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Artificial Intelligence and the Future of Primary Care: Exploratory Qualitative Study of UK General Practitioners’ Views
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1 of the 48 references listed on this page is in the Retraction Watch database as retracted (export 2026-11-04): 10.1056/nejmoa1712231 (notice 2020-01-29).
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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inferred← citeshttps://doi.org/10.1097/apo.0000000000000406openalex-snapshot
Impact and Challenges of Integrating Artificial Intelligence and Telemedicine into Clinical Ophthalmology
inferred← citeshttps://doi.org/10.1097/hmr.0000000000000455openalex-snapshot
Examining team creativity in primary care
inferred← citeshttps://doi.org/10.1097/nna.0000000000001673openalex-snapshot
Unveiling the Capacity of Artificial Intelligence in Mitigating Nurse Burnout
inferred← citeshttps://doi.org/10.1097/wno.0000000000001682openalex-snapshot
Real-World Translation of Artificial Intelligence in Neuro-Ophthalmology: The Challenges of Making an Artificial Intelligence System Applicable to Clinical Practice
inferred← citeshttps://doi.org/10.1101/2023.06.29.23291561openalex-snapshot
Medical Expectations Survey on Artificial Intelligence Solutions in daily practice
inferred← citeshttps://doi.org/10.1101/2025.03.22.25324458openalex-snapshot
Knowledge–Attitude Gap and Unsupervised Use of Artificial Intelligence Tools Among Swiss Primary Care Physicians: A Multicentric Cross-Sectional Survey
inferred← citeshttps://doi.org/10.1108/978-1-83662-878-120261005openalex-snapshot
Equity-Driven AI Frameworks for Faculty Performance Evaluation in Public and Private Universities
inferred← citeshttps://doi.org/10.1108/978-1-83708-022-920261025openalex-snapshot
Early Adopters of AI and Gender Disparities in Health Outcomes
inferred← citeshttps://doi.org/10.1108/jhom-03-2020-0074openalex-snapshot
Organizational readiness for artificial intelligence in health care: insights for decision-making and practice
inferred← citeshttps://doi.org/10.1108/jhom-12-2025-0889openalex-snapshot
Physicians’ attitudes toward artificial intelligence and ChatGPT: a cross-sectional study
inferred← citeshttps://doi.org/10.1109/cbms49503.2020.00009openalex-snapshot
Evaluating Local Interpretable Model-Agnostic Explanations on Clinical Machine Learning Classification Models
inferred← citeshttps://doi.org/10.1109/iccit62134.2024.10701255openalex-snapshot
Evaluating AI-Induced Anxiety and Technical Blindness in Healthcare Professionals
inferred← citeshttps://doi.org/10.1111/bioe.70019openalex-snapshot
Assisting, Replicating, or Autonomously Acting? An Ethical Framework for Integrating AI Tools and Technologies in Healthcare
inferred← citeshttps://doi.org/10.1111/hex.70216openalex-snapshot
Artificial Intelligence in Medical Care – Patients' Perceptions on Caregiving Relationships and Ethics: A Qualitative Study
inferred← citeshttps://doi.org/10.1111/jdv.18192openalex-snapshot
Risks and benefits of dermatological machine learning health care applications—an overview and ethical analysis
inferred← citeshttps://doi.org/10.1111/nicc.70157openalex-snapshot
When Machines Decide: Exploring How Trust in AI Shapes the Relationship Between Clinical Decision Support Systems and Nurses' Decision Regret: A Cross‐Sectional Study
inferred← citeshttps://doi.org/10.1111/risa.14216openalex-snapshot
Choosing human over AI doctors? How comparative trust associations and knowledge relate to risk and benefit perceptions of AI in healthcare
inferred← citeshttps://doi.org/10.1136/bmj.n2915openalex-snapshot
Of research and robots: making sense of chance findings
inferred← citeshttps://doi.org/10.1136/bmjdhai-2025-000051openalex-snapshot
Generative artificial intelligence in medicine: a mixed-methods survey of UK general practitioners
inferred← citeshttps://doi.org/10.1136/bmjhci-2021-100450openalex-snapshot
Exploring stakeholder attitudes towards AI in clinical practice
inferred← citeshttps://doi.org/10.1136/bmjopen-2023-077156openalex-snapshot
Perceptions on artificial intelligence-based decision-making for coexisting multiple long-term health conditions: protocol for a qualitative study with patients and healthcare professionals
inferred← citeshttps://doi.org/10.1136/bmjopen-2024-092624openalex-snapshot
Barriers to and facilitators of clinician acceptance and use of artificial intelligence in healthcare settings: a scoping review
inferred← citeshttps://doi.org/10.1136/bmjopen-2025-102833openalex-snapshot
Perceptions of an AI-based clinical decision support tool for prescribing in multiple long-term conditions: a qualitative study of general practice clinicians in England
inferred← citeshttps://doi.org/10.1136/fmch-2022-001670openalex-snapshot
Artificial intelligence and health inequities in primary care: a systematic scoping review and framework
inferred← citeshttps://doi.org/10.1136/fmch-2025-003860openalex-snapshot
Evaluation of AI tool assisting primary healthcare physicians to diagnostic and treatment tasks
inferred← citeshttps://doi.org/10.1136/jme-2023-109574openalex-snapshot
Open AI meets open notes: surveillance capitalism, patient privacy and online record access
inferred← citeshttps://doi.org/10.1136/leader-2024-001093openalex-snapshot
Perceptions of artificial intelligence in healthcare: a qualitative study among healthcare professionals in Jordan
inferred← citeshttps://doi.org/10.1145/3446569.3446573openalex-snapshot
Challenges and Prospects for Digital Marketers while Dealing with SMEs: An Interview
inferred← citeshttps://doi.org/10.1145/3569893openalex-snapshot
Collaboration, not Confrontation: Understanding General Practitioners’ Attitudes Towards Natural Language and Text Automation in Clinical Practice
inferred← citeshttps://doi.org/10.1145/3786579.3804946openalex-snapshot
“But Who Takes the Liability?”: Analyzing Clinician Discourse on AI in Primary Care
inferred← citeshttps://doi.org/10.1167/tvst.10.7.14openalex-snapshot
Impact of Artificial Intelligence on Medical Education in Ophthalmology
inferred← citeshttps://doi.org/10.1177/00084174251327301openalex-snapshot
Opinions and Perspectives of Canadian Occupational Therapists on Artificial Intelligence
inferred← citeshttps://doi.org/10.1177/02666669241249744openalex-snapshot
Human-AI interaction: Augmenting decision-making for IT leader's project selection
inferred← citeshttps://doi.org/10.1177/10732748251343245openalex-snapshot
Perceptions, Attitudes, and Concerns on Artificial Intelligence Applications in Patients with Cancer
inferred← citeshttps://doi.org/10.1177/14604582211052391openalex-snapshot
Is it alright to use artificial intelligence in digital health? A systematic literature review on ethical considerations
inferred← citeshttps://doi.org/10.1177/14604582241262961openalex-snapshot
Dilemmas and prospects of artificial intelligence technology in the data management of medical informatization in China: A new perspective on SPRAY-type AI applications
inferred← citeshttps://doi.org/10.1177/2055207620968355openalex-snapshot
Artificial intelligence and the future of psychiatry: Qualitative findings from a global physician survey
inferred← citeshttps://doi.org/10.1177/20552076211003433openalex-snapshot
Exploring healthcare professionals’ perceptions of artificial intelligence: Validating a questionnaire using the e-Delphi method
inferred← citeshttps://doi.org/10.1177/20552076231176644openalex-snapshot
Determinants of the implementation of artificial intelligence-based screening for diabetic retinopathy—a cross-sectional study with general practitioners in Germany
inferred← citeshttps://doi.org/10.1177/20552076231186064openalex-snapshot
Medical artificial intelligence ethics: A systematic review of empirical studies
inferred← citeshttps://doi.org/10.1177/20552076251328578openalex-snapshot
Patients’, clinicians’ and developers’ perspectives and experiences of artificial intelligence in cardiac healthcare: A qualitative study
inferred← citeshttps://doi.org/10.1177/20552076251360863openalex-snapshot
General practitioners’ opinions of generative artificial intelligence in the UK: An online survey
inferred← citeshttps://doi.org/10.1177/20552076251386706openalex-snapshot
Bridging trust gaps: Stakeholder perspectives on AI adoption in the United Kingdom NHS primary care
inferred← citeshttps://doi.org/10.1177/20552076261491457openalex-snapshot
Beyond algorithms: A qualitative study on AI as a relational space for organ transplant patients
inferred← citeshttps://doi.org/10.1177/21582440251414704openalex-snapshot
Thanks a Bot: Leveraging Artificial Intelligence for Improved Workplace Appreciation
inferred← citeshttps://doi.org/10.1177/23821205211024078openalex-snapshot
Are We Ready to Integrate Artificial Intelligence Literacy into Medical School Curriculum: Students and Faculty Survey
inferred← citeshttps://doi.org/10.1186/s12875-023-02024-6openalex-snapshot
The voice of healthcare: introducing digital decision support systems into clinical practice - a qualitative study
inferred← citeshttps://doi.org/10.1186/s12875-024-02282-yopenalex-snapshot
Navigating the doctor-patient-AI relationship - a mixed-methods study of physician attitudes toward artificial intelligence in primary care
inferred← citeshttps://doi.org/10.1186/s12875-024-02516-zopenalex-snapshot
Healthcare leaders’ experiences of implementing artificial intelligence for medical history-taking and triage in Swedish primary care: an interview study
inferred← citeshttps://doi.org/10.1186/s12875-025-02785-2openalex-snapshot
Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review
inferred← citeshttps://doi.org/10.1186/s12875-025-03157-6openalex-snapshot
Adoption of artificial intelligence in primary health care: systematic synthesis of stakeholder perspectives
inferred← citeshttps://doi.org/10.1186/s12909-024-05078-xopenalex-snapshot
Insights from semi-structured interviews on integrating artificial intelligence in clinical chemistry laboratory practices
inferred← citeshttps://doi.org/10.1186/s12909-024-05826-zopenalex-snapshot
Shaping future practices: German-speaking medical and dental students’ perceptions of artificial intelligence in healthcare
inferred← citeshttps://doi.org/10.1186/s12911-022-01984-6openalex-snapshot
Is primary health care ready for artificial intelligence? What do primary health care stakeholders say?
inferred← citeshttps://doi.org/10.1186/s12911-023-02162-yopenalex-snapshot
The impact of artificial intelligence on the person-centred, doctor-patient relationship: some problems and solutions
inferred← citeshttps://doi.org/10.1186/s12911-026-03614-xopenalex-snapshot
Ethical challenges to patient autonomy in the era of artificial intelligence: a systematic review
inferred← citeshttps://doi.org/10.1186/s12913-024-11667-9openalex-snapshot
Exploring the perspectives of healthcare professionals regarding artificial intelligence; acceptance and challenges
inferred← citeshttps://doi.org/10.1186/s12913-025-12664-2openalex-snapshot
Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels
inferred← citeshttps://doi.org/10.1186/s12913-026-15097-7openalex-snapshot
Patient-reported negative interpersonal experiences in Polish primary care and their association with healthcare avoidance: a large-scale cross-sectional study
inferred← citeshttps://doi.org/10.1186/s12967-019-02204-yopenalex-snapshot
Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France
inferred← citeshttps://doi.org/10.1186/s40942-025-00764-4openalex-snapshot
Global perspectives of ophthalmologists on artificial intelligence adoption in clinical practice
inferred← citeshttps://doi.org/10.1186/s44247-024-00066-zopenalex-snapshot
Bridging the voice of healthcare to digital transformation in practice – a holistic approach
inferred← citeshttps://doi.org/10.1200/cci-25-00300openalex-snapshot
Knowledge and Use of Artificial Intelligence Among Oncology Faculty and Trainees at a Comprehensive Cancer Center in 2025
inferred← citeshttps://doi.org/10.1200/cci.23.00023openalex-snapshot
Study of Patient and Physician Attitudes Toward Automated Prognostic Models for Patients With Metastatic Cancer
inferred← citeshttps://doi.org/10.12923/pielxxiw-2026-0002openalex-snapshot
Nurses’ perceptions of artificial intelligence use in clinical practice: a qualitative study
inferred← citeshttps://doi.org/10.1344/rbd2023.57.35146openalex-snapshot
Aspectos bioéticos da utilização de sistemas de inteligência artificial no campo da saúde: um estudo exploratório
inferred← citeshttps://doi.org/10.1370/afm.2887openalex-snapshot
Competencies for the Use of Artificial Intelligence in Primary Care
inferred← citeshttps://doi.org/10.1371/journal.pdig.0000114openalex-snapshot
German medical students´ views regarding artificial intelligence in medicine: A cross-sectional survey
inferred← citeshttps://doi.org/10.1371/journal.pdig.0000255openalex-snapshot
An evidence-based approach to artificial intelligence education for medical students: A systematic review
inferred← citeshttps://doi.org/10.1371/journal.pdig.0000864openalex-snapshot
AI-driven healthcare: A review on ensuring fairness and mitigating bias
inferred← citeshttps://doi.org/10.1371/journal.pdig.0001052openalex-snapshot
A quantitative study of pathologists’ perceptions towards artificial intelligence-assisted diagnostic system
inferred← citeshttps://doi.org/10.1371/journal.pdig.0001212openalex-snapshot
Hierarchy and hope: Exploring AI’s role in medicine through a thematic analysis of online discourse
inferred← citeshttps://doi.org/10.1371/journal.pdig.0001353openalex-snapshot
Use of generative AI for health among urban youth in Pakistan: A mixed-methods study
inferred← citeshttps://doi.org/10.1371/journal.pone.0239947openalex-snapshot
US primary care in 2029: A Delphi survey on the impact of machine learning
inferred← citeshttps://doi.org/10.1371/journal.pone.0282415openalex-snapshot
Exploring the experiences and views of doctors working with Artificial Intelligence in English healthcare; a qualitative study
inferred← citeshttps://doi.org/10.1371/journal.pone.0294028openalex-snapshot
American public opinion on artificial intelligence in healthcare
inferred← citeshttps://doi.org/10.1371/journal.pone.0314196openalex-snapshot
Implementation challenges of artificial intelligence (AI) in primary care: Perspectives of general practitioners in London UK
inferred← citeshttps://doi.org/10.15694/mep.2021.000075.1openalex-snapshot
Knowledge and Attitudes on Artificial Intelligence in Healthcare: A Provincial Survey Study of Medical Students
inferred← citeshttps://doi.org/10.1590/2358-2898202514510070popenalex-snapshot
Aplicação da Inteligência Artificial na Atenção Primária à Saúde: revisão de escopo e avaliação crítica
inferred← citeshttps://doi.org/10.18203/issn.2454-2156.intjscirep20254113openalex-snapshot
The dark side of artificial intelligence in medical education and the healthcare system: challenges and strategies for a balanced approach: a systematic meta-analysis
inferred← citeshttps://doi.org/10.21203/rs.3.rs-10343485/v1openalex-snapshot
Explainable Artificial Intelligence (XAI) in Medical Education: A Multi- Modal Framework for Enhancing Human-AI Collaboration
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4945818/v1openalex-snapshot
Exploring Trust In Artificial Intelligence Among Primary Care Stakeholders: A Mixed-Methods Study
inferred← citeshttps://doi.org/10.21203/rs.3.rs-5454088/v1openalex-snapshot
Generative Artificial Intelligence in Medicine: A Mixed Methods Survey of UK General Practitioners
inferred← citeshttps://doi.org/10.21203/rs.3.rs-7019423/v1openalex-snapshot
How useful are artificial intelligence programs used by patients to obtain information prior to sleeve gastrectomy surgery?
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9066307/v1openalex-snapshot
Clinician Perceptions of Artificial Intelligence in Healthcare and Frameworks for Ensuring Safe Integration into Clinical Practice of the West African College of Physicians: A Multi-Country Mixed-Methods Study
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9088143/v1openalex-snapshot
Integration of Artificial Intelligence into Medical Education: A Mixed-Methods Study Identifying the Risks, Safeguards, and Optimal Approaches to Protect Clinical Skill Development across West African Countries
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9095240/v1openalex-snapshot
Cognitive Stitching: Informal AI Integration in Everyday Clinical Reasoning
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9832908/v1openalex-snapshot
Women’s knowledge and attitude scale toward ai-based chatbot systems in preconception counseling: A scale development study
inferred← citeshttps://doi.org/10.2139/ssrn.5349881openalex-snapshot
Welsh Healthcare in the AI Era: A Reflection on the Digital Evolution of Welsh Healthcare and Its Future in the Forthcoming AI Era
inferred← citeshttps://doi.org/10.2139/ssrn.5362039openalex-snapshot
Transparency and Accountability in Healthcare AI: A Multilayered Survey on Fairness, Bias Mitigation, and Regulatory Compliance
inferred← citeshttps://doi.org/10.2139/ssrn.6056414openalex-snapshot
Overdiagnosis by Information Overload: Self-diagnosis, health anxiety, and the clinical visit as a validation encounter
inferred← citeshttps://doi.org/10.2139/ssrn.6632719openalex-snapshot
Theoretical Exploration of Error Thresholds for Clinical AI Decision Support in Nursing: An Exploratory Simulation Study Grounded in Human-AI Reliance Data
inferred← citeshttps://doi.org/10.2147/amep.s528281openalex-snapshot
Knowledge Attitudes and Ethical Concerns About Artificial Intelligence Among Medical Students at Taibah University: A Cross-Sectional Study
inferred← citeshttps://doi.org/10.21673/anadoluklin.1770408openalex-snapshot
Perceptions; attitudes and anxiety toward artificial intelligence among medical students: A cross-sectional study
inferred← citeshttps://doi.org/10.2196/103006openalex-snapshot
Primary Care Doctors’ Perspectives and Experiences With a Chest X-Ray AI Triage Program: Qualitative Study
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