Table 2.

Top five questions prioritised by healthcare professionals, cardiovascular researchers and data science and/or computer science researchers for the prioritisation domains (A) positive impact for patients, (B) potential to reduce inequalities in healthcare and (C) ability to be implemented into UK healthcare practice in a timely manner

	Healthcare professionals	Cardiovascular imaging researchers	Data science and/or computer science researchers
(A) Positive impact for patients
1	How do we use cardiovascular imaging to guide management, reduce disease progression and improve prognosis for patients with coronary artery disease?	How can cardiovascular imaging be used to make more rapid and accurate diagnoses?	How do we ensure patients have equal access to cardiovascular imaging when it is needed?
2	How do we ensure patients have equal access to cardiovascular imaging when it is needed?	How do we ensure patients have equal access to cardiovascular imaging when it is needed?	Can we reduce the number of cardiac imaging tests that patients need during follow-up?
3	How can cardiovascular imaging be used to make more rapid and accurate diagnoses?	Can we use cardiovascular imaging to better diagnose the cause and subtypes of heart failure and predict which patients with heart failure would benefit from different treatments?	Can we use AI to prioritise cardiovascular imaging scans for reporting and improve clinical decision-making based on cardiovascular imaging?
4	Can we reduce the number of cardiac imaging tests that patients need during follow-up?	How can we use cardiovascular imaging to avoid invasive procedures?	How can we use cardiovascular imaging to avoid invasive procedures?
5	How do we use cardiovascular imaging to identify patients at risk of sudden cardiac death?	How do we better train staff to perform and report cardiovascular imaging?	How can cardiovascular imaging be used to make more rapid and accurate diagnoses?
(B) Potential to reduce inequalities in healthcare
1	How do we ensure patients have equal access to cardiovascular imaging when it is needed?	How do we ensure patients have equal access to cardiovascular imaging when it is needed?	How can we use cardiovascular imaging to make care pathways more efficient and improve the cost-effectiveness of cardiovascular imaging?
2	How can cardiovascular imaging be used to make more rapid and accurate diagnoses?	How can we use cardiovascular imaging to make care pathways more efficient and improve the cost effectiveness of cardiovascular imaging?	How do we ensure patients have equal access to cardiovascular imaging when it is needed?
3	How do we use cardiovascular imaging to guide management, reduce disease progression and improve prognosis for patients with coronary artery disease?	For cardiovascular imaging AI techniques, how do we identify and reduce bias, improve generalisability, and explain the results (explainable AI)?	How do we create a national representative, large-scale cardiovascular imaging research database with ground truth annotation to enable training and validation of AI techniques?
4	Can we use AI to prioritise cardiovascular imaging scans for reporting and improve clinical decision-making based on cardiovascular imaging?	How do we link cardiovascular imaging data to other health data, for example, NHS patient records in a safe, secure, and responsible manner, and manage public trust when using unconsented data?	For cardiovascular imaging AI techniques, how do we identify and reduce bias, improve generalisability and explain the results explainable AI?
5	How can we simplify, shorten and standardise cardiovascular image acquisition protocols for easier widespread use and reduced variability?	How can cardiovascular imaging be used to make more rapid and accurate diagnoses?	How can we use cardiovascular imaging to avoid invasive procedures?
(C) Ability to be implemented into UK healthcare practice in a timely manner
1	How can we use cardiovascular imaging to avoid invasive procedures?	How can we use cardiovascular imaging to make care pathways more efficient and improve the cost effectiveness of cardiovascular imaging?	For cardiovascular imaging AI techniques, how do we identify and reduce bias, improve generalisability and explain the results explainable AI?
2	How do we use cardiovascular imaging to guide management, reduce disease progression and improve prognosis for patients with coronary artery disease?	How can cardiovascular imaging be used to make more rapid and accurate diagnoses?	How do we create a national, representative, large scale cardiovascular imaging research database with ground truth annotation to enable training and validation of AI techniques?
3	How can cardiovascular imaging be used to make more rapid and accurate diagnoses?	How can we simplify, shorten and standardise cardiovascular image acquisition protocols for easier widespread use and reduced variability?	How can we simplify, shorten and standardise cardiovascular image acquisition protocols for easier widespread use and reduced variability?
4	How do we ensure patients have equal access to cardiovascular imaging when it is needed?	How can we use cardiovascular imaging to avoid invasive procedures?	Can we use AI to prioritise cardiovascular imaging scans for reporting and improve clinical decision-making based on cardiovascular imaging?
5	How can we use cardiovascular imaging to make care pathways more efficient and improve the cost effectiveness of cardiovascular imaging?	How do we link cardiovascular imaging data to other health data, for example, NHS patient records in a safe, secure, and responsible manner, and manage public trust when using unconsented data?	How do we link cardiovascular imaging data to other health data, for example, NHS patient records in a safe, secure, and responsible manner, and manage public trust when using unconsented data?

Green indicates that the question featured in the top five list for all three stakeholder groups for that prioritisation domain, orange indicates the question featured in two stakeholder groups and red only in one stakeholder group.

AI, artificial intelligence; NHS, National Health Service.