Artificial intelligence in the medical physics community: An international survey. (January 2021)

• Record Type: Journal Article
• Authors: Diaz, Oliver; Guidi, Gabriele; Ivashchenko, Oleksandra; Colgan, Niall; Zanca, Federica
• Is Part Of: Physica medica. Volume 81(2021)
• Page Start: 141
• Page End: 146
• Publication Date: 2021-01
• Subjects: Medical physics -- Artificial intelligence -- Survey -- Medical technology; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2020.11.037
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Medical Physicists recognize the value and implications of AI in diagnostic and therapy. Medical Physicists want to be more involved in Artificial Intelligence projects and training. Artificial intelligence should be included in medical physics training. Abstract : Purpose: To assess current perceptions, practices and education needs pertaining to artificial intelligence (AI) in the medical physics field. Methods: A web-based survey was distributed to the European Federation of Organizations for Medical Physics (EFOMP) through social media and email membership list. The survey included questions about education, personal knowledge, needs, research and professionalism around AI in medical physics. Demographics information were also collected. Responses were stratified and analysed by gender, type of institution and years of experience in medical physics. Statistical significance ( p < 0.05 ) was assessed using paired t-test. Results: 219 people from 31 countries took part in the survey. 81% ( n = 177) of participants agreed that AI will improve the daily work of Medical Physics Experts (MPEs) and 88% ( n = 193) of respondents expressed the need for MPEs of specific training on AI. The average level of AI knowledge among participants was 2.3 ± 1.0 (mean ± standard deviation) in a 1-to-5 scale and 96% ( n = 210) of participants showed interest in improving their AI skills. A significantly lower AI knowledge was observed for female participants (2.0 ± 1.0), compared to male responders (2.4 ± 1.0). 64% of participants indicated that they are not involved in AI projects. The percentage of female leading AI projects was significantly lower than the male counterparts (3% vs 19%). Conclusions: AI was perceived as a positive resource to support MPEs in their daily tasks. Participants demonstrated a strong interest in improving their current AI-related skills, enhancing the need for dedicated training for MPEs.