Table 4 Summary of extracted data for each specific domain

(1) The health problem and current use of technology

No details extracted

(2) Technology aspects

The main topic is interpretability in the sense that we need to avoid the “Black box problem” and the analysis done by the algorithm needs to be transparent to physicians/staff i.e., explainable AI

Furthermore, risk of bias, possibly causes discrimination issues and validation. The algorithm development method is highlighted, including data quality, the importance of annotation, external evaluation, and reference standards

Equipment and IT was a topic mentioning the clinical IT integration and infrastructure

OUTCOME MEASURES: interpretability, quality of scans, technical functioning/feasibility

(3) Safety assessment

Safety of patients, potential challenges after implementation of AI to the healthcare system

Reducing side effects and especially radiation dose, data security and protection

OUTCOME MEASURES: natural radiation exposure, using clinical knowledge support

(4) Clinical effectiveness, e.g. clinical outcomes*

No details extracted

(5) Economics

The description of the savings and benefits are most often very general, e.g. improved cost-effectiveness

OUTCOME MEASURES: reduction in workload and time for staff, reduction in the number of biopsies and patients use of medication

(6) Ethical analysis

Privacy, consent, obligations, security, awareness of the use of patients’ data, and ownership of the data

Ethical approval and consider ethical issues of data, algorithms, trained models, and practice

Understanding risks vs. benefits, shared/clear decision-making and transparency of results

Big questions: “who owns data”, “can data and the algorithm be trusted” and “what is good clinical practice?

OUTCOME MEASURES: validity of data, risks versus benefits, patient safety

(7) Organisational aspects

Benefits in the form of reductions in workflow and tasks related to imaging for the staff as a result of AI

The use of additional time related to implementation and training and the challenges related to ensuring acceptability

OUTCOME MEASURES: changes in time use for the health care professionals and patient, clinician acceptability measures

(8) Patients and social aspects

Patients’ comfortability including easier imaging process and providing access to own data/report in a safe and secured platform

Better treatment outcome based on the improved clinical decision is the most discussed issue

Patients’ satisfaction, as well as clinical benefits, could result in better acceptability of AI technology in the healthcare system

OUTCOME MEASURES: the time required for diagnosis, rating for overall satisfaction, help patients make more informed activity choices

(9) Legal aspects

Data security and privacy

Responsibility for misdiagnosis

OUTCOME MEASURES: regulatory approvals, consent from patients

(10) Development of AI algorithm, performance metrics and validation

No details extracted

(11) Other aspects

Overpromising language in studies

Offering the possibility of performing expensive and time-consuming screening programs in countries that otherwise cannot afford them

OUTCOME MEASURES: none identified