Application of Artificial Intelligence in Personalized Medicine: Diagnosis and Treatment

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 1413

Special Issue Editor


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Guest Editor
Department of Health, Medicine and Caring Sciences, Linköping University, 581 85 Linköping, Sweden
Interests: artificial intelligence in medicine; deep learning; large language models in medicine; machine learning; multimodal model applications in medicine; cancer diagnosis image analysis

Special Issue Information

Dear Colleagues,

This Special Issue aims to focus on the transformative role of artificial intelligence (AI) in advancing personalized medicine, particularly in terms of diagnosis and treatment. AI technologies are revolutionizing healthcare by enabling more precise, individualized approaches to patient care. The integration of AI with medical imaging has a rich history, evolving from basic pattern recognition to sophisticated deep learning models capable of analyzing complex radiological data. Today, AI algorithms can detect subtle abnormalities in medical images, predict disease progression, and recommend personalized treatment plans with unprecedented accuracy.

Objectives: Our aim is to explore cutting-edge AI applications in personalized medicine, emphasizing novel diagnostic tools and tailored therapeutic strategies. We seek to highlight how AI is enhancing clinical decision-making, improving patient outcomes, and reshaping the landscape of precision medicine and healthcare. We welcome the submission of original research articles, comprehensive reviews, and other types of research that demonstrate innovative AI approaches in medical imaging for personalized diagnosis and treatment.

Topics of interest for this Special Issue may include, but are not limited to, cutting-edge research focused on AI-driven image analysis for early disease detection; predictive modeling for treatment response; and AI-assisted treatment planning in various medical specialties.

Dr. Abdulkader Helwan
Guest Editor

Manuscript Submission Information

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Keywords

  • artificial intelligence (AI)
  • personalized medicine
  • precision health
  • AI-driven diagnostics
  • machine learning
  • deep learning
  • predictive analytics
  • genomics
  • proteomics
  • biomarkers
  • patient-centric care
  • treatment personalization
  • healthcare data integration
  • drug discovery
  • real-time monitoring
  • ethical considerations in AI
  • individual variability
  • wearable technology
  • health informatics

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Published Papers (1 paper)

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Review

15 pages, 549 KiB  
Review
A Scoping Review of the Use of Artificial Intelligence in the Identification and Diagnosis of Atrial Fibrillation
by Antônio da Silva Menezes Junior, Ana Lívia Félix e Silva, Louisiany Raíssa Félix e Silva, Khissya Beatryz Alves de Lima and Henrique Lima de Oliveira
J. Pers. Med. 2024, 14(11), 1069; https://doi.org/10.3390/jpm14111069 - 24 Oct 2024
Viewed by 634
Abstract
Background/Objective: Atrial fibrillation [AF] is the most common arrhythmia encountered in clinical practice and significantly increases the risk of stroke, peripheral embolism, and mortality. With the rapid advancement in artificial intelligence [AI] technologies, there is growing potential to enhance the tools used in [...] Read more.
Background/Objective: Atrial fibrillation [AF] is the most common arrhythmia encountered in clinical practice and significantly increases the risk of stroke, peripheral embolism, and mortality. With the rapid advancement in artificial intelligence [AI] technologies, there is growing potential to enhance the tools used in AF detection and diagnosis. This scoping review aimed to synthesize the current knowledge on the application of AI, particularly machine learning [ML], in identifying and diagnosing AF in clinical settings. Methods: Following the PRISMA ScR guidelines, a comprehensive search was conducted using the MEDLINE, PubMed, SCOPUS, and EMBASE databases, targeting studies involving AI, cardiology, and diagnostic tools. Precisely 2635 articles were initially identified. After duplicate removal and detailed evaluation of titles, abstracts, and full texts, 30 studies were selected for review. Additional relevant studies were included to enrich the analysis. Results: AI models, especially ML-based models, are increasingly used to optimize AF diagnosis. Deep learning, a subset of ML, has demonstrated superior performance by automatically extracting features from large datasets without manual intervention. Self-learning algorithms have been trained using diverse data, such as signals from 12-lead and single-lead electrocardiograms, and photoplethysmography, providing accurate AF detection across various modalities. Conclusions: AI-based models, particularly those utilizing deep learning, offer faster and more accurate diagnostic capabilities than traditional methods with equal or superior reliability. Ongoing research is further enhancing these algorithms using larger datasets to improve AF detection and management in clinical practice. These advancements hold promise for significantly improving the early diagnosis and treatment of AF. Full article
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