Artificial Intelligence is changing Health and eHealth care
Keywords:Artificial Intelligence, clinical decision support, electronic health record systems
Artificial Intelligence (AI) will be used more and more in the healthcare industry as a result of the complexity and growth of data in the sector. Payers, care providers, and life sciences organisations currently use a variety of AI technologies. The main application categories include recommendations for diagnosis and treatment, patient engagement and adherence, and administrative tasks. Although there are many situations in which AI can execute healthcare duties just as well as or better than humans, implementation issues will keep the jobs of healthcare professionals from becoming extensively automated for a substantial amount of time. The use of AI in healthcare and ethical concerns are also highlighted.
Hechler E, Oberhofer M, Schaeck T. Deploying AI in the Enterprise. IT Approaches for Design, DevOps, Governance, Change Management, Blockchain, and Quantum Computing, Apress, Berkeley, CA. 2020. DOI: https://doi.org/10.1007/978-1-4842-6206-1
Lee SI, Celik S, Logsdon BA, Lundberg SM, Martins TJ, Oehler VG, Estey EH, Miller CP, Chien S, Dai J, Saxena A. A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nature communications. 2018 Jan 3;9(1):1-3. DOI: https://doi.org/10.1038/s41467-017-02465-5
Sordo M. Introduction to neural networks in healthcare. Open Clinical: Knowledge Management for Medical Care. 2002 Oct.
Fakoor R, Ladhak F, Nazi A, Huber M. Using deep learning to enhance cancer diagnosis and classification. InProceedings of the international conference on machine learning 2013 Jun (Vol. 28, pp. 3937-3949). ACM, New York, USA.
Vial A, Stirling D, Field M, Ros M, Ritz C, Carolan M, Holloway L, Miller AA. The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. Transl Cancer Res. 2018 Jun 1;7(3):803-16. DOI: https://doi.org/10.21037/tcr.2018.05.02
Davenport TH, Glaser J. Just-in-time delivery comes to knowledge management. Harvard business review. 2002 Jul 1;80(7):107-1.
Hussain A, Malik A, Halim MU, Ali AM. The use of robotics in surgery: a review. International journal of clinical practice. 2014 Nov;68(11):1376-82. DOI: https://doi.org/10.1111/ijcp.12492
Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future healthcare journal. 2019 Jun;6(2):94. DOI: https://doi.org/10.7861/futurehosp.6-2-94
Grosan C, Abraham A. Rule-based expert systems. In Intelligent systems 2011 (pp. 149-185). Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-642-21004-4_7
Ross C, Swetlitz I. IBM pitched its Watson supercomputer as a revolution in cancer care. It’s nowhere close. Stat. 2017 Sep 5.
Davenport TH. The AI advantage: How to put the artificial intelligence revolution to work. mit Press; 2018 Oct 16. DOI: https://doi.org/10.7551/mitpress/11781.001.0001
Coulter A, Collins A. Making shared decision-making a reality. London: King's Fund. 2011.
Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, Brink J. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. Journal of the American College of Radiology. 2018 Mar 1;15(3):504-8. DOI: https://doi.org/10.1016/j.jacr.2017.12.026
Schmidt-Erfurth U, Bogunovic H, Sadeghipour A, Schlegl T, Langs G, Gerendas BS, Osborne A, Waldstein SM. Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration. Ophthalmology Retina. 2018 Jan 1;2(1):24-30. DOI: https://doi.org/10.1016/j.oret.2017.03.015
Aronson SJ, Rehm HL. Building the foundation for genomics in precision medicine. Nature. 2015 Oct;526(7573):336-42. DOI: https://doi.org/10.1038/nature15816
Rysavy M. Evidence-based medicine: a science of uncertainty and an art of probability. AMA Journal of Ethics. 2013 Jan 1;15(1):4-8. DOI: https://doi.org/10.1001/virtualmentor.2013.15.1.fred1-1301
Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, Liu PJ, Liu X, Marcus J, Sun M, Sundberg P. Scalable and accurate deep learning with electronic health records. NPJ digital medicine. 2018 May 8;1(1):1-0. DOI: https://doi.org/10.1038/s41746-018-0029-1
Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ open respiratory research. 2017 Nov 1;4(1):e000234. DOI: https://doi.org/10.1136/bmjresp-2017-000234
Nait Aicha A, Englebienne G, Van Schooten KS, Pijnappels M, Kröse B. Deep learning to predict falls in older adults based on daily-life trunk accelerometry. Sensors. 2018 May 22;18(5):1654. DOI: https://doi.org/10.3390/s18051654
Low LL, Lee KH, Hock Ong ME, Wang S, Tan SY, Thumboo J, Liu N. Predicting 30-day readmissions: performance of the LACE index compared with a regression model among general medicine patients in Singapore. BioMed research international. 2015 Oct;2015. DOI: https://doi.org/10.1155/2015/169870
Davenport TH, Hongsermeier T, Mc Cord KA. Using AI to improve electronic health records. Harvard Business Review. 2018 Dec 13;12:1-6.
Volpp KG, Mohta NS. Patient engagement survey: improved engagement leads to better outcomes, but better tools are needed. NEJM Catalyst. 2016 May 12;2(3).
Berg S. Nudge theory explored to boost medication adherence. Chicago: American Medical Association. 2018.
Commins J. Nurses say distractions cut bedside time by 25%. Health Leaders. 2010 Mar.
Utermohlen K. Four robotic process automation (RPA) applications in the healthcare industry. Medium. 2018.
Huang CY, Yang MC, Huang CY, Chen YJ, Wu ML, Chen KW. A chatbot-supported smart wireless interactive healthcare system for weight control and health promotion. In2018 IEEE international conference on industrial engineering and engineering management (IEEM) 2018 Dec 16 (pp. 1791-1795). IEEE. DOI: https://doi.org/10.1109/IEEM.2018.8607399
Deloitte LLP (Firm). From brawn to brains: the impact of technology on jobs in the UK, 2015.
Manyika J, Chui M, Miremadi M, Bughin J, George K, Willmott P, Dewhurst M. A future that works: AI, automation, employment, and productivity. McKinsey Global Institute Research, Tech. Rep. 2017 Jun;60:1-35.
Davenport TH, Kirby J. Only humans need apply: Winners and losers in the age of smart machines. New York: Harper Business; 2016 May 24.
Davenport TH, Dreyer K. AI will change radiology, but it won’t replace radiologists. Harvard Business Review. 2018 Mar 27;27.
Char DS, Shah NH, Magnus D. Implementing machine learning in health care—addressing ethical challenges. The New England journal of medicine. 2018 Mar 15;378(11):981. DOI: https://doi.org/10.1056/NEJMp1714229
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