On the public citation graph

Computerization and the future of primary care: A survey of general practitioners in the UK

https://doi.org/10.1371/journal.pone.0207418
0 signed · 117 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1001/jama.2013.281078openalex-snapshot
Can Mobile Health Technologies Transform Health Care?
inferredcites →https://doi.org/10.1001/jama.2013.393openalex-snapshot
The Inevitable Application of Big Data to Health Care
inferredcites →https://doi.org/10.1001/jama.2015.17452openalex-snapshot
Innovations in Health Care Delivery and the Future of Medicine
inferredcites →https://doi.org/10.1001/jama.2015.18421openalex-snapshot
Machine Learning and the Profession of Medicine
inferredcites →https://doi.org/10.1001/jama.2017.15028openalex-snapshot
The Evolution of Patient Diagnosis
inferredcites →https://doi.org/10.1001/jama.2017.19198openalex-snapshot
What This Computer Needs Is a Physician
inferredcites →https://doi.org/10.1001/jama.2018.11100openalex-snapshot
Deep Learning—A Technology With the Potential to Transform Health Care
inferredcites →https://doi.org/10.1001/jama.2018.11103openalex-snapshot
On the Prospects for a (Deep) Learning Health Care System
inferredcites →https://doi.org/10.1007/s11606-009-1129-zopenalex-snapshot
Single Item Measures of Emotional Exhaustion and Depersonalization Are Useful for Assessing Burnout in Medical Professionals
inferredcites →https://doi.org/10.1007/s11606-014-3112-6openalex-snapshot
Using a Single Item to Measure Burnout in Primary Care Staff: A Psychometric Evaluation
inferredcites →https://doi.org/10.1016/j.techfore.2016.08.019openalex-snapshot
The future of employment: How susceptible are jobs to computerisation?
inferredcites →https://doi.org/10.1016/s0140-6736(17)31540-4openalex-snapshot
Artificial intelligence in health care: within touching distance
inferredcites →https://doi.org/10.1056/nejme1213371openalex-snapshot
A Glimpse of the Next 100 Years in Medicine
inferredcites →https://doi.org/10.1056/nejmp1606181openalex-snapshot
Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
inferredcites →https://doi.org/10.1056/nejmp1705348openalex-snapshot
Lost in Thought — The Limits of the Human Mind and the Future of Medicine
inferredcites →https://doi.org/10.1086/341394openalex-snapshot
The Impact of “No Opinion” Response Options on Data Quality
inferredcites →https://doi.org/10.1093/fampra/cmv074openalex-snapshot
GPs’ adherence to guidelines for structured assessments of stroke survivors in the community and care homes
inferredcites →https://doi.org/10.1093/fampra/cmw018openalex-snapshot
Point-of-care testing in UK primary care: a survey to establish clinical needs
inferredcites →https://doi.org/10.1136/bmj.j3980openalex-snapshot
Margaret McCartney: Innovation without sufficient evidence is a disservice to all
inferredcites →https://doi.org/10.1136/bmj.k156openalex-snapshot
The apps attempting to transfer NHS 111 online
inferredcites →https://doi.org/10.1136/bmjqs-2011-000067openalex-snapshot
Development of a primary care physician task list to evaluate clinic visit workflow: Table 1
inferredcites →https://doi.org/10.1192/pb.bp.116.054247openalex-snapshot
Burnout and psychiatric morbidity among doctors in the UK: A systematic literature review of prevalence and associated factors
inferredcites →https://doi.org/10.1371/journal.pone.0058247openalex-snapshot
Placebo Use in the United Kingdom: Results from a National Survey of Primary Care Practitioners
inferredcites →https://doi.org/10.1787/2e2f4eea-enopenalex-snapshot
Automation, skills use and training
inferredcites →https://doi.org/10.2196/jmir.4456openalex-snapshot
How Consumers and Physicians View New Medical Technology: Comparative Survey
inferredcites →https://doi.org/10.2466/pms.1987.64.2.359openalex-snapshot
The Midpoint on a Five-Point Likert-Type Scale
inferredcites →https://doi.org/10.4258/hir.2015.21.4.321openalex-snapshot
Patient Will See You Now: The Future of Medicine is in Your Hands
inferredcites →https://doi.org/10.5664/jcsm.4788openalex-snapshot
The Patient Will See You Now: The Future of Medicine is in Your Hands

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4788661openalex-extracted
Healthcare Professionals’ Perceptions Towards Ai Integration in Jordan's Health Sector
inferred← citeshttps://doi.org/10.3897/popecon.10.e149635zenodo-depositor-declared
The Role of Psychological Factors in the Successful Digital Transformation of an Organization in Reducing Burnout Among Social Workers (Using Teachers as an Example)
inferred← citeshttps://doi.org/10.1007/978-3-658-33597-7_14openalex-snapshot
Zwischen Automatisierung und ethischem Anspruch – Disruptive Effekte des KI-Einsatzes in und auf Professionen der Gesundheitsversorgung
inferred← citeshttps://doi.org/10.1016/j.aprim.2026.103529openalex-snapshot
Performance of artificial intelligence in the collection of patient history in general practice
inferred← citeshttps://doi.org/10.1016/j.artmed.2019.101753openalex-snapshot
Artificial intelligence and the future of psychiatry: Insights from a global physician survey
inferred← citeshttps://doi.org/10.1016/j.csbj.2024.02.009openalex-snapshot
How do medical professionals make sense (or not) of AI? A social-media-based computational grounded theory study and an online survey
inferred← citeshttps://doi.org/10.1016/j.ijmedinf.2025.105945openalex-snapshot
Knowledge, attitude, and practice of primary care physicians toward clinical AI-assisted digital health technologies: Systematic review and meta-analysis
inferred← citeshttps://doi.org/10.1016/j.jaapos.2021.01.011openalex-snapshot
Evaluation of pediatric ophthalmologists' perspectives of artificial intelligence in ophthalmology
inferred← citeshttps://doi.org/10.1016/j.jhep.2022.03.003openalex-snapshot
“Beyond MELD” – Emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation
inferred← citeshttps://doi.org/10.1016/j.optom.2022.06.006openalex-snapshot
Optometrist's perspectives of Artificial Intelligence in eye care
inferred← citeshttps://doi.org/10.1017/s0022215124000550openalex-snapshot
Understanding, experience and attitudes towards artificial intelligence technologies for clinical decision support in hearing health: a mixed-methods survey of healthcare professionals in the UK
inferred← citeshttps://doi.org/10.1055/s-0039-1677901openalex-snapshot
Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges
inferred← citeshttps://doi.org/10.1080/07399332.2022.2055760openalex-snapshot
Artificial intelligence and the future of midwifery: What do midwives think about artificial intelligence? A qualitative study
inferred← citeshttps://doi.org/10.1093/ajcp/aqaa170openalex-snapshot
The Value of Artificial Intelligence in Laboratory Medicine
inferred← citeshttps://doi.org/10.1093/jamia/ocaa094openalex-snapshot
Latent bias and the implementation of artificial intelligence in medicine
inferred← citeshttps://doi.org/10.1093/jamia/ocaa131openalex-snapshot
Envisioning an artificial intelligence documentation assistant for future primary care consultations: A co-design study with general practitioners
inferred← citeshttps://doi.org/10.1101/2023.08.21.23294326openalex-snapshot
Patients Accessing their Online Records in England: A Survey of General Practitioners’ Experiences and Opinions
inferred← citeshttps://doi.org/10.1108/jhom-04-2023-0127openalex-snapshot
Medical secretaries’ fears and opportunities in an increasingly digitalised workplace environment
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-2021-100480openalex-snapshot
Machine learning in medical education: a survey of the experiences and opinions of medical students in Ireland
inferred← citeshttps://doi.org/10.1136/bmjopen-2019-032412openalex-snapshot
Qualitative and quantitative approach to assess the potential for automating administrative tasks in general 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-2023-078158openalex-snapshot
Experiences and opinions of general practitioners with patient online record access: an online survey 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/leader-2024-001093openalex-snapshot
Perceptions of artificial intelligence in healthcare: a qualitative study among healthcare professionals in Jordan
inferred← citeshttps://doi.org/10.1167/tvst.10.7.14openalex-snapshot
Impact of Artificial Intelligence on Medical Education in Ophthalmology
inferred← citeshttps://doi.org/10.1177/0003489420903329openalex-snapshot
Factors Correlating to Burnout among Otolaryngology Residents
inferred← citeshttps://doi.org/10.1177/0003489420987194openalex-snapshot
Otolaryngology Resident Wellness, Training, and Education in the Early Phase of the COVID-19 Pandemic
inferred← citeshttps://doi.org/10.1177/09717218241265004openalex-snapshot
A Network Analysis of the Relationship Between Automation and Job Characteristics
inferred← citeshttps://doi.org/10.1177/1755738019856388openalex-snapshot
The future of general practice
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/10.1177/20552076231152167openalex-snapshot
Are physicians and medical students ready for artificial intelligence applications in healthcare?
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/10.1177/23821205241281648openalex-snapshot
Artificial Intelligence Readiness Among Jordanian Medical Students: Using Medical Artificial Intelligence Readiness Scale For Medical Students (MAIRS-MS)
inferred← citeshttps://doi.org/10.1186/s12888-025-07135-1openalex-snapshot
Psychiatrists’ and trainees’ knowledge, perception, and readiness for integration of artificial intelligence in mental health care in Nigeria
inferred← citeshttps://doi.org/10.1186/s12909-024-05874-5openalex-snapshot
Perspectives on virtual interviews and emerging technologies integration in family medicine residency programs: a cross-sectional survey study
inferred← citeshttps://doi.org/10.1186/s12910-024-01092-2openalex-snapshot
Encompassing trust in medical AI from the perspective of medical students: a quantitative comparative study
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/s12913-023-09493-6openalex-snapshot
Opinion research among Russian Physicians on the application of technologies using artificial intelligence in the field of medicine and health care
inferred← citeshttps://doi.org/10.1186/s12913-023-09740-wopenalex-snapshot
Needs and expectations for artificial intelligence in emergency medicine according to Canadian physicians
inferred← citeshttps://doi.org/10.1186/s40359-025-03071-7openalex-snapshot
Investigating psychotherapists’ attitudes towards artificial intelligence in psychotherapy
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/10.1371/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.pone.0239947openalex-snapshot
US primary care in 2029: A Delphi survey on the impact of machine learning
inferred← citeshttps://doi.org/10.14245/ns.2448098.049openalex-snapshot
Analyzing Large Language Models’ Responses to Common Lumbar Spine Fusion Surgery Questions: A Comparison Between ChatGPT and Bard
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/10.1590/1980-265x-tce-2020-0675openalex-snapshot
TECHNOSOCIALITY AND HEALTH PROMOTION IN THE DAILY LIVES OF PRIMARY CARE USERS: A SCOPING REVIEW
inferred← citeshttps://doi.org/10.20517/ais.2024.38openalex-snapshot
Patient perspectives on AI: a pilot study comparing large language model and physician-generated responses to routine cervical spine surgery questions
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/10.2196/12802openalex-snapshot
Artificial Intelligence and the Future of Primary Care: Exploratory Qualitative Study of UK General Practitioners’ Views
inferred← citeshttps://doi.org/10.2196/13863openalex-snapshot
Accuracy of a Chatbot (Ada) in the Diagnosis of Mental Disorders: Comparative Case Study With Lay and Expert Users
inferred← citeshttps://doi.org/10.2196/42639openalex-snapshot
Computerization of the Work of General Practitioners: Mixed Methods Survey of Final-Year Medical Students in Ireland
inferred← citeshttps://doi.org/10.2196/43496openalex-snapshot
Patient Online Record Access in English Primary Care: Qualitative Survey Study of General Practitioners’ Views
inferred← citeshttps://doi.org/10.2196/46020openalex-snapshot
Roles and Competencies of Doctors in Artificial Intelligence Implementation: Qualitative Analysis Through Physician Interviews
inferred← citeshttps://doi.org/10.2196/71678openalex-snapshot
Weighing Costs and Benefits of Delay and the Acceptance of Two Decision Support Tools in Mental Health Care: Scoping Study Using Quantitative and Qualitative Data
inferred← citeshttps://doi.org/10.2196/73602openalex-snapshot
Health Care Professionals’ Experiences and Opinions About Generative AI and Ambient Scribes in Clinical Documentation: Protocol for a Scoping Review
inferred← citeshttps://doi.org/10.2196/74428openalex-snapshot
Generative Artificial Intelligence in Primary Care: Qualitative Study of UK General Practitioners’ Views
inferred← citeshttps://doi.org/10.2196/preprints.42805openalex-snapshot
Manuscript title What are the roles and competencies of doctors in the artificial intelligence implementation society? A qualitative analysis through physician interview (Preprint)
inferred← citeshttps://doi.org/10.2196/preprints.65318openalex-snapshot
Generative Artificial Intelligence in Medicine: A Mixed Methods Survey of UK General Practitioners (Preprint)
inferred← citeshttps://doi.org/10.2196/preprints.74428openalex-snapshot
Generative AI in Primary Care: A qualitative study of UK General Practitioners’ Views (Preprint)
inferred← citeshttps://doi.org/10.2196/preprints.98859openalex-snapshot
Insights into the Integration of AI in Clinical Practice: A Survey of Swiss Physicians (Preprint)
inferred← citeshttps://doi.org/10.25259/medindia_12_2026openalex-snapshot
Artificial intelligence and the rise of algorithm driven self diagnosis
inferred← citeshttps://doi.org/10.3122/jabfm.2022.220171r1openalex-snapshot
Priorities for Artificial Intelligence Applications in Primary Care: A Canadian Deliberative Dialogue with Patients, Providers, and Health System Leaders
inferred← citeshttps://doi.org/10.3389/fdgth.2026.1879415openalex-snapshot
Physician versus patient use of AI for diabetes prevention: public perceptions and comfort levels
inferred← citeshttps://doi.org/10.3389/fpsyg.2021.620766openalex-snapshot
Preparing Workplaces for Digital Transformation: An Integrative Review and Framework of Multi-Level Factors
inferred← citeshttps://doi.org/10.3389/fpubh.2021.623088openalex-snapshot
Machine Learning in Clinical Psychology and Psychotherapy Education: A Mixed Methods Pilot Survey of Postgraduate Students at a Swiss University
inferred← citeshttps://doi.org/10.3390/clinpract15080138openalex-snapshot
Artificial Intelligence in Primary Care: Support or Additional Burden on Physicians’ Healthcare Work?—A Qualitative Study
inferred← citeshttps://doi.org/10.3390/diagnostics16172848openalex-snapshot
Large Language Models in Perioperative Antithrombotic Management: A Blinded Scenario-Based Expert Evaluation
inferred← citeshttps://doi.org/10.37191/mapsci-jasr-3(1)-019openalex-snapshot
Perspectives of Anesthesiologists Towards the Use of Artificial Intelligence in Anesthesia Practice in a Developing Country
inferred← citeshttps://doi.org/10.37829/hf-2021-i03openalex-snapshot
Switched on: How do we get the best out of automation and AI in health care?
inferred← citeshttps://doi.org/10.4018/ijitsa.381803openalex-snapshot
The Influence of Artificial Intelligence Technology and Human-Machine Interaction Industrial Robot on Labor Employment Under Educational Psychology
inferred← citeshttps://doi.org/10.4103/jfcm.jfcm_144_24openalex-snapshot
The wizard of artificial intelligence: Are physicians prepared?
inferred← citeshttps://doi.org/10.4414/smw.2022.w30196openalex-snapshot
Measuring workload of Swiss general practice: a five-yearly questionnaire-based survey on general practitioners’ self-reported working activities (2005–2020)
inferred← citeshttps://doi.org/10.48550/arxiv.2001.09778openalex-snapshot
Artificial intelligence in medicine and healthcare: a review and classification of current and near-future applications and their ethical and social Impact
inferred← citeshttps://doi.org/10.51594/csitrj.v5i4.1026openalex-snapshot
ARTIFICIAL INTELLIGENCE FOR SYSTEMS ENGINEERING COMPLEXITY: A REVIEW ON THE USE OF AI AND MACHINE LEARNING ALGORITHMS
inferred← citeshttps://doi.org/10.51847/36mpdz9az8openalex-snapshot
Medical Students’ Perspectives on Trust in Medical AI: A Quantitative Comparative Study
inferred← citeshttps://doi.org/10.55783/rcmf.140206openalex-snapshot
Descripción de la implantación y grado de desarrollo de tecnología de comunicación e informática de los equipos de Atención Primaria en los servicios autonómicos de salud en España
inferred← citeshttps://doi.org/10.5772/intechopen.114907openalex-snapshot
Ethics of Artificial Intelligence: Implications for Primary Care and Family Medicine Residency Programs
inferred← citeshttps://doi.org/10.5812/jjcdc-143796openalex-snapshot
Obstacles to the Development of the Use of Artificial Intelligence from the Point of View of Physicians Working in Selected AJA Medical Centers: A Qualitative Content Analysis Study
inferred← citeshttps://doi.org/10.6084/m9.figshare.21077165openalex-snapshot
Additional file 1 of Is primary health care ready for artificial intelligence? What do primary health care stakeholders say?
inferred← citeshttps://doi.org/10.6084/m9.figshare.26606542openalex-snapshot
Additional file 1 of Needs and expectations for artificial intelligence in emergency medicine according to Canadian physicians
inferred← citeshttps://doi.org/10.6084/m9.figshare.26606545openalex-snapshot
Additional file 2 of Needs and expectations for artificial intelligence in emergency medicine according to Canadian physicians
inferred← citeshttps://doi.org/10.6084/m9.figshare.26606548openalex-snapshot
Additional file 3 of Needs and expectations for artificial intelligence in emergency medicine according to Canadian physicians
inferred← citeshttps://doi.org/10.6084/m9.figshare.26606551openalex-snapshot
Additional file 4 of Needs and expectations for artificial intelligence in emergency medicine according to Canadian physicians
inferred← citeshttps://doi.org/10.6084/m9.figshare.26966403openalex-snapshot
Additional file 1 of Perspectives on virtual interviews and emerging technologies integration in family medicine residency programs: a cross-sectional survey study
inferred← citeshttps://doi.org/10.7759/cureus.75518openalex-snapshot
Implementing Artificial Intelligence in Family Medicine: Challenges and Limitations
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.