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The end of medicine as we know it - and why your health has a future pp 201–216Cite as

Big Data Medicine

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Abstract

Every minute of the day, approximately 3 petabytes (in bytes, that would be a number with 15 zeros!) of internet data are generated. Considering the exposome, many of them can be declared as health-related data in a narrower or broader sense. Only computers provide a solution for the analysis of these gigantic amounts of data for the purpose of individual diagnoses.

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Notes

  1. 1.

    Data Never Sleeps 7.0 (2019) DOMO, https://www.domo.com/learn/data-never-sleeps-7

  2. 2.

    Shortliffe E (1976) Computer-Based Medical Consultations: MYCIN, Elsevier, 264 p., eBook ISBN: 9780444601735.

  3. 3.

    Aitken M et al. (2017) The growing value of digital health: evidence and impact on human health and the healthcare system. IQVIA Institute for Human Data Science, https://www.iqvia.com/institute/reports/the-growing-value-of-digital-health

  4. 4.

    https://medicalfuturist.com/fda-approved-ai-based-algorithms/

  5. 5.

    Samuel AL (2000) Some studies in machine learning using the game of checkers. IBM J Res Dev 44:206–226

  6. 6.

    Lunden I (2020) Babylon Health is building an integrated, AI-based health app to serve a city of 300 K in England. Techcrunch, https://techcrunch.com/2020/01/22/babylon-health-is-building-an-integrated-ai-based-health-app-to-serve-a-city-of-300k-in-england

  7. 7.

    Ricci M (2017) Sensely and Mayo Clinic take virtual nurse one step further. Pharmaphorum, https://pharmaphorum.com/news/sensely-mayo-clinic-develop-virtual-doctor

  8. 8.

    Benjamens S et al. (2020) The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. Npj Digit Med 3:118, https://doi.org/10.1038/s41746-020-00324-0

  9. 9.

    Kang DY et al. (2020) Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services. Scand J Trauma Resusc Emerg Med 28:17

  10. 10.

    Liu X et al. (2019) A comparison of deep learning performance against healthcare professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digital Health 1:e271–97, https://doi.org/10.1016/S2589-7500(19)30123-2

  11. 11.

    Ardila D et al. (2019) End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 25:954–961, https://doi.org/10.1038/s41591-019-0447-x

  12. 12.

    McKinney SM et al. (2020) International evaluation of an AI system for breast cancer screening. Nature 577:89–94, https://doi.org/10.1038/s41586-019-1799-6

  13. 13.

    Matek C et al. (2019) Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks. Nat Mach Intell 1:538–544, https://doi.org/10.1038/s42256-019-0101-9

  14. 14.

    Williams SA et al. (2019) Plasma protein patterns as comprehensive indicators of health. Nat Med 25:1851–1857, https://doi.org/10.1038/s41591-019-0665-2

  15. 15.

    Liu MC et al. (2020) Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Annals of Oncology 31:745–759, https://doi.org/10.1016/j.annonc.2020.02.011

  16. 16.

    Ofman JJ (2020) GRAIL and the quest for earlier multi-cancer detection. Nature, https://media.nature.com/original/magazine-assets/d42473-020-00079-y/d42473-020-00079-y.pdf

  17. 17.

    Brinker TJ et al. (2019) Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur J of Cancer 113:47–54, https://doi.org/10.1016/j.ejca.2019.04.001

  18. 18.

    Liu Y et al. (2020) A deep learning system for differential diagnosis of skin diseases. Nat Med 26, 900–908, https://doi.org/10.1038/s41591-020-0842-3; Jain A et al. (2021) Development and Assessment of an Artificial Intelligence–Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners in Teledermatology Practices. JAMA Netw Open 4:e217249, https://doi.org/10.1001/jamanetworkopen.2021.7249

  19. 19.

    Assmann G et al. (2002) Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Münster (PROCAM) study. Circulation. 105:310–315, https://doi.org/10.1161/hc0302.102575, Erratum (2002) Circulation 105:900, PMID: 11804985, PROCAM score calculator: http://www.scores.bnk.de/procam.html

  20. 20.

    Weng SF et al. (2017) Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS One, 12:e0174944, https://doi.org/10.1371/journal.pone.0174944

  21. 21.

    Daniel MC et al. (2019) Automated segmentation of the corneal endothelium in a large set of ‘real-world’ specular microscopy images using the U-Net architecture. Sci Rep 9:4752, https://doi.org/10.1038/s41598-019-41034-2

  22. 22.

    De Fauw J et al. (2018) Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 24:1342–1350, https://doi.org/10.1038/s41591-018-0107-6; Arcadu F et al. (2019) Deep learning algorithm predicts diabetic retinopathy progression in individual patients. Npj Digit Med. 2:92, https://doi.org/10.1038/s41746-019-0172-3

  23. 23.

    Liang H et al. (2019) Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med 25:433–438, https://doi.org/10.1038/s41591-018-0335-9

  24. 24.

    Perez MV et al. (2019) Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med 381:1909–1917, https://doi.org/10.1056/NEJMoa1901183

  25. 25.

    Chugh SS et al. (2014) Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study. Circulation 129:837–47; Gallagher C et al. (2019) Increasing trends in hospitalisations due to atrial fibrillation in Australia from 1993 to 2013. Heart 105:1358–1363, https://doi.org/10.1136/heartjnl-2018-314471

  26. 26.

    Sanna T et al. (2014) Cryptogenic stroke and underlying atrial fibrillation. N Engl J Med 370:2478–86

  27. 27.

    Dorr M et al. (2019) The WATCH AF trial: smartwatches for detection of atrial fibrillation. JACC Clin Electrophysiol 5:199–208; Kotecha D et al. (2018) European Society of Cardiology smartphone and tablet applications for patients with atrial fibrillation and their healthcare providers. Europace 20:225–33

  28. 28.

    Vivalink, https://www.vivalnk.com/products/medical-wearable-sensors/continuous-ecg-monitor

  29. 29.

    Schoettker P et al. (2020) Blood pressure measurements with the OptiBP smartphone app validated against reference auscultatory measurements. Sci Rep 10:17827, https://doi.org/10.1038/s41598-020-74955-4

  30. 30.

    Sim I (2019) Mobile devices and health. N Engl J Med, 381:956–68, doi: 10.1056/NEJMe1913980

  31. 31.

    Hu L et al. (2019) An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening. Journal of the National Cancer Institute, 111:923–932, https://doi.org/10.1093/jnci/djy225

  32. 32.

    Singh H et al. (2014) The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations BMJ Quality & Safety 23:727–731; Saber Tehrani AS et al. (2013) 25-Year summary of US malpractice claims for diagnostic errors 1986–2010: an analysis from the National Practitioner Data Bank BMJ Quality & Safety 22:672–680.

  33. 33.

    Elliott RA et al. (2019) Economic analysis of the prevalence and clinical and economic burden of medication error in England. BMJ Quality & Safety 010206, https://doi.org/10.1136/bmjqs-2019-010206

  34. 34.

    https://www.basicthinking.de/blog/2018/08/29/daten-pro-stunde-google/

  35. 35.

    McMahan HB et al. (2017) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) Fort Lauderdale, Florida, USA. JMLR: W&CP 54, http://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf

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  1. Aachen, Germany

    Harald H. H. W. Schmidt

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  1. Harald H. H. W. Schmidt
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Schmidt, H.H.H.W. (2022). Big Data Medicine. In: The end of medicine as we know it - and why your health has a future. Springer, Cham. https://doi.org/10.1007/978-3-030-95293-8_16

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