Advances in Artificial Intelligence-Based Medical Devices for Healthcare Applications
Abstract Medical devices play a crucial role in modern healthcare, providing innovative solutions for diagnosing, preventing, monitoring, and treating ailments. Artificial Intelligence is transforming the field of medical devices, offering unprecedented opportunities through diagnostic accuracy, personalized treatment plans, and enhancing patient outcomes. This review outlines the applications of artificial intelligence-based medical devices in healthcare specialties, especially in dentistry, medical imaging, ophthalmology, mental health, autism spectrum disorder diagnosis, oncology, and general medicine. Specifically, the review highlights advancements such as improved diagnostic accuracy, tailored treatment planning, and enhanced clinical outcomes in the above-mentioned applications. Regulatory approval remains a key issue, where medical devices must be approved or cleared by the United States Food and Drug Administration to establish their safety and efficacy. The regulatory guidance pathway for artificial intelligence-based medical devices is presented and moreover the critical technical, ethical, and implementation challenges that must be addressed for large-scale adoption are discussed. The review concludes that the intersection of artificial intelligence with the medical device domain and internet-enabled or enhanced technology, such as biotechnology, nanotechnology, and personalized therapeutics, enables an enormous opportunity to accelerate customized and patient-centered care. By evaluating these advancements and challenges, the study aims to present insights into the future trajectory of smart medical technologies and their role in advancing personalized, patient-centered care.
Authors
- Steven R. Bauer (ORCID: https://orcid.org/0000-0003-2831-846X)
- Felix Tettey (ORCID: https://orcid.org/0000-0002-2101-0131)
- Santosh Kumar Parupelli (ORCID: https://orcid.org/0000-0003-0535-7618)
- Narayan Bhattarai (ORCID: https://orcid.org/0000-0002-0583-7913)
- Salil Desai (ORCID: https://orcid.org/0000-0002-6116-2105)
Institutions
- Forest Institute (US)
- North Carolina Agricultural and Technical State University (US)
- Wake Forest University (US)
Publication Details
- Journal
- Biomedical Materials & Devices
- Published
- 2025-05-30
- DOI
- https://doi.org/10.1007/s44174-025-00379-1
- Citations
- 6
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 8.16
Funders
- Wake Forest University
- Division of Computer and Network Systems
- Division of Information and Intelligent Systems
- Division of Civil, Mechanical and Manufacturing Innovation
- Innovation and Technology Ecosystems