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Artificial Intelligence Applied to Clinical Practice
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Artificial Intelligence Applied to Clinical Practice

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Omics/Informatics".

Deadline for manuscript submissions: closed (25 May 2024) | Viewed by 7303

Special Issue Editors

Prof. Dr. Bernard P. Veldkamp

E-Mail Website
Guest Editor
Cognition, Data and Education (CODE) Section, Learning, Data-Analytics and Technology Department, Faculty of Behavioural Management and Social Sciences (BMS), University of Twente, Enschede, The Netherlands
Interests: testing; learning; student; post-traumatic stress disorders; data mining; adaptive testing; students research
Dr. Maryam Amir Haeri

E-Mail Website
Guest Editor
Cognition, Data and Education (CODE) Section, Learning, Data-Analytics and Technology Department, Faculty of Behavioural Management and Social Sciences (BMS), University of Twente, Enschede, The Netherlands
Interests: machine learning; big data analytics; complex networks; soft computing
Dr. Stephanie M. Van den Berg

E-Mail Website
Guest Editor
Cognition, Data and Education (CODE) Section, Learning, Data-Analytics and Technology Department, Faculty of Behavioural Management and Social Sciences (BMS), University of Twente, Enschede, The Netherlands
Interests: data science; text mining

Special Issue Information

Dear Colleagues,

This Special Issue provides a platform to delve into the scientific background and highlight the importance of AI research in clinical practice. We welcome submissions that address the challenges and opportunities in applying AI to personalized medicine, emphasizing the potential impact on patient care, treatment outcomes, and healthcare delivery. We specifically encourage investigations into transparency, explainability, and fairness of AI models in healthcare decision-making.

The primary objective of this Special Issue is to foster a comprehensive understanding of the application of AI in clinical practice within the scope of the journal. We encourage contributors to explore topics such as AI-driven predictive modeling in clinical decision-making, AI applications for personalized treatment and drug discovery, and ethical considerations surrounding the trade-offs between accuracy and interpretability. Additionally, we invite submissions that explore the legal perspective of AI decision-making, the responsible deployment of algorithms, and challenges associated with imbalanced datasets and small sample sizes in healthcare.

We invite original research articles and reviews that address the following themes (but are not limited to):

  • AI-driven methods in clinical decision making: 
    • Treatment selection;
    • Optimization of treatment plans;
    • Targeted therapies.
  • AI-driven diagnosis and disease detection:
    • Early detection of cancer and other diseases;
    • AI-assisted diagnosis in specific medical conditions;
    • Computer-aided diagnostics using AI algorithms;
    • AI-based screening and early intervention.
  • Explainable AI in clinical practice: 
    • Methods for enhancing interpretability;
    • Approaches for transparency and explainability in AI models.
  • Challenges in healthcare AI applications:
    • Handling imbalanced datasets;
    • Strategies for small sample sizes in AI research; 
    • Overcoming bias and generalization issues in healthcare AI.
  • Ethical and legal perspectives in AI-driven clinical practice:
    • Ethical considerations in AI adoption;
    • Legal perspectives on AI decision making and accountability;
    • Ensuring patient privacy and data security in AI applications.

We look forward to receiving contributions that shed light on the transparency, explainability, and fairness of AI models in clinical practice, as well as exploring the legal and algorithmic perspectives in healthcare decision-making.

Prof. Dr. Bernard P. Veldkamp
Dr. Maryam Amir Haeri
Dr. Stephanie M. Van den Berg
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine 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 2600 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 (AI)
  • AI for Health
  • Clinical Decision Support Systems
  • Explainable AI
  • Personalized Healthcare

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

Published Papers (4 papers)

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Research

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32 pages, 1113 KiB  
Article
Analysis of Missingness Scenarios for Observational Health Data
by Alireza Zamanian, Henrik von Kleist, Octavia-Andreea Ciora, Marta Piperno, Gino Lancho and Narges Ahmidi
J. Pers. Med. 2024, 14(5), 514; https://doi.org/10.3390/jpm14050514 - 11 May 2024
Viewed by 999
Abstract
Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into the missing data methods. In this paper, we argue that the remedy is to [...] Read more.
Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into the missing data methods. In this paper, we argue that the remedy is to identify the key scenarios that lead to data missingness and investigate their theoretical implications. Based on this proposal, we first introduce an analysis framework where we investigate how different observation agents, such as physicians, influence the data availability and then scrutinize each scenario with respect to the steps in the missing data analysis. We apply this framework to the case study of observational data in healthcare facilities. We identify ten fundamental missingness scenarios and show how they influence the identification step for missing data graphical models, inverse probability weighting estimation, and exponential tilting sensitivity analysis. To emphasize how domain-informed analysis can improve method reliability, we conduct simulation studies under the influence of various missingness scenarios. We compare the results of three common methods in medical data analysis: complete-case analysis, Missforest imputation, and inverse probability weighting estimation. The experiments are conducted for two objectives: variable mean estimation and classification accuracy. We advocate for our analysis approach as a reference for the observational health data analysis. Beyond that, we also posit that the proposed analysis framework is applicable to other medical domains. Full article
(This article belongs to the Special Issue Artificial Intelligence Applied to Clinical Practice)
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14 pages, 1651 KiB  
Article
A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals
by Sarah Alshehri, Khalid A. Alahmari and Areej Alasiry
J. Pers. Med. 2024, 14(4), 354; https://doi.org/10.3390/jpm14040354 - 28 Mar 2024
Cited by 1 | Viewed by 1458
Abstract
The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize medical diagnostics, particularly in specialized fields such as Ear, Nose, and Throat (ENT) medicine. However, the successful adoption of AI-assisted diagnostic tools in ENT practice depends on the understanding of [...] Read more.
The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize medical diagnostics, particularly in specialized fields such as Ear, Nose, and Throat (ENT) medicine. However, the successful adoption of AI-assisted diagnostic tools in ENT practice depends on the understanding of various factors; these include influences on their effectiveness and acceptance among healthcare professionals. This cross-sectional study aimed to assess the usability and integration of AI tools in ENT practice, determine the clinical impact and accuracy of AI-assisted diagnostics in ENT, measure the trust and confidence of ENT professionals in AI tools, gauge the overall satisfaction and outlook on the future of AI in ENT diagnostics, and identify challenges, limitations, and areas for improvement in AI-assisted ENT diagnostics. A structured online questionnaire was distributed to 600 certified ENT professionals with at least one year of experience in the field. The questionnaire assessed participants’ familiarity with AI tools, usability, clinical impact, trust, satisfaction, and identified challenges. A total of 458 respondents completed the questionnaire, resulting in a response rate of 91.7%. The majority of respondents reported familiarity with AI tools (60.7%) and perceived them as generally usable and clinically impactful. However, challenges such as integration with existing systems, user-friendliness, accuracy, and cost were identified. Trust and satisfaction levels varied among participants, with concerns regarding data privacy and support. Geographic and practice setting differences influenced perceptions and experiences. The study highlights the diverse perceptions and experiences of ENT professionals regarding AI-assisted diagnostics. While there is general enthusiasm for these tools, challenges related to integration, usability, trust, and cost need to be addressed for their widespread adoption. These findings provide valuable insights for developers, policymakers, and healthcare providers aiming to enhance the role of AI in ENT practice. Full article
(This article belongs to the Special Issue Artificial Intelligence Applied to Clinical Practice)
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Review

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19 pages, 917 KiB  
Review
Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices
by Alessia Maccaro, Katy Stokes, Laura Statham, Lucas He, Arthur Williams, Leandro Pecchia and Davide Piaggio
J. Pers. Med. 2024, 14(5), 443; https://doi.org/10.3390/jpm14050443 - 23 Apr 2024
Cited by 2 | Viewed by 1663
Abstract
The use of AI in healthcare has sparked much debate among philosophers, ethicists, regulators and policymakers who raised concerns about the implications of such technologies. The presented scoping review captures the progression of the ethical and legal debate and the proposed ethical frameworks [...] Read more.
The use of AI in healthcare has sparked much debate among philosophers, ethicists, regulators and policymakers who raised concerns about the implications of such technologies. The presented scoping review captures the progression of the ethical and legal debate and the proposed ethical frameworks available concerning the use of AI-based medical technologies, capturing key themes across a wide range of medical contexts. The ethical dimensions are synthesised in order to produce a coherent ethical framework for AI-based medical technologies, highlighting how transparency, accountability, confidentiality, autonomy, trust and fairness are the top six recurrent ethical issues. The literature also highlighted how it is essential to increase ethical awareness through interdisciplinary research, such that researchers, AI developers and regulators have the necessary education/competence or networks and tools to ensure proper consideration of ethical matters in the conception and design of new AI technologies and their norms. Interdisciplinarity throughout research, regulation and implementation will help ensure AI-based medical devices are ethical, clinically effective and safe. Achieving these goals will facilitate successful translation of AI into healthcare systems, which currently is lagging behind other sectors, to ensure timely achievement of health benefits to patients and the public. Full article
(This article belongs to the Special Issue Artificial Intelligence Applied to Clinical Practice)
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Other

Jump to: Research, Review

15 pages, 908 KiB  
Brief Report
Artificial Intelligence in Scoliosis Classification: An Investigation of Language-Based Models
by Artur Fabijan, Bartosz Polis, Robert Fabijan, Krzysztof Zakrzewski, Emilia Nowosławska and Agnieszka Zawadzka-Fabijan
J. Pers. Med. 2023, 13(12), 1695; https://doi.org/10.3390/jpm13121695 - 9 Dec 2023
Cited by 2 | Viewed by 2442
Abstract
Open-source artificial intelligence models are finding free application in various industries, including computer science and medicine. Their clinical potential, especially in assisting diagnosis and therapy, is the subject of increasingly intensive research. Due to the growing interest in AI for diagnostics, we conducted [...] Read more.
Open-source artificial intelligence models are finding free application in various industries, including computer science and medicine. Their clinical potential, especially in assisting diagnosis and therapy, is the subject of increasingly intensive research. Due to the growing interest in AI for diagnostics, we conducted a study evaluating the abilities of AI models, including ChatGPT, Microsoft Bing, and Scholar AI, in classifying single-curve scoliosis based on radiological descriptions. Fifty-six posturographic images depicting single-curve scoliosis were selected and assessed by two independent neurosurgery specialists, who classified them as mild, moderate, or severe based on Cobb angles. Subsequently, descriptions were developed that accurately characterized the degree of spinal deformation, based on the measured values of Cobb angles. These descriptions were then provided to AI language models to assess their proficiency in diagnosing spinal pathologies. The artificial intelligence models conducted classification using the provided data. Our study also focused on identifying specific sources of information and criteria applied in their decision-making algorithms, aiming for a deeper understanding of the determinants influencing AI decision processes in scoliosis classification. The classification quality of the predictions was evaluated using performance evaluation metrics such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and balanced accuracy. Our study strongly supported our hypothesis, showing that among four AI models, ChatGPT 4 and Scholar AI Premium excelled in classifying single-curve scoliosis with perfect sensitivity and specificity. These models demonstrated unmatched rater concordance and excellent performance metrics. In comparing real and AI-generated scoliosis classifications, they showed impeccable precision in all posturographic images, indicating total accuracy (1.0, MAE = 0.0) and remarkable inter-rater agreement, with a perfect Fleiss’ Kappa score. This was consistent across scoliosis cases with a Cobb’s angle range of 11–92 degrees. Despite high accuracy in classification, each model used an incorrect angular range for the mild stage of scoliosis. Our findings highlight the immense potential of AI in analyzing medical data sets. However, the diversity in competencies of AI models indicates the need for their further development to more effectively meet specific needs in clinical practice. Full article
(This article belongs to the Special Issue Artificial Intelligence Applied to Clinical Practice)
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