Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.4
Journals
Healthcare
Special Issues
Advances in Health Technology Assessment in the Era of AI...
share announcement

Advances in Health Technology Assessment in the Era of AI and Data Science

A special issue of Healthcare (ISSN 2227-9032).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 568

Special Issue Editors

Dr. Z. Kevin Lu

E-Mail Website
Guest Editor
College of Pharmacy, University of South Carolina, Columbia, SC 29208, USA
Interests: health economics; health policy; health technology assessment; real-world study
Special Issues, Collections and Topics in MDPI journals
Dr. Jing Yuan

E-Mail Website
Guest Editor
Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai 201203, China
Interests: health economics; health policy; health technology assessment; real-world study
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are excited to invite your submissions for a Special Issue of the journal Healthcare, titled "Advances in Health Technology Assessment in the Era of AI and Data Science". This issue seeks to highlight the most recent advancements and innovations in health technology assessment (HTA), with a particular emphasis on clinical effectiveness research, pharmacoeconomics, and evaluation of the economic burden and safety of new technologies.

In our rapidly advancing technological landscape, HTA is crucial for ensuring that new health technologies are both safe and effective, as well as economically viable. This Special Issue aims to gather and showcase the latest research and insights from experts in the field, providing valuable contributions that can guide healthcare decision making.

We especially encourage submissions that explore the role of artificial intelligence (AI) in health technology assessment. AI offers unique opportunities to enhance the accuracy and efficiency of HTA processes, and we are eager to see how these technologies are being integrated into healthcare evaluations. Given the rapid development in AI and big data, contributions from international researchers are particularly welcome.

There are some aspects that might not fully align with our scope, such as the pharmaceutical industry, drug price studies, business, or procurement. However, the emphasis of benefits to patient outcomes or healthcare delivery can be considered.

We invite researchers, educators, and policymakers to contribute their findings and perspectives. Your work will help foster a comprehensive understanding of the advancements shaping the future of health technology assessment.

Dr. Z. Kevin Lu
Dr. Jing Yuan
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. 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

  • health technology assessment (HTA)
  • artificial intelligence
  • big data

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 644 KiB  
Article
Ethical, Legal, and Social Assessment of AI-Based Technologies for Prevention and Diagnosis of Rare Diseases in Health Technology Assessment Processes
by Pietro Refolo, Costanza Raimondi, Violeta Astratinei, Livio Battaglia, Josep M. Borràs, Paula Closa, Alessandra Lo Scalzo, Marco Marchetti, Sonia Muñoz-López, Laura Sampietro-Colom and Dario Sacchini
Healthcare 2025, 13(7), 829; https://doi.org/10.3390/healthcare13070829 - 4 Apr 2025
Viewed by 367
Abstract
Background: While the HTA community appears well-equipped to assess preventive and diagnostic technologies, certain limitations persist in evaluating technologies designed for rare diseases, including those based on Artificial Intelligence (AI). In Europe, the EUnetHTA Core Model® serves as a reference for assessing [...] Read more.
Background: While the HTA community appears well-equipped to assess preventive and diagnostic technologies, certain limitations persist in evaluating technologies designed for rare diseases, including those based on Artificial Intelligence (AI). In Europe, the EUnetHTA Core Model® serves as a reference for assessing preventive and diagnostic technologies. This study aims to identify key ethical, legal, and social issues related to AI-based technologies for the prevention and diagnosis of rare diseases, proposing enhancements to the Core Model. Methods: An exploratory sequential mixed methods approach was used, integrating a PICO-guided literature review and a focus group. The review analyzed six peer-reviewed articles and compared the findings with a prior study on childhood melanoma published in this journal (Healthcare), retaining only newly identified issues. A focus group composed of experts in ethical, legal, and social domains provided qualitative insights. Results: Thirteen additional issues and their corresponding questions were identified. Ethical concerns related to rare diseases included insufficient disease history knowledge, lack of robust clinical data, absence of validated efficacy tools, overdiagnosis/underdiagnosis risks, and unknown ICER thresholds. Defensive medicine was identified as a legal issue. For AI-based technologies, concerns included discriminatory outcomes, explicability, and environmental impact (ethical); accountability and reimbursement (legal); and patient involvement and job losses (social). Conclusions: Integrating these findings into the Core Model enables a comprehensive HTA of AI-based rare disease technologies. Beyond the Core Model, these issues may inform broader assessment frameworks, ensuring rigorous and ethically responsible evaluations. Full article
(This article belongs to the Special Issue Advances in Health Technology Assessment in the Era of AI and Data Science)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop