Data · dataset · 2025
Artificial Intelligence in Medicine: Revolutionizing Healthcare Practices and Patient Outcomes
Listed in Teesside University Research Data Repository
Description
Abstract
Artificial intelligence (AI) has transformed medicine, advancing diagnostics, treatment, and patient outcomes. This study employs bibliometric analysis and the PRISMA framework to explore AI research trends in medicine from 2019 to 2023. Key findings reveal significant growth in publications, with radiomics, genomics, and predictive analytics as major focus areas.
Read the rest (2 more)
Federated learning frameworks and wearable technologies emerge as critical innova-tions, addressing challenges such as data privacy and real-time monitoring. Despite these advancements, significant barriers persist, including algorithmic bias, data confidentiality, and the need for infrastructure and training to integrate AI into clinical workflows. Future directions emphasize the importance of interdisciplinary collaboration, ethical AI develop-ment, and explainable models to ensure trust and equitable access.
By addressing these chal-lenges, AI has the potential to revolutionize healthcare systems, optimize resource alloca-tion, and enhance personalized medicine. This study highlights the transformative role of AI in shaping the future of medicine and improving global health outcomes. Keywords: Artificial Intelligence, Personalized Medicine, Healthcare Innovation, AI-Driven Diagnostics, Machine Learning, Deep Learning, Predictive Analytics
Links
Where it is published
- DOI doi.org/10.17632/wfdp6t7v2f.2 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/wfdp6t7v2f.2 | 8 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:field:461103 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Deep Learning'] |
| concepts[field].anzsrc:group:4602 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Artificial Intelligence'] |
| concepts[field].anzsrc:group:4611 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |