Vendors' perspectives on AI implementation in medical imaging and oncology: a cross-sectional survey

Abstract

Objectives: To explore the perspectives of AI vendors on the integration of AI in medical imaging and oncology clinical practice.

Materials and methods: An online survey was created on Qualtrics, comprising 23 closed and 5 open-ended questions. This was administered through social media, personalised emails, and the channels of the European Society of Medical Imaging Informatics and Health AI Register, to all those working at a company developing or selling accredited AI solutions for medical imaging and oncology. Quantitative data were analysed using SPSS software, version 28.0. Qualitative data were summarised using content analysis on NVivo, version 14.

Results: In total, 83 valid responses were received, with participants having a global distribution and diverse roles and professional backgrounds (business/management/clinical practitioners/engineers/IT, etc). The respondents mentioned the top enablers (practitioner acceptance, business case of AI applications, explainability) and challenges (new regulations, practitioner acceptance, business case) of AI implementation. Co-production with end-users was confirmed as a key practice by most (52.9%). The respondents recognised infrastructure issues within clinical settings (64.1%), lack of clinician engagement (54.7%), and lack of financial resources (42.2%) as key challenges in meeting customer expectations. They called for appropriate reimbursement, robust IT support, clinician acceptance, rigorous regulation, and adequate user training to ensure the successful integration of AI into clinical practice.

Conclusion: This study highlights that people, infrastructure, and funding are fundamentals of AI implementation. AI vendors wish to work closely with regulators, patients, clinical practitioners, and other key stakeholders, to ensure a smooth transition of AI into daily practice.

Key points: Question AI vendors' perspectives on unmet needs, challenges, and opportunities for AI adoption in medical imaging are largely underrepresented in recent research. Findings Provision of consistent funding, optimised infrastructure, and user acceptance were highlighted by vendors as key enablers of AI implementation. Clinical relevance Vendors' input and collaboration with clinical practitioners are necessary to clinically implement AI. This study highlights real-world challenges that AI vendors face and opportunities they value during AI implementation. Keeping the dialogue channels open is key to these collaborations.

Keywords: Artificial intelligence; Diagnostic imaging; Industry; Radiation oncology.