knowledge research on chronic conditions. However, longitudinal observations with multiple waves of predictors and outcomes are needed to capture the evolution of polychronic conditions. Motivation. The transitional probabilities could be estimated from big-data analysis with further verification. Simulation or predictive models could then generate a useful explanatory pathogenesis of the end-stage-disorder or outcomes. Hence, the clinical decision support system for patient-centered interventions could be systematically designed and executed. Methodology. A customized algorithm for polychronic conditions coupled with constraints-oriented reasoning approaches is suggested. Based on theoretical specifications of causal inquiries, we could mitigate the effects of multiple confounding factors in conducting evaluation research on the determinants of patient care outcomes. This is what we consider as the mechanism for avoiding the black-box expression in the formulation of predictive analytics. The remaining task is to gather new data to verify the practical utility of the proposed and validated predictive equation(s). More specifically, this includes two approaches guiding future research on chronic disease and care management: (1) To develop a biomedical evolutionary learning platform to predict the risk of polychronic conditions at various stages, especially for predicting the micro- and macro-cardiovascular complications experienced by patients with Type 2 diabetes for multidisciplinary care; and (2) to formulate appropriate prescriptive intervention services, such as patient-centered care management interventions for a high-risk group of patients with polychronic conditions. Conclusions. The commentary has identified trends, challenges, and solutions in conducting innovative AI-based healthcare research that can improve understandings of disease-state transitions from diabetes to other chronic polychronic conditions. Hence, better predictive models could be further formulated to expand from inductive (problem solving) to deductive (theory based and hypothesis testing) inquiries in care management research. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Current State and Future Perspectives)
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