ASiT Innovation Summit Oral Presentations
N.M. Smyth, A. Narsiman, C.T. Canavan, R. Chandavarkar, D. Toncheva, G. Hill, L. Fleming, A. Ranatunga, M. Quirke, C. Cahir, A.D.K. Hill, N. Healy, M. Shakir, S. Pattnaik, S. Pattnaik, S. Biswas, L. Guo, K.J. George, A. Selim, A. Ibrahim, R. Warren, N. Graham, D. Redfern

TL;DR
This paper explores patient perceptions of AI in healthcare and surgery, evaluates AI's role in wound assessment, and presents a deep learning system for physiotherapy monitoring.
Contribution
The study introduces a novel deep learning system for real-time physiotherapy monitoring and evaluates AI's potential in wound assessment and patient attitudes toward AI in surgery.
Findings
Patients prefer clinicians to retain final decision-making when AI is used in surgery.
AI models achieved up to 98% accuracy in burn assessment, outperforming junior clinicians.
A deep learning system for physiotherapy achieved 99% accuracy in classifying exercise postures.
Abstract
Artificial intelligence (AI) is being increasingly integrated into healthcare and surgery, however there is limited data regarding patient perceptions of AI this. Understanding patient perspectives is essential to ensure responsible integration of AI. The aim of this study was to evaluate patient attitudes regarding the use of AI in surgery. Audit approval was obtained (CA2025-106). A questionnaire was distributed to patients ≥16 years, attending the Emergency Department in Beaumont Hospital, Dublin. Collection commenced in June 2025 and concluded in August 2025. A tally was kept of all patients approached to complete the survey. Anonymous responses were compiled and descriptive statistics were performed. Of 1623 people approached, 1088 responded (response rate 67%), median age group 40-49years, 50% female, and 77% White Irish. Knowledge of AI was limited, with 74% indicating little…
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Taxonomy
TopicsEducational Robotics and Engineering · Human auditory perception and evaluation · Diverse Scientific and Economic Studies
