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Artificial Intelligence in Healthcare: Current State and Future Perspectives
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Special Issue "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: 30 September 2023 | Viewed by 10659

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 quarterly 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 1200 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

Published Papers (4 papers)

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Research

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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 - 02 Mar 2023
Cited by 6 | Viewed by 4126
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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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 1 | Viewed by 1742
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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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
Viewed by 2341
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

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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
Viewed by 944
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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