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AIM in Primary Healthcare

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Artificial Intelligence in Medicine

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Abstract

Primary healthcare is a highly interesting generalist field in medicine. Over the coming years, this field will continue to profoundly benefit from AI in medicine, which will result in positive changes in the everyday lives of patients.

Medical specialist knowledge will reach out to primary healthcare settings, profoundly altering the whole referral system and its indications. Specialist domains will be distributed widely and remotely, as scientific advances will reach primary care patients and doctors more frequently, rapidly, and accurately, thus tilting the dependency balance in the patient-doctor relationship.

Personalized and precision healthcare will reach out to every clinic and patient, and nowhere will it be as obvious as in the primary healthcare setting. AI in primary care will also speed up disease theranostics, which will impact management decisions. Decision support will be abundant for the GP and the patient. Patient power will likely see an increase as patients become more active, well-informed, and independent in information discovery and learning about their own disease, a trend that has already occurred in most developed economies. This trend will likely continue in emerging economies through AI-powered mHealth platforms thanks to the rise in smart phone technologies.

Many areas within primary care are entering a revolution: Pharmacogenomics will profoundly change the way we prescribe medications. All types of pattern recognition in image-based specialties will essentially strengthen their presence in the primary care clinic: radiology ranging from flat X-rays to ultrasonography, dermatology, pathology, and parts of ophthalmology and scopic inspections, where other image pattern recognitions can be further expanded. Moreover, interpersonal psychotherapy, follow-ups, and compliance will be armored with surveillance, coaching, and instructing components.

Verily, in primary healthcare, the whole medical AI symphony will reach its soaring tutti and eventually the energetic confluence of all the thematic lines – incorporating the Allkunstwerk of AI in medicine.

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References

  1. Alami H, Rivard L, Lehoux P, Hoffman SJ, Cadeddu SBM, Savoldelli M, Samri MA, Ag Ahmed MA, Fleet R, Fortin JP. Artificial intelligence in health care: laying the foundation for responsible, sustainable, and inclusive innovation in low- and middle-income countries. Glob Health. 2020;16(1):52.

    Google Scholar 

  2. Bloom D, et al. The Global Economic Burden of Non-Communicable Diseases. A report by the World Economic Forum and the Harvard School of Public Health, September 2011. 2011.

    Google Scholar 

  3. WHO. Global Health Workforce alliance and World Health Organization. A universal truth: no health without a workforce. Available online: https://www.whoint/workforcealliance/knowledge/resources/GHWA-a_universal_truth_reportpdf?ua=1. 2013.

  4. Li L. Artificial intelligence and diagnosis in general practice. Br J Gen Pract. 2019;69(686):430.

    PubMed  PubMed Central  Google Scholar 

  5. Summerton N, Cansdale M. Artificial intelligence and diagnosis in general practice. Br J Gen Pract. 2019;69(684):324–5.

    PubMed  PubMed Central  Google Scholar 

  6. Rappoport N, Shamir, R. Multi-omic and multi-view clustering algorithms: review and cancer benchmark [published correction appears in Nucleic Acids Res. 2019 Jan 25;47(2):1044]. Nucleic Acids Res. 2018;46(20): 10546–10562.

    Google Scholar 

  7. Imison C, Curry, N, Holder, H, Castle-Clarke, S, Nimmons, D, Appleby, J, Thorlby, R and Lombardo, S. Shifting the balance of care: great expectations. Research report Nuffield Trust. 2017.

    Google Scholar 

  8. Sinsky C, Colligan L, Li L, Prgomet M, Reynolds S, Goeders L, Westbrook J, Tutty M, Blike G. Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties. Ann Intern Med. 2016;165(11):753–60. Epub 2016 Sep 6. PMID: 27595430.

    PubMed  Google Scholar 

  9. Liaw S, Liyanage H, Kuziemsky C, Terry AL, Schreiber R, Jonnagaddala J, de Lusignan S. Ethical use of electronic health record data and artificial intelligence: recommendations of the primary care informatics Working Group of the International Medical Informatics Association. Yearb Med Inform. 2020;29(1):51–7.

    PubMed  PubMed Central  Google Scholar 

  10. Liaw W, Kakadiaris IA. Primary care artificial intelligence: a branch hiding in plain sight. Ann Fam Med. 2020;18(3):194–5.

    PubMed  PubMed Central  Google Scholar 

  11. Liyanage H, Liaw ST, Jonnagaddala J, Schreiber R, Kuziemsky C, Terry AL, de Lusignan S. Artificial intelligence in primary health care: perceptions, issues, and challenges. Yearb Med Inform. 2019;28(1):41–6. https://doi.org/10.1055/s-0039-1677901.

    Article  PubMed  PubMed Central  Google Scholar 

  12. NHS. The Topol review: the Topol Review — NHS Health Education England. NHS Health Education England Retrieved 2020-03-11. 2018.

    Google Scholar 

  13. Topol E. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.

    CAS  PubMed  Google Scholar 

  14. Peiffer-Smadja N, Rawson TM, Ahmad R, Buchard A, Georgiou P, Lescure FX, Birgand G, Holmes AH. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clin Microbiol Infect. 2020;26(5):584–95.

    CAS  PubMed  Google Scholar 

  15. Ghatnekar S, Faletsky A, Nambudiri VE. Digital scribe utility and barriers to implementation in clinical practice: a scoping review. Health Technol (Berl). 2021;11:1–7.

    Google Scholar 

  16. Semigran H, Linder JA, Gidengil C, Mehrotra A. Evaluation of symptom checkers for self diagnosis and triage: audit study. BMJ. 2015;351:h3480.

    PubMed  PubMed Central  Google Scholar 

  17. Kroenke K, Jackson JL. Outcome in general medical patients presenting with common symptoms: a prospective study with a 2-week and a 3-month follow-up. Fam Pract. 1998;15(5):398–403.

    CAS  PubMed  Google Scholar 

  18. Chan S, Reddy V, Myers B, et al. Machine learning in dermatology: current applications, opportunities, and limitations. Dermatol Ther (Heidelb). 2020;10:365–86.

    Google Scholar 

  19. Burns D, Razmjou H, Shaw J, Richards R, McLachlin S, Hardisty M, Henry P, Whyne C. Adherence tracking with smart watches for shoulder physiotherapy in rotator cuff pathology: protocol for a Longitudinal Cohort Study. JMIR Res Protoc. 2020;9(7):e17841.

    PubMed  PubMed Central  Google Scholar 

  20. Myburgh H, Jose S, Swanepoel D, Laurent C. Towards low cost automated smartphone- and cloud-based otitis media diagnosis. Biomed Signal Process Cont. 2018;39:34–52.

    Google Scholar 

  21. Silva PJ, Jacobs D, Kriak J, Abu-Baker A, Udeani G, Neal G, Ramos K. Implementation of pharmacogenomics and artificial intelligence tools for chronic disease management in primary care setting. J Pers Med. 2021;11:443.

    PubMed  PubMed Central  Google Scholar 

  22. Primorac D, et al. Pharmacogenomics at the center of precision medicine: challenges and perspective in an era of Big Data. Pharmacogenomics. 2020;21(2):141–56.

    CAS  PubMed  Google Scholar 

  23. Guan J. Artificial intelligence in healthcare and medicine: promises, ethical challenges and governance. Chin Med Sci J. 2019;34(2):76–83.

    PubMed  Google Scholar 

  24. Darzi A. High quality care for all – NHS next stage review final report Department of Health. 2008.

    Google Scholar 

  25. Tversky A, Kahneman D. Judgement under uncertainty: heuristics and biases. Science. 1974;185(4157):1124–31.

    CAS  PubMed  Google Scholar 

  26. Sheringham J, Sequeira R, Myles J, et al. Variations in GPs’ decisions to investigate suspected lung cancer: a factorial experiment using multimedia vignettes. BMJ Qual Saf. 2017;26(6):449–59.

    PubMed  Google Scholar 

  27. Ryan P, Luz S, Albert P, Vogel C, Normand C, Elwyn G, et al. Using artificial intelligence to assess clinicians’ communication skills. BMJ. 2019;364:l161.

    PubMed  Google Scholar 

  28. Miles O. Acceptability of chatbot versus General Practitioner consultations for healthcare conditions varying in terms of perceived stigma and severity (Preprint). Qeios. 2020; https://doi.org/10.32388/BK7M49.

  29. Willis M, Duckworth P, Coulter A, Meyer ET, Osborne M. The future of health care: protocol for measuring the potential of task automation grounded in the National Health Service Primary Care System. JMIR Res Protoc. 2019;8(4):e11232.

    PubMed  PubMed Central  Google Scholar 

  30. Cirillo D, Catuara-Solarz S, Morey C, et al. Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. NPJ Digit Med. 2020;3:81.

    PubMed  PubMed Central  Google Scholar 

  31. Zakhem G, Motosko CC, Ho RS. How should artificial intelligence screen for skin cancer and deliver diagnostic predictions to patients? JAMA Dermatol. 2018;154(12):1383–4.

    PubMed  Google Scholar 

  32. Abràmoff M, Lou Y, Erginay A, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57:5200–6.

    PubMed  Google Scholar 

  33. Ting D, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, Tan GSW, Schmetterer L, Keane P, Wong TY. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167–75.

    PubMed  Google Scholar 

  34. Verma A, Bhatia S. A policy framework for health systems to promote triple aim innovation. Healthc Pap. 2016;15(3):9–23.

    PubMed  Google Scholar 

  35. Gubbi S, Hamet P, Tremblay J, Koch CA, Hannah-Shmouni F. Artificial intelligence and machine learning in endocrinology and metabolism: the Dawn of a New Era. Front Endocrinol (Lausanne). 2019;10:185. Published 2019 Mar 28. https://doi.org/10.3389/fendo.2019.00185

  36. Vettoretti M, Cappon G, Facchinetti A, Sparacino G. Advanced diabetes management using artificial intelligence and continuous glucose monitoring sensors. Sensors (Basel). 2020;20(14):3870. Published 2020 Jul 10

    CAS  Google Scholar 

  37. Van Doorn WPTM, Foreman YD, Schaper NC, Savelberg HHCM, Koster A, et al. Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: the Maastricht Study. PLoS One. 2021;16(6):e0253125.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Shakibfar S, Krause O, Lund-Andersen C, Aranda A, Moll J, Andersen TO, et al. Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning. EP Europace. 2018;21(2):268–74.

    Google Scholar 

  39. Ribeiro AH, Ribeiro MH, Paixão GMM, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020;11:1760.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Luo H, et al. Smartphone-based blood pressure measurement using transdermal optical imaging technology, circulation. Cardiovasc Imaging. 2019;12(8):e008857. https://doi.org/10.1161/CIRCIMAGING.119.008857. Epub 2019 Aug 6. PMID: 31382766

  41. Kaplan A, Cao H, FitzGerald JM, Iannotti N, Yang E, Kocks JWH, et al. Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis. J Allergy Clin Immunol Pract. 2021;9(6):2255–61.

    PubMed  Google Scholar 

  42. Liyanage H, Liaw ST, Jonnagaddala J, et al. Artificial intelligence in primary health care: perceptions, issues, and challenges. Yearb Med Inform. 2019;28(1):41–6.

    PubMed  PubMed Central  Google Scholar 

  43. Pedersen M, Verspoor K, Jenkinson M, Law M, Abbott DF, Jackson GD. Artificial intelligence for clinical decision support in neurology. Brain Commun. 2020;2(2):fcaa096. Published 2020 Jul 9

    PubMed  PubMed Central  Google Scholar 

  44. Cavedoni S, Chirico A, Pedroli E, Cipresso P, Riva G. Digital biomarkers for the early detection of mild cognitive impairment: artificial intelligence meets virtual reality. Front Hum Neurosci. 2020;14(245). Published 2020 Jul 24. https://doi.org/10.3389/fnhum.2020.00245

  45. Raghavendra U, Acharya UR, Adeli H. Artificial intelligence techniques for automated diagnosis of neurological disorders. Eur Neurol. 2019;82(1–3):41–64.

    CAS  PubMed  Google Scholar 

  46. Anna Z, et al. Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling. Int J Med Inform. 2020;138:104131.

    Google Scholar 

  47. Feduniw S, Sys D, Kwiatkowski S, Kajdy A. Application of artificial intelligence in screening for adverse perinatal outcomes: a protocol for systematic review. Medicine. 2020;99(50):e23681.

    PubMed  PubMed Central  Google Scholar 

  48. Davidson L, Boland MR. Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence. J Pharmacokinet Pharmacodyn. 2020;47:305–18.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Iftikhar P, et al. Artificial intelligence: a new paradigm in obstetrics and gynecology research and clinical practice. Cureus. 2020;12(2):e7124. Published 2020 Feb 28

    PubMed  PubMed Central  Google Scholar 

  50. Jones O, et al. Artificial intelligence techniques that may be applied to primary care data to facilitate earlier diagnosis of cancer: systematic review. J Med Internet Res. 2021;23(3):e23483.

    PubMed  PubMed Central  Google Scholar 

  51. Pak M, Kim S. A review of deep learning in image recognition. 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), 2017, p. 1–3.

    Google Scholar 

  52. Ker J, et al. Deep learning applications in medical image analysis. in IEEE Access, vol 6, p. 9375–9389, 2018.

    Google Scholar 

  53. Zame W, Bica I, Shen C, Curth A, Lee H-S, Bailey S, et al. Machine learning for clinical trials in the era of COVID-19. Stat Biopharm Res. 2020;12(4):506–17.

    PubMed  PubMed Central  Google Scholar 

  54. Bae K, et al. Pulmonary nodules: automated detection on CT images with morphologic matching algorithm—preliminary results. Radiology. 2005;236(1):286–93.

    PubMed  Google Scholar 

  55. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.

    PubMed  Google Scholar 

  56. Gunčar G, Kukar M, Notar M, et al. An application of machine learning to haematological diagnosis. Sci Rep. 2018;8:411.

    PubMed  PubMed Central  Google Scholar 

  57. Schubart J, Fowler CE, Donowitz GR, Connors AF Jr. Algorithm-based decision rules to safely reduce laboratory test ordering. Stud Health Technol Inform. 2001;84(Pt 1):523–7. PMID: 1160479.

    CAS  PubMed  Google Scholar 

  58. Dalton L, Ballarin V, Brun M. Clustering algorithms: on learning, validation, performance, and applications to genomics. Curr Genomics. 2009;10(6):430–445. https://doi.org/10.2174/138920209789177601

  59. Yang L, et al. Gut microbiota-based algorithms in the prediction of metachronous adenoma in colorectal cancer patients following surgery. Front Microbiol. 2020;11:1106.

    Google Scholar 

  60. Thomas A, Barriere S, Broseus L, et al. GECKO is a genetic algorithm to classify and explore high throughput sequencing data. Commun Biol. 2:222, 2019.

    Google Scholar 

  61. Webster P. Patient data in the cloud. Lancet, Digital Health. 2019;1(8):E391–2.

    Google Scholar 

  62. Haras C, et al. Patient data synchronization process in a continuity of care environment. AMIA Annu Symp Proc. 2005;2005:296–300.

    PubMed Central  Google Scholar 

  63. Yu T. The design of electronic medical records system using Skip-gram algorithm. Netw Model Anal Health Inform Bioinform. 2021;10:7.

    Google Scholar 

  64. Google. GoogleCloud Platform. Online Accessed June 2021. https://www.cloudgooglecom/ai-platform/training/docs/algorithms

  65. Schrödle B, Held L. Spatio-temporal disease mapping using INLA. Environmetrics. 2011;22:725–34.

    Google Scholar 

  66. Delgadillo J. Machine learning: a primer for psychotherapy researchers. Psychother Res. 2021;31(1):1–4.

    PubMed  Google Scholar 

  67. Oh K, Lee, D, Ko, B, Choi, H. A chatbot for psychiatric counseling in mental healthcare service based on emotional dialogue analysis and sentence generation. 18th IEEE International Conference on Mobile Data Management (MDM), 2017, p. 371–375.

    Google Scholar 

  68. Nasehi S, Pourghassem H. Seizure detection algorithms based on analysis of EEG and ECG signals: a survey. Neurophysiology. 2012;44:174–86.

    Google Scholar 

  69. Adli M, et al. Algorithms and collaborative-care systems for depression: are they effective and why?: a systematic review. Biol Psychiatry. 2006;59(11):1029–38.

    PubMed  Google Scholar 

  70. Granja C, Almada-Lobo B, Janela F, Seabra J, Mendes A. An optimization based on simulation approach to the patient admission scheduling problem using a linear programing algorithm. J Biomed Inform. 2014;52:427–37.

    CAS  PubMed  Google Scholar 

  71. Hippisley-Cox J, Coupland C, Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ. 2017;357:j2099.

    PubMed  PubMed Central  Google Scholar 

  72. Parker SK, Grote G. Automation, algorithms, and beyond: why work design matters more than ever in a digital world. Appl Psychol Int Rev. 2020:1–45. https://doi.org/10.1111/apps.12241

  73. Shalit U, Johansson, F, Sontag, D. Estimating individual treatment effect: generalization bounds and algorithms. Proceedings of the 34th International Conference on Machine Learning. PMLR 70: 3076–3085, 2017.

    Google Scholar 

  74. Christophe Le Tourneau MK, Tsimberidou A-M, Bedard P, Pierron G, Callens C, Rouleau E, Vincent-Salomon A, Servant N, Alt M, Rouzier R, Paoletti X, Delattre O, Bièche I. Treatment algorithms based on tumor molecular profiling: the essence of precision medicine trials. JNCI. 2016;108(4):djv362.

    Google Scholar 

  75. Zame W, Bica I, Shen C, Curth A, et al. Machine learning for clinical trials in the era of COVID-19. Stat Biopharm Res. 2020;12(4):506–17.

    PubMed  PubMed Central  Google Scholar 

  76. van Ginneken B. Grand challenges. Available from: https://grand-challenge.org/

  77. Gupta R, Srivastava D, Sahu M, et al. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers. 2021;25:1315–1360. https://doi.org/10.1007/s11030-021-10217-3

  78. Imhoff MK, Kuhls S. Algorithms in critical care monitoring. Anesth Analg. 2006;102(5):1525–37.

    PubMed  Google Scholar 

  79. Wang S, Yang DM, Rong R, Zhan X, Xiao G. Pathology image analysis using segmentation deep learning algorithms. Am J Pathol. 2019;189(9):1686–98.

    PubMed  PubMed Central  Google Scholar 

  80. Carnier M, Gavidia L, Severeyn E, La Cruz A. ToraxIA: virtual assistant for radiologists based on deep learning from chest x-ray. Artificial Intelligence, Computer and Software Engineering Advances. 2021;1326:49–63. Published 2021 Feb 15.

    Google Scholar 

  81. Muche-Borowski C, Lühmann D, Schäfer I, The Guideline Group of the German College of General Practice and Family Medicine (DEGAM), et al. Development of a meta-algorithm for guiding primary care encounters for patients with multimorbidity using evidence-based and case-based guideline development methodology. BMJ Open. 2017;7:e015478.

    PubMed  PubMed Central  Google Scholar 

  82. ter Haar Romeny BM, Bekkers EJ, Zhang J, et al. Brain-inspired algorithms for retinal image analysis. Mach Vis Appl. 2016;27:1117–35. https://doi.org/10.1007/s00138-016-0771-9.

    Article  Google Scholar 

  83. Abbasi-Sureshjani S, Dashtbozorg B, ter Haar Romeny BM, Fleuret F. Exploratory study on direct prediction of diabetes using deep residual networks. In: Tavares J, Natal Jorge R, editors. VipIMAGE 2017. ECCOMAS 2017, Lecture notes in computational vision and biomechanics, vol. 27. Cham: Springer; 2018. https://doi.org/10.1007/978-3-319-68195-5_86.

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden

    Niklas Lidströmer

  2. Imperial College London NHS Trust, London, UK

    Joseph Davids

  3. Health Education England, London, UK

    Harpreet S. Sood

  4. Hamlyn Centre for Robotics and Artificial Intelligence, Department of Surgery and Cancer, Imperial College London, London, UK

    Joseph Davids & Hutan Ashrafian

  5. National Hospital for Neurology and Neurosurgery Queen Square, London, UK

    Joseph Davids

  6. Department of Surgery and Cancer, Imperial College London NHS Trust, London, UK

    Hutan Ashrafian

  7. Institute of Global Health Innovation, Imperial College London, London, UK

    Hutan Ashrafian

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  1. Niklas Lidströmer
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  2. Joseph Davids
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  3. Harpreet S. Sood
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Correspondence to Niklas Lidströmer .

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Editors and Affiliations

  1. Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden

    Niklas Lidströmer

  2. Imperial College London, London, UK

    Hutan Ashrafian

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Lidströmer, N., Davids, J., Sood, H.S., Ashrafian, H. (2022). AIM in Primary Healthcare. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-64573-1_340

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