Application of Artificial Intelligence (AI) in Optimising Patient Recruitment in Clinical Trial Enrolment Processes
| dc.contributor.author | Kandimalla, Rajitha | |
| dc.date.accessioned | 2025-06-23T14:40:17Z | |
| dc.date.available | 2025-06-23T14:40:17Z | |
| dc.date.issued | 2025-05 | |
| dc.description.abstract | In this study, an evaluation of AI technologies in clinical trial recruitment was conducted through a quantitative survey of 120 professionals including Clinical Research Associate, Clinical Trial Coordinator, Clinical Research Physician, Clinical Data Manager, Digital Technology Specialist, Clinical Research Manager, AI Implementation Specialists and Clinical Operations Director. The research examined AI's impact on recruitment efficiency, participant diversity enhancement, implementation challenges, and operational outcomes. Findings revealed substantial AI adoption (71.7%) with significant efficiency benefits, as 65.9% of respondents agreed AI reduces recruitment time, with 46.6% reporting time savings exceeding 50%. AI demonstrated particular effectiveness in participant screening (32.5%) and database management (29.2%). Regarding diversity enhancement, perceptions were mixed (42.5% reporting positive impacts), with language barriers (25.0%) identified as the most addressable diversity challenge. Data privacy emerged as the predominant implementation concern (79.2%), alongside balanced challenges in skilled personnel availability, tool complexity, and resistance to change. Operationally, AI showed promise in patient monitoring (72.5% reporting improvements) and overall efficiency (35.0%). Statistical analysis revealed significant differences between AI users and non-users across multiple dimensions, suggesting implementation experience substantially enhances perceived benefits. The research indicates AI offers meaningful advantages for clinical trial recruitment, though successful implementation requires addressing interconnected technical, organizational, and human factors through comprehensive, phased implementation strategies. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14136/233 | |
| dc.title | Application of Artificial Intelligence (AI) in Optimising Patient Recruitment in Clinical Trial Enrolment Processes | |
| dc.type | Thesis |