Table 2 Hindering factors

Knowledge about AI

• Lack of education programs [37, 56, 87, 88]

• Lack of knowledge [32, 42, 52, 55, 59, 69, 71, 76, 80, 83, 87, 89, 90]

• Time constraint for education [37, 39, 40]

• Age of healthcare professionals [39]

Attitude towards profession

• Fear of job replacement [32, 38, 48, 91]

• Fear of dependency, overreliance, loss of competency [30, 58, 61, 69, 73, 76]

• Job requirements [58]

Working with AI

• Conflict of opinion [92]

• Information used by AI is not up to date [30]

• Increased workload [31, 32, 38, 61]

Other

• Overloaded by technology [31]

Implication of relationships to patients

• Impact on doctor-patient relationship [31, 36, 45, 46, 52, 76]

• Communication with patient [29, 82]

• Lack of human touch [30, 32, 46, 47, 52, 77, 78, 82, 83]

• AI disclosure to patient [30]

• Patient compliance depends on useability [43]

Medical decision making in clinical setting

• Reliability (AI not up to date, trust issues) [30, 69, 76, 93]

• Clinical errors [30, 31, 38, 40, 46, 52, 55, 81, 84, 85]

• Decreased sensitivity/specifitity [61]

• Inability to account for patient diversity, complex or controversial cases, context [29,30,31,32,33,34,35, 48, 52, 55, 61, 76, 83]

• Limitation of programming scope [32, 83]

• Inadequacy in specific contexts [46]

Organizational readiness

• Lack of responsible personnel (Chief Officer or Office) [87]

• Lack of organisational support [37]

• Lack of funding [42]

• Compatibility of treatment methods and digital systems [62]

Organizational costs

• Implementation costs [37, 39, 54, 75, 94]

• Education and training [83]

• Development and Acquisition [83, 91]

Healthcare organizations

• Dehumanization of healthcare [29, 47]

• Commercial interests [37, 38, 45]

• inappropriate use by insurance companies [69]

Research and development

• Lack of transparency in research, development and validation [33, 36, 38]

• Bias in training data (e.g. color of skin) [38]

• Explainability and interpretability of AI [37, 39]

Healthcare system issues

• Divestment of healthcare to large technology companies [65]

• Lack of adequate reimbursement models [31]

Equity issues

• Health inequalities [32, 61]

• Inequitable healthcare quality due to AI use [38]

Legal issues

• Unclear responsibility [41, 65, 86]

• Liability and accountability [30, 31, 61, 65, 70, 76, 80]

• Security and privacy (data) [30, 38, 45, 46, 52, 65, 76, 91]

• Lack of regulatory policies [45, 56, 64]