Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment
出版年份 2023 全文链接
标题
Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment
作者
关键词
-
出版物
Frontiers in Public Health
Volume 11, Issue -, Pages -
出版商
Frontiers Media SA
发表日期
2023-04-26
DOI
10.3389/fpubh.2023.1088121
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Pelvic U-Net: multi-label semantic segmentation of pelvic organs at risk for radiation therapy anal cancer patients using a deeply supervised shuffle attention convolutional neural network
- (2022) Michael Lempart et al. Radiation Oncology
- Machine Learning Methods in Health Economics and Outcomes Research—The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force
- (2022) William V. Padula et al. VALUE IN HEALTH
- Using real-world evidence in healthcare from Western to Central and Eastern Europe: a review of existing barriers
- (2022) Maria Kamusheva et al. Journal of Comparative Effectiveness Research
- Barriers to Use Artificial Intelligence Methodologies in Health Technology Assessment in Central and East European Countries
- (2022) Konstantin Tachkov et al. Frontiers in Public Health
- Health Technology Assessment for Cardiovascular Digital Health Technologies and Artificial Intelligence: Why is it Different?
- (2021) Dominique Vervoort et al. CANADIAN JOURNAL OF CARDIOLOGY
- Synthetic data in machine learning for medicine and healthcare
- (2021) Richard J. Chen et al. Nature Biomedical Engineering
- Artificial intelligence and the conduct of literature reviews
- (2021) Gerit Wagner et al. JOURNAL OF INFORMATION TECHNOLOGY
- Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah
- (2021) Madelon M. Voets et al. VALUE IN HEALTH
- Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity
- (2020) Hassane Alami et al. JOURNAL OF MEDICAL INTERNET RESEARCH
- Improving Transparency to Build Trust in Real-World Secondary Data Studies for Hypothesis Testing—Why, What, and How: Recommendations and a Road Map from the Real-World Evidence Transparency Initiative
- (2020) Lucinda S. Orsini et al. VALUE IN HEALTH
- Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment
- (2020) Seamus Kent et al. PHARMACOECONOMICS
- Data science in modern evidence-based medicine
- (2019) Dina Radenkovic et al. JOURNAL OF THE ROYAL SOCIETY OF MEDICINE
- A Strategy to Support Efficient Development and Use of Innovations in Personalized Medicine and Precision Medicine
- (2019) Louis P. Garrison et al. Journal of Managed Care & Specialty Pharmacy
- Iterative PET Image Reconstruction Using Convolutional Neural Network Representation
- (2018) Kuang Gong et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Transforming health policy through machine learning
- (2018) Hutan Ashrafian et al. PLOS MEDICINE
- Evaluation of Healthcare Interventions and Big Data: Review of Associated Data Issues
- (2017) Carl V. Asche et al. PHARMACOECONOMICS
- HTA Implementation Roadmap in Central and Eastern European Countries
- (2016) Zoltán Kaló et al. HEALTH ECONOMICS
- GRADE guidelines: 4. Rating the quality of evidence—study limitations (risk of bias)
- (2011) Gordon H. Guyatt et al. JOURNAL OF CLINICAL EPIDEMIOLOGY
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started