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References from Patients’ and Physicians’ Perceptions of AI Integration in Prostate Cancer Diagnosis: Mixed Methods Study of Challenges to the Patient-Physician Relationship. Local targets link to admitted publications; unresolved targets remain external evidence.
A narrative review of artificial intelligence in medical diagnostics
10.32604/cmc.2025.063803 · External reference
High-performance medicine: the convergence of human and artificial intelligence
10.1038/s41591-018-0300-7 · External reference
The integration of artificial intelligence into clinical medicine: trends, challenges, and future directions
10.1016/j.disamonth.2025.101882 · External reference
Patients’ attitudes toward artificial intelligence (AI) in cancer care: a scoping review protocol
10.1371/journal.pone.0317276 · External reference
Facilitating public involvement in research about healthcare AI: a scoping review of empirical methods
10.1016/j.ijmedinf.2024.105417 · External reference
Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review
10.1016/s2589-7500(21)00132-1 · External reference
Patient perspectives on the use of artificial intelligence in health care: a scoping review
10.17294/2330-0698.2029 · External reference
Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: a scoping review
10.1016/j.artmed.2023.102698 · External reference
Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey
10.1186/s12911-021-01586-8 · External reference
Determinants of physicians’ intention to use AI-assisted diagnosis: an integrated readiness perspective
10.1016/j.chb.2023.107868 · External reference
Artificial intelligence and human trust in healthcare: focus on clinicians
10.2196/15154 · External reference
Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives
10.1186/s12911-020-01191-1 · External reference
Use and control of artificial intelligence in patients across the medical workflow: single-center questionnaire study of patient perspectives
10.2196/24221 · External reference
Patients’ perceptions of artificial intelligence acceptance, challenges, and use in medical care: qualitative study
10.2196/70487 · External reference
An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals
10.1038/s41746-023-00852-5 · External reference
Artificial intelligence (AI)-based tools in the diagnosis and management of prostate cancer: a systematic review and meta-analysis
10.1038/s41391-025-01060-w · External reference
Catalyzing precision medicine: artificial intelligence advancements in prostate cancer diagnosis and management
10.3390/cancers16101809 · External reference
The Barcelona Predictive Model of clinically significant prostate cancer
10.3390/cancers14061589 · External reference
Artificial intelligence-driven prostate cancer diagnosis: enhancing accuracy and personalizing patient care
10.1016/j.urolonc.2025.11.013 · External reference
Patients’ trust in artificial intelligence-based decision-making for localized prostate cancer: results from a prospective trial
10.1016/j.euf.2023.10.020 · External reference
Patient perspectives on the use of artificial intelligence in prostate cancer diagnosis on MRI
10.1007/s00330-024-11012-y · External reference
Men’s sociotechnical imaginaries of artificial intelligence for prostate cancer diagnostics - a focus group study
10.1016/j.socscimed.2024.116771 · External reference
Acceptance of artificial intelligence in supporting cancer patients
10.3233/shti230561 · External reference
Shared decision making--pinnacle of patient-centered care
10.1056/nejmp1109283 · External reference
Unresolved reference
External reference
Toward a framework for appraising the quality of integration in mixed methods research
10.1177/15586898241257555 · External reference
The REDCap consortium: building an international community of software platform partners
10.1016/j.jbi.2019.103208 · External reference
Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support
10.1016/j.jbi.2008.08.010 · External reference
Acceptability of healthcare interventions: an overview of reviews and development of a theoretical framework
10.1186/s12913-017-2031-8 · External reference
Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies
10.2196/jmir.8775 · External reference
Supporting best practice in reflexive thematic analysis reporting in : a review of published research and introduction to the (RTARG)
10.1177/02692163241234800 · External reference
Toward good practice in thematic analysis: avoiding common problems and be(com)ing a researcher
10.1080/26895269.2022.2129597 · External reference
Empowerment, patient centred care and self-management
10.1111/j.1369-7625.2011.00757.x · External reference
Narrative based medicine: narrative based medicine in an evidence based world
10.1136/bmj.318.7179.323 · External reference
Uncertainty and objectivity in clinical decision making: a clinical case in emergency medicine
10.1007/s11019-016-9714-5 · External reference
Towards more reliable prostate cancer detection: incorporating clinical data and uncertainty in MRI deep learning
10.1016/j.compbiomed.2025.110440 · External reference
Artificial intelligence uncertainty quantification in radiotherapy applications - a scoping review
10.1016/j.radonc.2024.110542 · External reference
Patient information needs for transparent and trustworthy cardiovascular artificial intelligence: a qualitative study
10.1371/journal.pdig.0000826 · External reference
Patient perspectives on trust in artificial intelligence-powered tools in prostate cancer diagnostics
10.1177/10497323251387545 · External reference
Conceptualizing clinicians’ trust in artificial intelligence as a function of their expertise, workload, patient outcome, diagnosis difficulty, and AI accuracy: a systems thinking approach
10.1109/access.2025.3586555 · External reference
Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system
10.1186/s12911-017-0430-8 · External reference
An overview of clinical decision support systems: benefits, risks, and strategies for success
10.1038/s41746-020-0221-y · External reference
Evaluating accountability, transparency, and bias in AI-assisted healthcare decision- making: a qualitative study of healthcare professionals’ perspectives in the UK
10.1186/s12910-025-01243-z · External reference
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
10.1186/s12911-020-01332-6 · External reference
Presenting machine learning model information to clinical end users with model facts labels
10.1038/s41746-020-0253-3 · External reference
Perceived responsibility in AI-supported medicine
10.1007/s00146-024-01972-6 · External reference
On the ethics of algorithmic decision-making in healthcare
10.1136/medethics-2019-105586 · External reference
Factors influencing AI acceptance in radiology: a systematic review across the radiology workflow
10.1007/s00330-025-12129-4 · External reference
Clinical AI: opacity, accountability, responsibility and liability
10.1007/s00146-020-01019-6 · External reference
People are averse to machines making moral decisions
10.1016/j.cognition.2018.08.003 · External reference
Key information influencing patient decision-making about AI in health care: survey experiment study
10.2196/75615 · External reference
Do as AI say: susceptibility in deployment of clinical decision-aids
10.1038/s41746-021-00385-9 · External reference
Impact of a deep learning assistant on the histopathologic classification of liver cancer
10.1038/s41746-020-0232-8 · External reference
Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels
10.1186/s12913-025-12664-2 · External reference
Relational expertise: what machines can’t know
10.1111/joms.12915 · External reference
Exploring the complex nature of implementation of artificial intelligence in clinical practice: an interview study with healthcare professionals, researchers and policy and governance experts
10.1371/journal.pdig.0000847 · External reference
How to discuss transferability of qualitative research in health professions education
10.1111/tct.13762 · External reference
