Special Issues
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Artificial Intelligence and Data Science in Healthcare

Submission Deadline: 31 October 2023 (closed) View: 38

Guest Editors

Dr. José Machado, University of Minho, Portugal
Dr. Hugo Peixoto, University of Minho, Portugal

Summary

The healthcare sector is undergoing a transformation because to the development of artificial intelligence and data science, which are bringing fresh perspectives and answers to some of the industry's most serious problems. We encourage contributions of original research articles that examine the most recent advancements, uses, and difficulties in the nexus of artificial intelligence and data science with healthcare.

 

Possible subjects might include, but are not limited to:

 

• AI-powered diagnosis and planning of treatments

• Personalized medicine using predictive modeling

• Using big data analytics to control population health

• Using machine learning to diagnose and forecast illnesses

• Medical imaging using deep learning for image and signal analysis

• Clinical decision assistance and medical record natural language processing

• AI for medical decision-making explained

• AI-driven medicine development and discovery

• Ethical and legal concerns with AI in healthcare

• Big Data in Healthcare

• Ontologies and Standards for better Data Quality

 

We are eager to review the most recent studies in the area of artificial intelligence and data science in healthcare and look forward to receiving your suggestions.


Keywords

AI; Explainable AI; Data Science; Healthcare; Diagnosis; Treatment; Personalized Medicine; Predictive Modeling; Big Data Analytics; Population Health Management; Machine Learning; Deep Learning; Medical Imaging; Natural Language Processing; Medical Records; Clinical Decision Support; Drug Discovery; Ethics; Regulation; Ontologies.

Published Papers


  • Open Access

    REVIEW

    A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

    Deepak Upreti, Eunmok Yang, Hyunil Kim, Changho Seo
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2239-2274, 2024, DOI:10.32604/cmes.2024.048932
    (This article belongs to the Special Issue: Artificial Intelligence and Data Science in Healthcare)
    Abstract Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security. It involves constructing machine learning models using datasets spread across several data centers, including medical facilities, clinical research facilities, Internet of Things devices, and even mobile devices. The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information, reducing the risk of data loss, privacy breaches, or data exposure. The application of federated learning in the healthcare industry holds significant promise More >

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