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Potential Benefits of Artificial Intelligence in Healthcare

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Artificial Intelligence and Machine Learning for Healthcare

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 229))

Abstract

Healthcare systems worldwide are confronted with numerous challenges such as an aging population, an increasing number of chronically ill patients, innovations as cost drivers and growing cost pressure. The COVID-19 pandemic causes additional burden for healthcare systems. In order to overcome these challenges, digital technologies are increasingly used. Especially the past decade witnessed a tremendous boom of artificial intelligence (AI) within the healthcare sector. AI has the potential to revolutionize healthcare and to mitigate the challenges healthcare systems are confronted with. The existing literature has frequently examined specific benefits of AI within the healthcare sector. However, there are still research gaps according to different application areas in healthcare. For this reason, an empirical study design has been conducted to investigate the potentials of AI in healthcare and to consequently identify its role. Based on a Systematic Literature Review (SLR), the following application areas for key determinants in healthcare have been identified: management tasks, medical diagnostics, medical treatment and drug discovery. By means of structural equation modeling (SEM), the study confirmed medical diagnostics and drug discovery as positive and significant influencing factors on the potential benefits of AI in healthcare. The other determinants didn’t prove a significant influence. Based on the findings of the study, various recommendations have been derived to further exploit the potentials of AI in healthcare.

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References

  1. Ng, A.: cited by C. Jewell, Artificial Intelligence: The New Electricity (2019), Available online at https://www.wipo.int/wipo_magazine/en/2019/03/article_0001.html. Accessed 20 March 2022

  2. Obschonka, M., Audretsch, D.B.: Artificial intelligence and big data in entrepreneurship: a new era has begun. Small Bus. Econ. 55(3), 529–539 (2020). https://doi.org/10.1007/s11187-019-00202-4

    Article  Google Scholar 

  3. Esmaeilzadeh, P.: Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives. BMC Med. Inform. Decis. Mak. 20, 170 (2020). https://doi.org/10.1186/s12911-020-01191-1

    Article  Google Scholar 

  4. Jiang, F., et al.: Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol. 2, 1–14 (2017). https://doi.org/10.1136/svn-2017-000101

    Article  Google Scholar 

  5. Bardhan, I., et al.: Connecting systems, data, and people: a multidisciplinary research roadmap for chronic disease management. MISQ 44(1), 185–200 (2020). https://doi.org/10.25300/MISQ/2020/14644

  6. Paranjape, K., et al.: Short keynote paper: mainstreaming personalized healthcare-transforming healthcare through new era of artificial intelligence. IEEE J. Biomed. Health Inf. 24(7), 1860–1863 (2020). https://doi.org/10.1109/JBHI.2020.2970807

    Article  Google Scholar 

  7. Meskó, B., et al.: Will artificial intelligence solve the human resource crisis in healthcare? BMC Health Serv. Res. 18, 545 (2018). https://doi.org/10.1186/s12913-018-3359-4

    Article  Google Scholar 

  8. Bundesministerium für Gesundheit: The German healthcare system: Strong. Reliable. Proven. (2020)

    Google Scholar 

  9. Akter, S., et al.: Transforming business using digital innovations: the application of AI, blockchain, cloud and data analytics. Ann. Oper. Res. 308, 7–39 (2022). https://doi.org/10.1007/s10479-020-03620-w

    Article  MATH  Google Scholar 

  10. Akay, A., Hess, H.: Deep learning: current and emerging applications in medicine and technology. IEEE J. Biomed. Health Inf. 23(3), 906–920 (2019). https://doi.org/10.1109/JBHI.2019.2894713

    Article  Google Scholar 

  11. Barda, A.J., et al.: A qualitative research framework for the design of user-centered displays of explanations for machine learning model predictions in healthcare. BMC Med Inform Decis. Mak. 20, 257 (2020). https://doi.org/10.1186/s12911-020-01276-x

    Article  Google Scholar 

  12. Hagan, R., et al.: Comparing regression and neural network techniques for personalized predictive analytics to promote lung protective ventilation in intensive care units. Comput. Biol. Med. 126, 104030 (2020). https://doi.org/10.1016/j.compbiomed.2020.104030

    Article  Google Scholar 

  13. Kocaballi, A.B., et al.: Envisioning an artificial intelligence documentation assistant for future primary care consultations: a co-design study with general practitioners. JAMIA 27(11), 1695–1704 (2020). https://doi.org/10.1093/jamia/ocaa131

    Article  Google Scholar 

  14. Keshavarzi Arshadi, A., et al.: Artificial intelligence for COVID-19 drug discovery and vaccine development. Front. Artif. Intell. 3, 65 (2020). https://doi.org/10.3389/frai.2020.00065

  15. Suri, J.S., et al.: COVID-19 pathways for brain and heart injury in comorbidity patients: a role of medical imaging and artificial intelligence-based COVID severity classification: a review. Comput. Biol. Med. 124, 103960 (2020). https://doi.org/10.1016/j.compbiomed.2020.103960

    Article  Google Scholar 

  16. Bertsimas, D., et al.: Personalized treatment for coronary artery disease patients: a machine learning approach. Health Care Manag. Sci. 23, 482–506 (2020). https://doi.org/10.1007/s10729-020-09522-4

    Article  Google Scholar 

  17. Fairley, M., et al.: Improving the efficiency of the operating room environment with an optimization and machine learning model. Health Care Manag. Sci. 22, 756–767 (2019). https://doi.org/10.1007/s10729-018-9457-3

    Article  Google Scholar 

  18. Reinhardt, R., Oliver, W.J.: The cost problem in health care. In: Gurtner, S., Soyez, K. (eds.) Challenges and Opportunities in Health Care Management, pp. 3–13. Springer International Publishing, Cham (2015). https://doi.org/10.1007/978-3-319-12178-9_1

  19. Denicolai, S., Previtali, P.: Precision Medicine: implications for value chains and business models in life sciences. Technol. Forecast Soc. Chang. 151, 119767 (2020). https://doi.org/10.1016/j.techfore.2019.119767

    Article  Google Scholar 

  20. Latan, H., Noonan R. (eds.): Editor's preface. In: Partial Least Squares Path Modeling. Cham, Springer International Publishing (2017). https://doi.org/10.1007/978-3-319-64069-3

  21. Tran, B.X., et al.: Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. J. Clin. Med. 8(3), 360 (2019). https://doi.org/10.3390/jcm8030360

    Article  Google Scholar 

  22. Gunn, A.A.: The diagnosis of acute abdominal pain with computer analysis. J. R. Coll. Surg. Edinb. 21(3), 170–172 (1976)

    Google Scholar 

  23. Bohr, A., Memarzadeh, K.: The rise of artificial intelligence in healthcare applications. Artif. Intell. Healthc. 25–60 (2020). https://doi.org/10.1016/B978-0-12-818438-7.00002-2

  24. Cosma, G., et al.: A survey on computational intelligence approaches for predictive modeling in prostate cancer. Expert Syst. Appl. 70, 1–19 (2017). https://doi.org/10.1016/j.eswa.2016.11.006

    Article  Google Scholar 

  25. Carter, S.M., et al.: The ethical, legal and social implications of using artificial intelligence systems in breast cancer care. Breast 49, 25–32 (2020). https://doi.org/10.1016/j.breast.2019.10.001

    Article  Google Scholar 

  26. MarketsandMarkets: Artificial Intelligence in Healthcare Market by Offering. (Hardware, Software, Services), Technology (Machine Learning, NLP, Context-aware Computing, Computer Vision), Application, End User and Geography—Global Forecast to 2027 (2022). https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-healthcare-market-54679303.html. Accessed 15 Jan 2022

  27. IP PRAGMATICS: Artificial Intelligence in the Life Sciences & Patent Analytics: Market Developments and Intellectual Property Landscape (2018)

    Google Scholar 

  28. Garbuio, M., Lin, N.: Artificial intelligence as a growth engine for health care startups: emerging business models. Calif. Manag. Rev. 61(2), 59–83 (2019). https://doi.org/10.1177/0008125618811931

    Article  Google Scholar 

  29. Roland Berger: Artificial Intelligence—A Strategy for European Startups (2018)

    Google Scholar 

  30. Slevitch, L.: Qualitative and quantitative methodologies compared: ontological and epistemological perspectives. J. Qual. Assur. Hosp. Tour. (2011). https://doi.org/10.1080/1528008X.2011.541810

    Article  Google Scholar 

  31. Ringle, C.M., et al.: SmartPLS 3 (2015). Available online at http://www.smartpls.com. Accessed 20 March 2022

  32. Durach, C.F., et al.: A new paradigm for systematic literature reviews in supply chain management. J. Supply Chain Manag. 53(4), 67–85 (2017). https://doi.org/10.1111/jscm.12145

    Article  Google Scholar 

  33. VHB e.V., VHB-JOURQUAL 3 (2019). https://vhbonline.org/vhb4you/vhb-jourqual/vhb-jourqual-3. Accessed 20 March 2022

  34. Computing Research & Education, CORE Rankings Portal (2016). https://www.core.edu.au/conference-portal. Accessed 20 March 2022

  35. VHB e.V., Über den Verband (2019). https://vhbonline.org/ueber-uns. Accessed 20 March 2022

  36. Sarstedt, M., et al.: Partial least squares structural equation modeling. In: Homburg, C. et al. (ed.) Handbook of Market Research. Springer, Cham (2017), pp 1–40. https://doi.org/10.1007/978-3-319-05542-8_15-1

  37. Chin, W.W.: How to write up and report PLS analyses. In: Esposito Vinzi, V. et al. (ed.) Handbook of Partial Least Squares. Springer, Berlin, Heidelberg (2010), pp. 655–690. https://doi.org/10.1007/978-3-540-32827-8_29

  38. Henseler, J., et al.: Partial least squares path modeling: updated guidelines. In Latan, H., Noonan, R. (eds.) Partial Least Squares Path Modeling, pp. 19–39. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64069-3_2

  39. Hoppe, N.: Benefits of artificial intelligence in healthcare—a systematic literature review (2021). http://www.kmu-aalen.de/kmu-aalen/forschung/publikationen/. Accessed 19 March 2022

  40. Krämer, J., et al.: Classification of hospital admissions into emergency and elective care: a machine learning approach. Health Care Manag. Sci. 22(1), 85–105 (2019). https://doi.org/10.1007/s10729-017-9423-5

    Article  Google Scholar 

  41. Turgeman, L., et al.: Insights from a machine learning model for predicting the hospital length of stay (LOS) at the time of admission. Expert Syst. Appl. 78, 376–385 (2017). https://doi.org/10.1016/j.eswa.2017.02.023

    Article  Google Scholar 

  42. Doraiswamy, P.M., et al.: Artificial intelligence and the future of psychiatry: Insights from a global physician survey. Artif. Intell. Med. 102, 101753 (2020). https://doi.org/10.1016/j.artmed.2019.101753

    Article  Google Scholar 

  43. Onukwugha, E., et al.: Cost prediction using a survival grouping algorithm: an application to incident prostate cancer cases. Pharmacoeconomics 34(2), 207–216 (2016). https://doi.org/10.1007/s40273-015-0368-6

    Article  Google Scholar 

  44. Thesmar, D., et al.: Combining the power of artificial intelligence with the richness of healthcare claims data: opportunities and challenges. Pharmacoeconomics 37, 745–752 (2019). https://doi.org/10.1007/s40273-019-00777-6

    Article  Google Scholar 

  45. Azzi, S., et al.: Healthcare applications of artificial intelligence and analytics: a review and proposed framework. Appl. Sci. 10(18), 6553 (2020). https://doi.org/10.3390/app10186553

    Article  MathSciNet  Google Scholar 

  46. Kaplan, A., Haenlein, M.: Rulers of the world, unite! The challenges and opportunities of artificial intelligence. Bus. Horiz. 63(1), 37–50 (2020). https://doi.org/10.1016/j.bushor.2019.09.003

    Article  Google Scholar 

  47. Bostrom, N.: Superintelligence. Oxford University Press, Oxford, England (2014)

    Google Scholar 

  48. Pee, L.G., et al.: Artificial intelligence in healthcare robots: a social informatics study of knowledge embodiment. JASIST 70(4), 351–369 (2019). https://doi.org/10.1002/asi.24145

    Article  Google Scholar 

  49. Fan, W., et al.: Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Ann. Oper. Res. 294, 567–592 (2020). https://doi.org/10.1007/s10479-018-2818-y

    Article  Google Scholar 

  50. Yuan, K.-C., et al.: The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit. Int. J. Med. Inform. 141, 104176 (2020). https://doi.org/10.1016/j.ijmedinf.2020.104176

    Article  Google Scholar 

  51. Shin, C., et al.: Autonomous tissue manipulation via surgical robot using learning based model predictive control. In: 2019 International Conference on Robotics, pp. 3875–3881 (2019). https://doi.org/10.1109/ICRA.2019.8794159

  52. Kalis, B., et al.: 10 promising AI applications in health care. Harv. Bus. Rev. REPRINT H04BM0, 1–5 (2018)

    Google Scholar 

  53. Schinkel, M., et al.: Clinical applications of artificial intelligence in sepsis: a narrative review. Comput. Biol. Med. 115, 103488 (2019). https://doi.org/10.1016/j.compbiomed.2019.103488

    Article  Google Scholar 

  54. Vemulapalli, V., et al.: Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data. Artif. Intell. Med. 74, 1–8 (2016). https://doi.org/10.1016/j.artmed.2016.11.001

    Article  Google Scholar 

  55. Chan, H.C.S.: Advancing drug discovery via artificial intelligence. Trends Pharmacol. Sci. 40(8), 592–604 (2019). https://doi.org/10.1016/j.tips.2019.06.004

    Article  Google Scholar 

  56. Davenport, T.H., Ronanki, R.: Artificial intelligence for the real world. Don’t start with moon shots. Harv. Bus. Rev. 1–10 (2018)

    Google Scholar 

  57. Dezső, Z., Ceccarelli, M.: Machine learning prediction of oncology drug targets based on protein and network properties. BMC Bioinform. 21, 104 (2020). https://doi.org/10.1186/s12859-020-3442-9

    Article  Google Scholar 

  58. LimeSurvey: Turn questions into answers. (2021). https://www.limesurvey.org/. Accessed 20 March 2022

  59. Wong, K.K.-K.: Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Market. Bull. (24, Technical Note 1), 1–32 (2013)

    Google Scholar 

  60. Kose, U., et al.: Deep Learning for Medical Decision Support Systems. Springer Singapore, Singapore (2021). https://doi.org/10.1007/978-981-15-6325-6

  61. Chowdhary, K.R. (ed.): Fundamentals of Artificial Intelligence. Springer India, New Delhi (2020). https://doi.org/10.1007/978-81-322-3972-7

  62. European Commission: Commission Recommendation of 6 May 2003 Concerning the Definition of Micro, Small and Medium-Sized Enterprises. L 124/36 (2003)

    Google Scholar 

  63. Chin, W.W.: The partial least squares approach for structural equation modeling. In: Marcoulides, G.A. (ed.) Modern Methods for Business Research (Quantitative Methodology Series), pp. 295–336. Psychology Press, New York, NY (1998)

    Google Scholar 

  64. Schuberth, F., Cantaluppi, G.: Ordinal consistent partial least squares. In: Latan, H., Noonan, R. (eds.) Partial Least Squares Path Modeling, pp. 109–150. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64069-3_6

  65. Chin, W.W.: Issues and opinion on structural equation modeling. MIS Q. vii–xvi (1998)

    Google Scholar 

  66. Center for Systems Science and Engineering (CSSE): Johns Hopkins University (JHU), COVID-19 Dashboard (2022). https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6. Accessed 19 March 2022

  67. Keding, C.: Understanding the interplay of artificial intelligence and strategic management: four decades of research in review. Manag. Rev. Q. 71, 91–134 (2020). https://doi.org/10.1007/s11301-020-00181-x

    Article  Google Scholar 

  68. Astromskė, K., et al.: Ethical and legal challenges of informed consent applying artificial intelligence in medical diagnostic consultations. AI Soc. 36, 509–520 (2021). https://doi.org/10.1007/s00146-020-01008-9

    Article  Google Scholar 

  69. Rai, A.: Explainable AI: from black box to glass box. J. Acad. Mark. Sci. 48(1), 137–141 (2020). https://doi.org/10.1007/s11747-019-00710-5

    Article  Google Scholar 

  70. Schiff, D., Borenstein, J.: How should clinicians communicate with patients about the roles of artificially intelligent team members? AMA J. Ethics 21(2), E138-145 (2019). https://doi.org/10.1001/amajethics.2019.138

    Article  Google Scholar 

  71. Kraus, S., et al.: Digital transformation in healthcare: analyzing the current state-of-research. J. Bus. Res. 123, 557–567 (2021). https://doi.org/10.1016/j.jbusres.2020.10.030

    Article  Google Scholar 

  72. Reim, W., et al.: Implementation of artificial intelligence (AI): a roadmap for business model innovation. AI 1(2), 180–191 (2020). https://doi.org/10.3390/ai1020011

  73. Lee, J., et al.: Emerging technology and business model innovation: the case of artificial intelligence. JOItmC 5(3), 44 (2019). https://doi.org/10.3390/joitmc5030044

    Article  Google Scholar 

  74. Gombolay, M., et al.: Robotic assistance in the coordination of patient care. Int. J. Robot. Res. 37(10), 1300–1316 (2018). https://doi.org/10.1177/0278364918778344

    Article  Google Scholar 

  75. Laï, M.-C., et al.: Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France. J. Transl. Med. 18(1), 14 (2020). https://doi.org/10.1186/s12967-019-02204-y

    Article  Google Scholar 

  76. Brox, J.: Brilliant: The Evolution of Artificial Light. Houghton Mifflin Harcourt, Boston (2010). ISBN: 978-0-547-48715-1

    Google Scholar 

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Authors and Affiliations

  1. Aalen University of Applied Sciences, Aalen, Germany

    Nathalie Hoppe, Ralf-Christian Härting & Anke Rahmel

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  1. Nathalie Hoppe
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  2. Ralf-Christian Härting
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  3. Anke Rahmel
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Correspondence to Ralf-Christian Härting .

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Editors and Affiliations

  1. Deakin University, Victoria, VIC, Australia

    Chee Peng Lim

  2. Royal Adelaide Hospital,, Adelaide, SA, Australia

    Ashlesha Vaidya

  3. College of Information Science and Eng, Ritsumeikan University, Kusuatsu, Shiga, Japan

    Yen-Wei Chen

  4. ASF Insignia, Kyndryl Solutions Pvt Ltd,, Gurugram, Haryana, India

    Vaishnavi Jain

  5. Liverpool Hope University, KES International United Kingdom, Shoreham-by-Sea, UK

    Lakhmi C. Jain

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Hoppe, N., Härting, RC., Rahmel, A. (2023). Potential Benefits of Artificial Intelligence in Healthcare. In: Lim, C.P., Vaidya, A., Chen, YW., Jain, V., Jain, L.C. (eds) Artificial Intelligence and Machine Learning for Healthcare. Intelligent Systems Reference Library, vol 229. Springer, Cham. https://doi.org/10.1007/978-3-031-11170-9_9

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