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Healthcare
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Application Prospect of Artificial Intelligence in Healthcare
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Application Prospect of Artificial Intelligence in Healthcare

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: closed (30 May 2024) | Viewed by 42986

Special Issue Editor

Dr. Pejman Rasti

E-Mail Website
Guest Editor
1. LARIS, UMR INRAE IRHS, Université d’Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France
2. ESAIP-École Supérieure Angevine en Informatique et Productique, 49180 Angers, France
Interests: deep learning for MRI/fMRI brain data; big data for healthcare; AI in neuroscience

Special Issue Information

Dear Colleagues,

As we venture into the transformative landscape of AI in healthcare, the crucial role of outcome measures in this emergent field becomes evident. Notably, in the same way they are employed in rehabilitation to evaluate patient conditions before and after interventions, these metrics are of substantial value in AI-driven healthcare scenarios.

Nevertheless, the healthcare landscape presents an array of these tools, varying significantly across different national and cultural contexts. Although this diversity caters to the broad needs of the healthcare sector, it also presents challenges as the resulting lack of standardized outcome assessment may hinder comparative research and meta-analysis.

Addressing this concern, our Special Issue, "Application Prospect of Artificial Intelligence in Healthcare", striving towards universally accepted measures, aims to publish high-quality comparative studies and meta-analyses of AI-driven trials, catalysing advancements in clinical practice and research.

We are pleased to invite you to engage with our Special Issue. Our goal with this collection is to underscore the transformative impact of artificial intelligence across multiple healthcare domains. This Special Issue's scope is in harmony with the extensive focus areas of our journal, embracing topics from advanced inpatient care to chronic care, experimental medicine, and beyond.

We intend to explore AI's potential in optimizing diagnostic procedures, medication management, disease prevention, and enabling early diagnosis. Further, we will examine AI's capacity to enhance emergency, perioperative, and intensive care, including its application in medical imaging and monitoring. We also aim to probe the implications of AI on health policy, mental health, and prevention strategies, among other areas.

Our vision for this Special Issue strikes a balance between offering a comprehensive view of AI's role in healthcare and maintaining focus on specific practical applications that fall within our journal's scope. We aspire to stimulate insightful discussions that align with our journal's commitment to promoting knowledge and innovation in healthcare.

This Special Issue aims to solicit articles showcasing a plethora of themes and diverse methodologies. We welcome research papers and case studies on AI's role in diagnostics, treatment, and patient care management, as well as review articles evaluating the challenges and future trends of AI in healthcare. Articles discussing the socio-economic aspects of AI in healthcare are highly encouraged. By providing a platform for these myriad perspectives, we aim to foster a broad yet focused dialogue around the application prospects of AI in healthcare.

In this Special Issue, original research articles and reviews are welcome. Research areas may include the following:

Artificial intelligence in healthcare; predictive diagnostics; AI-driven patient care management; health informatics; AI in chronic care; AI in medical imaging; MRI in AI applications; machine learning in medicine; AI in disease prevention; and drug discovery.

We look forward to receiving your contributions.

Dr. Pejman Rasti
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. Healthcare 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 2700 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 in healthcare
  • predictive diagnostics
  • AI-driven patient care management
  • health informatics
  • AI in chronic care
  • AI in medical imaging
  • MRI in AI applications
  • machine learning in medicine
  • AI in disease prevention
  • drug discovery

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

Published Papers (3 papers)

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Research

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11 pages, 240 KiB  
Article
Effects of Artificial Intelligence on Surgical Patients’ Health Education
by Hsin-Shu Huang and Hsin-Yuan Fang
Healthcare 2023, 11(20), 2705; https://doi.org/10.3390/healthcare11202705 - 10 Oct 2023
Cited by 1 | Viewed by 3331
Abstract
Today, the various abilities that nurses require to meet patients’ healthcare needs adequately are all affected by AI-enabled systems. This research used an experimental study design in which 60 subjects were randomly assigned to either an experimental (AI image e-book guidance) group or [...] Read more.
Today, the various abilities that nurses require to meet patients’ healthcare needs adequately are all affected by AI-enabled systems. This research used an experimental study design in which 60 subjects were randomly assigned to either an experimental (AI image e-book guidance) group or a control (text paper guidance) group after meeting the admission conditions and agreeing to participate in the study. It was proven that providing AI image e-book guidance before surgery significantly changed the behavior of patients and promoted relief of urinary catheter discomfort through self-efficacy to reduce urinary catheter pain after surgery (p < 0.001). It was found that providing AI image e-book guidance can shorten the time for health education and provide patients with repeated medical education and familiarity with health guidance, which can help to address the important clinical service demand issue and the shortage of nursing staff. Full article
(This article belongs to the Special Issue Application Prospect of Artificial Intelligence in Healthcare)

Review

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22 pages, 2030 KiB  
Review
Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature
by Vivian Schmeis Arroyo, Marco Iosa, Gabriella Antonucci and Daniela De Bartolo
Healthcare 2024, 12(7), 781; https://doi.org/10.3390/healthcare12070781 - 3 Apr 2024
Cited by 2 | Viewed by 2520
Abstract
Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus [...] Read more.
Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy of the used models in the prediction of male infertility as a primary outcome. Particular attention will be paid to the use of artificial neural networks (ANNs). A comprehensive literature search was conducted in PubMed, Scopus, and Science Direct between 15 July and 23 October 2023, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a quality assessment of the included studies using the recommended tools suggested for the type of study design adopted. We also made a screening of the Risk of Bias (RoB) associated with the included studies. Thus, 43 relevant publications were included in this review, for a total of 40 different ML models detected. The studies included reported a good quality, even if RoB was not always good for all the types of studies. The included studies reported a median accuracy of 88% in predicting male infertility using ML models. We found only seven studies using ANN models for male infertility prediction, reporting a median accuracy of 84%. Full article
(This article belongs to the Special Issue Application Prospect of Artificial Intelligence in Healthcare)
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31 pages, 685 KiB  
Review
Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives
by Molly Bekbolatova, Jonathan Mayer, Chi Wei Ong and Milan Toma
Healthcare 2024, 12(2), 125; https://doi.org/10.3390/healthcare12020125 - 5 Jan 2024
Cited by 103 | Viewed by 35754
Abstract
Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The [...] Read more.
Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance. Full article
(This article belongs to the Special Issue Application Prospect of Artificial Intelligence in Healthcare)
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