Advances in Biomedical Analytics: Real-World Evidence, Digital Health, Robotics and Artificial Intelligence
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".
Deadline for manuscript submissions: 30 April 2025 | Viewed by 6035
Special Issue Editors
Interests: real-world data; real-world evidence; digital; digital health; artificial intelligence
Interests: bioinformatics; precision medicine; survival analysis; genomics; clinical trials
Interests: real-world data; real-world evidence; oncology; health technologies
Special Issue Information
Dear Colleague,
Following the 21st Century Cures Act in the United States (US), real-world evidence (RWE) has been well defined. According to the US Food and Drug Administration, real-world data (RWD) are “data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources”. RWE is “clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD”. RWD can be from a variety of sources, such as “data derived from electronic health records, medical claims data, data from product or disease registries, and data gathered from other sources (such as digital health technologies) that can inform on health status”.
With the great emphasis on RWD and RWE has come a variety of challenges and novel ways to optimize and enhance patient-centricity in the development of new therapeutics, whether by informing target selection, designing randomized controlled trials (RCTs), or enabling personalized medicine. This includes novel ways to evaluate disease burden and patient heterogeneity, increased ability to access, generate and leverage fit-for-purpose data sources, and opportunities to harness cloud computing for enhanced scalability and interactivity of digital tools. This includes novel means of gathering data, such as decentralized RCT’s to facilitate broader patient access, electronic patient-reported outcomes (PROs) capture, digital wearables, and bring-your-own-device (BYOD) designs. With this increase in volume and variety of patient-centric data also come increasing opportunities to adopt and deploy artificial intelligence (AI), machine learning (ML), deep learning (DL), digital health, robotics, and medical devices to unlock novel and timely insights.
This Special Issue focuses on the latest advancements in RWE, digital innovation and AI to enable and improve health care.
Dr. Kelly H. Zou
Dr. Susan Halabi
Dr. Aaron Galaznik
Dr. Kiyoyuki Chinzei
Guest Editors
Manuscript Submission Information
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Keywords
- real-world data
- real-world evidence
- big data
- randomized controlled trials
- patient-reported outcomes
- digital innovation
- digital health
- robotics
- medical devices
- artificial intelligence
- machine learning
- deep learning
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