Transforming Precision Medicine: The Intersection of Digital Health and AI—2nd Edition

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: 15 November 2024 | Viewed by 1584

Special Issue Editor


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Guest Editor
Centro Nazionale TISP, Istituto Superiore di Sanità, 00161 Rome, Italy
Interests: biomedical engineering; robotics; artificial intelligence; digital health; rehabilitation; smart technology; cybersecurity; mental health; animal-assisted therapy; social robotics; acceptance; diagnostic pathology and radiology; medical imaging; patient safety; healthcare quality; health assessment; chronic disease
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Special Issue Information

Dear Colleagues,

We are delighted to announce the launch of the second edition of the Special Issue on “Transforming Precision Medicine: The Intersection of Digital Health and AI”.

 As a Guest Editor, I am excited to lead this Special Issue after the success of the first edition (https://www.mdpi.com/journal/jpm/special_issues/4FMFQUN50A) and to provide a platform for the dissemination of cutting-edge research on the intersection of AI and digital health in precision medicine.

This Special Issue aims to explore the dynamic interplay between digital health and artificial intelligence (AI) and its transformative impact on the field of precision medicine.

Precision medicine has revolutionized healthcare by tailoring treatments to the unique characteristics of each patient. With the advent of digital health technologies and the advancements in AI, we are witnessing an unprecedented opportunity to further enhance the accuracy, efficiency, and accessibility of personalized healthcare.

We invite original research articles, reviews, and perspectives that shed light on the synergistic potential of these domains and showcase their applications across various healthcare settings.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • The role of wearable devices and remote monitoring in precision medicine;
  • AI-driven approaches for genomic analysis and precision diagnostics;
  • Intelligent algorithms for clinical decision support in precision medicine;
  • Applications of telemedicine and digital health platforms in delivering personalized care;
  • Ethical considerations and regulatory frameworks for digital health and AI in precision medicine;
  • Data privacy and security in the era of interconnected healthcare systems;
  • AI-driven precision medicine for specific diseases or populations;
  • Real-world case studies demonstrating the impact of digital health and AI on patient outcomes.

By bringing together diverse perspectives and cutting-edge research, this Special Issue aims to foster a deeper understanding of how the convergence of digital health and AI can transform precision medicine. We encourage submissions that present novel approaches, critical insights, and evidence-based outcomes, ultimately driving the field forward.

We invite researchers and practitioners from various disciplines to contribute to this Special Issue and contribute to expanding our collective knowledge in this exciting and rapidly evolving field.

Prof. Dr. Daniele Giansanti
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. 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

  • precision medicine
  • personalized medicine
  • genomic medicine
  • individualized medicine
  • evidence-based medicine
  • artificial intelligence
  • big data digital health

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Published Papers (1 paper)

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Research

24 pages, 2585 KiB  
Article
Precision Workforce Management for Radiographers: Monitoring and Managing Competences with an Automatic Tool
by Andrea Lastrucci, Yannick Wandael, Giovanni Orlandi, Angelo Barra, Stefano Chiti, Valentina Gigli, Massimo Marletta, Davide Pelliccia, Barbara Tonietti, Renzo Ricci and Daniele Giansanti
J. Pers. Med. 2024, 14(7), 669; https://doi.org/10.3390/jpm14070669 - 21 Jun 2024
Cited by 1 | Viewed by 1033
Abstract
Optimizing work shifts in healthcare is crucial for maintaining high standards of service delivery and fostering professional development. This study delves into the emerging field of skill-oriented work shift optimization, focusing specifically on radiographers within the healthcare sector. Through the development of Skills [...] Read more.
Optimizing work shifts in healthcare is crucial for maintaining high standards of service delivery and fostering professional development. This study delves into the emerging field of skill-oriented work shift optimization, focusing specifically on radiographers within the healthcare sector. Through the development of Skills Retention Monitoring (SRH), this research aims to enhance skill monitoring, workload management, and organizational performance. In this study, several key highlights emerged: (a) Introduction of the SRH tool: The SRH tool represents a resource-efficient solution that harnesses existing software infrastructure. A preliminary version, focusing on the radiographers’ professional profile, was released, and after several months of use, it demonstrated effectiveness in optimizing work based on competency monitoring. (b) The SRH tool has thus demonstrated the capacity to generate actionable insights in the organizational context of radiographers. By generating weekly reports, the SRH tool streamlines activity management and optimizes resource allocation within healthcare settings. (c) Application of a Computer-Assisted Web Interviewing (CAWI) tool for pre-release feedback during a training event. (d) Strategic importance of a maintenance and monitoring plan: This plan, rooted in a continuous quality improvement approach and key performance indicators, ensures the sustained effectiveness of the SRH tool. (e) Strategic importance of a transfer plan: Involving professional associations and employing targeted questionnaires, this plan ensures the customization of the tool from the perspective of each profession involved. This is a crucial point, as it will enable the release of tool versions tailored to various professions operating within the hospital sector. As a side result, the tool could allow for a more tailored and personalized medicine both by connecting the insights gathered through the SRH tool with the right competencies for healthcare professionals and with individual patient data. This integration could lead to better-informed decision making, optimizing treatment strategies based on both patient needs and the specific expertise of the healthcare provider. Future directions include deploying the SRH tool within the Pisa hospital network and exploring integration with AI algorithms for further optimization. Overall, this research contributes to advancing work shift optimization strategies and promoting excellence in healthcare service delivery. Full article
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