BowmanCE (2022)
There is nothing medically magical about machine learning. Journal of the Royal Society115(9): 332. DOI: 10.1177/01410768221123239.
2.
CoomperMSornalingamSHeathJ, et al. (2022)
Consultation dynamics and strategies: The Brighton guide. InnovAiT15(7): 419–424. DOI: 10.1177/17557380221093654.
3.
CooperMSornalingamSHeathJ (2023)
‘Bot-centred’ consultations are likely to increase GP workload. Rapid response to Uddin Y, Nair A, Shariq S, Hannan S H. Transforming primary healthcare through natural language processing and big data analytics. BMJ381: 948. DOI: 10.1136/bmj.p948.
4.
CooperMSornalingamSJegatheesanM, et al. (2022)
The undergraduate 'corridor of uncertainty': teaching core concepts for managing clinical uncertainty as the 'special technique' of general practice. Education for Primare Care33: 120–124. DOI: 10.1080/14739879.2021.1996276.
5.
CooperMSornalingamS, andO'donnellC (2015)
Street-level bureaucracy: An underused theoretical model for general practice?British Journal of General Practice65(636): 376–3777. DOI: 10.3399/bjgp15X685921.
HeathI (2013)
Overdiagnosis: When good intentions meet vested interests–an essay by Iona Heath. BMJ347: f6361. DOI: 10.1136/bmj.f6361.
8.
HosnyAParmarCQuackenbushJ, et al. (2018)
Artificial intelligence in radiology. Nature Reviews Cancer18(8): 500–510. DOI: 10.1038/s41568-018-0016-5.
9.
LinSYMahoneyMRSinskyCA (2019)
Ten ways artificial intelligence will transform primary care. Journal of General Internal Medicine34(8): 1626–1630. DOI: 10.1007/s11606-019-05035-1.
WillisM.DuckworthPCoulterA, et al. (2020)
Qualitative and quantitative approach to assess of the potential for automating administrative tasks in general practice. BMJ Open10(6): e032412. DOI: 10.1136/bmjopen-2019-032412.