Artificial Intelligence for Healthcare and Social Services: Optimizing Resources and Promoting Sustainability
Abstract
:1. Introduction
2. Materials and Methods
3. Results: Types of A.I.
- Logical-mathematical intelligence: the ability to solve complex problems through logical thinking [30];
- Social intelligence: the ability to understand emotions and interact with human beings;
- Visual–spatial intelligence: the ability to perceive space and understand it [31];
- Linguistic–verbal intelligence: the ability to understand human language and simulate it to communicate with humans;
- Assisted intelligence is based on performing simple tasks with maximum efficiency, such as classifying diagnostic images;
- Augmented Intelligence goes beyond task repetition and modifies the very essence of a specific task to improve it and provide new skills;
- Autonomous Intelligence is the most advanced stage of A.I.; it will enable machines to make choices on their own. Substantial ethical challenges must be addressed before the actual development of this type of intelligence [34].
4. Discussion: Uses in Healthcare
- Prevention
- Diagnosis
- Treatment
- Monitoring/Rehabilitation
4.1. Prevention
4.2. Diagnosis
4.3. Treatment
4.4. Monitoring
4.5. Management
5. Issues
5.1. Technical Issues
5.2. Ethical Issues
5.3. Social Issues
6. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Panetta, K. 5 Trends Appear on the Gartner Hype Cycle for Emerging Technologies; Gartner, Inc.: Stamford, CT, USA, 2019; Available online: https://www.gartner.com/smarterwithgartner/5-trends-appear-on-the-gartner-hype-cycle-for-emerging-technologies-2019/ (accessed on 8 July 2022).
- Choudhury, A.; Asan, O. Human factors: Bridging artificial intelligence and patient safety. In Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care, Toronto, ON, Canada, 8–11 March 2020; SAGE Publications: Los Angeles, CA, USA, 2020; Volume 9, pp. 211–215. [Google Scholar]
- Saridis, G.N.; Valavanis, K.P. Analytical design of intelligent machines. Automatica 1988, 24, 123–133. [Google Scholar] [CrossRef]
- McCarthy, J. What Is Artificial Intelligence? 2007. Available online: http://www-formal.stanford.edu/jmc/whatisai/ (accessed on 8 July 2022).
- McCulloch, W.S.; Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 1943, 5, 115–133. [Google Scholar] [CrossRef]
- Turing, A.M. Intelligent machinery, a heretical theory. Turing Test Verbal Behav. Hallmark Intell. 1948, 105–110. [Google Scholar] [CrossRef] [Green Version]
- Rosenblatt, F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychol. Rev. 1958, 65, 386–408. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- London, A.J. Groundhog day for medical artificial intelligence. Hastings Cent. Rep. 2018, 48. inside-back. [Google Scholar] [CrossRef] [Green Version]
- Hendler, J. Avoiding another AI winter. IEEE Intell. Syst. 2008, 23, 2–4. [Google Scholar] [CrossRef]
- Poole, D.L.; Mackworth, A.K. Artificial Intelligence: Foundations of Computational Agents; Cambridge University Press: Cambridge, UK, 2010. [Google Scholar]
- Joyce, K.; Smith-Doerr, L.; Alegria, S.; Bell, S.; Cruz, T.; Hoffman, S.G.; Noble, S.U.; Shestakofsky, B. Toward a sociology of artificial intelligence: A call for research on inequalities and structural change. Socius 2021, 7, 2378023121999581. [Google Scholar] [CrossRef]
- Liu, Z. Sociological perspectives on artificial intelligence: A typological reading. Sociol. Compass 2021, 15, e12851. [Google Scholar] [CrossRef]
- Sharma, M.; Savage, C.; Nair, M.; Larsson, I.; Svedberg, P.; Nygren, J.M. Artificial Intelligence Applications in Health Care Practice: Scoping Review. J. Med. Internet Res. 2022, 24, e40238. [Google Scholar] [CrossRef]
- Siala, H.; Wang, Y. SHIFTing artificial intelligence to be responsible in healthcare: A systematic review. Soc. Sci. Med. 2022, 296, 114782. [Google Scholar] [CrossRef]
- Research and Markets. Artificial Intelligence in Healthcare Market by Product (Hardware, Software, Services), Technology (Machine Learning, Context-Aware Computing, NLP), Application (Drug Discovery, Precision Medicine), End User, and Geography—Global Forecast to 2025; Research and Markets: Dublin, Ireland, 2019; pp. 1–178. [Google Scholar]
- Garbuio, M.; Lin, N. Artificial intelligence as a growth engine for health care startups: Emerging business models. Calif. Manag. Rev. 2019, 61, 59–83. [Google Scholar] [CrossRef] [Green Version]
- Hintze, A. Understanding the four types of AI, from Reactive Robots to Self-Aware Beings. Conversation. 2016. Available online: https://theconversation.com/understanding-the-four-types-of-ai-from-reactive-robots-to-self-aware-beings-67616 (accessed on 20 June 2022).
- Topol, E. The Patient Will See You Now: The Future of Medicine Is in Your Hands; Basic Books: New York City, NY, USA, 2015. [Google Scholar]
- Diprose, W.; Buist, N. Artificial intelligence in medicine: Humans need not apply? N. Zeal. Med. J. (Online) 2016, 129, 73. [Google Scholar]
- Gado, S.; Kempen, R.; Lingelbach, K.; Bipp, T. Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence? Psychol. Learn. Teach. 2022, 21, 37–56. [Google Scholar] [CrossRef]
- Chennu, S. DeepMind: Can We Ever Trust a Machine to Diagnose Cancer. Conversation. 2017. Available online: https://theconversation.com/deepmind-can-we-ever-trust-a-machine-to-diagnose-cancer-88707 (accessed on 15 June 2022).
- Chen, M.; Decary, M. Artificial intelligence in healthcare: An essential guide for health leaders. In Healthcare Management Forum; SAGE Publications: Los Angeles, CA, USA, 2020; Volume 33, pp. 10–18. [Google Scholar]
- Miller, D.D.; Brown, E.W. Artificial intelligence in medical practice: The question to the answer? Am. J. Med. 2018, 131, 129–133. [Google Scholar] [CrossRef]
- Dimitrov, D.V. Medical internet of things and big data in healthcare. Healthc. Inform. Res. 2016, 22, 156–163. [Google Scholar] [CrossRef]
- Sciarretta, E.; Alimenti, L. Smart Speakers for Inclusion: How Can Intelligent Virtual Assistants Really Assist Everybody? In Proceedings of the International Conference on Human-Computer Interaction, Washington, DC, USA, 24–9 July 2021; Springer: Cham, Switzerland, 2021; pp. 77–93. [Google Scholar]
- Clemente, C.; Greco, E.; Sciarretta, E.; Altieri, L. Alexa, how do I feel today? Smart speakers for healthcare and wellbeing: An analysis about uses and challenges. Sociol. Soc. Work. Rev. 2022, 6, 6–24. [Google Scholar]
- Bickmore, T.; Trinh, H.; Asadi, R.; Olafsson, S. Safety first: Conversational agents for health care. In Studies in Conversational UX Design; Springer: Cham, Switzerland, 2018; pp. 33–57. [Google Scholar]
- Cichocki, A.; Kuleshov, A.P. Future trends for human-ai collaboration: A comprehensive taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles. Comput. Intell. Neurosci. 2021, 2021, 8893795. [Google Scholar] [CrossRef]
- Pantano, E.; Scarpi, D. I, Robot, You, Consumer: Measuring Artificial Intelligence Types and their Effect on Consumers Emotions in Service. J. Serv. Res. 2022, 25, 10946705221103538. [Google Scholar] [CrossRef]
- Huang, M.H.; Rust, R.T. Artificial intelligence in service. J. Serv. Res. 2018, 21, 155–172. [Google Scholar] [CrossRef]
- Čaić, M.; Odekerken-Schröder, G.; Mahr, D. Service robots: Value co-creation and co-destruction in elderly care networks. J. Serv. Manag. 2018, 29, 178–205. [Google Scholar] [CrossRef] [Green Version]
- Grewal, D.; Kroschke, M.; Mende, M.; Roggeveen, A.L.; Scott, M.L. Frontline cyborgs at your service: How human enhancement technologies affect customer experiences in retail, sales, and service settings. J. Interact. Mark. 2020, 51, 9–25. [Google Scholar] [CrossRef]
- Dong, Y.; Hou, J.; Zhang, N.; Zhang, M. Research on how human intelligence, consciousness, and cognitive computing affect the development of artificial intelligence. Complexity 2020, 2020, 1680845. [Google Scholar] [CrossRef]
- Rao, A. A strategist’s guide to artificial intelligence. Strategy+ Bus. 2017, 87, 46–50. [Google Scholar]
- Kassam, A.; Kassam, N. Artificial intelligence in healthcare: A Canadian context. In Healthcare Management Forum; SAGE Publications: Los Angeles, CA, USA, 2020; Volume 33, pp. 5–9. [Google Scholar]
- Kalis, B.; Collier, M.; Fu, R. 10 Promising AI Applications in Health Care. Harv. Bus. Rev. 2018. Available online: https://hbr.org/2018/05/10-promising-ai-applications-in-health-care (accessed on 26 June 2022).
- Wertz, J. How Startup Investors Can Utilize AI to Make Smarter Investments. 2019. Available online: https://www.forbes.com/sites/jiawertz/2019/01/18/startup-investors-utilize-ai-smarter-investments/ (accessed on 22 June 2022).
- Klumpp, M.; Hintze, M.; Immonen, M.; Ródenas-Rigla, F.; Pilati, F.; Aparicio-Martínez, F.; Çelebi, D.; Liebig, T.; Jirstrand, M.; Urbann, O.; et al. Artificial intelligence for hospital health care: Application cases and answers to challenges in European hospitals. In Healthcare; MDPI: Basel, Switzerland, 2021; Volume 9, p. 961. [Google Scholar]
- Reddy, S.; Fox, J.; Purohit, M.P. Artificial intelligence-enabled healthcare delivery. J. R. Soc. Med. 2019, 112, 22–28. [Google Scholar] [CrossRef] [PubMed]
- Li, L.; Qin, L.; Xu, Z.; Yin, Y.; Wang, X.; Kong, B.; Bai, J.; Lu, Y.; Fang, Z.; Song, Q.; et al. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology. 2020. [Google Scholar] [CrossRef]
- Fonseka, T.M.; Bhat, V.; Kennedy, S.H. The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors. Aust. New Zealand J. Psychiatry 2019, 53, 954–964. [Google Scholar] [CrossRef] [Green Version]
- Racine, E.; Boehlen, W.; Sample, M. Healthcare uses of artificial intelligence: Challenges and opportunities for growth. In Healthcare Management Forum; SAGE Publications: Los Angeles, CA, USA, 2019; Volume 32, pp. 272–275. [Google Scholar]
- Saria, S. A $3 trillion challenge to computational scientists: Transforming healthcare delivery. IEEE Intell. Syst. 2014, 29, 82–87. [Google Scholar] [CrossRef]
- Senthilraja, M. Application of artificial intelligence to address issues related to the COVID-19 Virus. Slas Technol. Transl. Life Sci. Innov. 2021, 26, 123–126. [Google Scholar] [CrossRef]
- Morrow, D.G.; Lane, H.C.; Rogers, W.A. A framework for design of conversational agents to support health self-care for older adults. Hum. Factors 2021, 63, 369–378. [Google Scholar] [CrossRef]
- Derrington, D. Artificial Intelligence for Health and Health Care; The MITRE Corporation: McLean, VA, USA, 2017. [Google Scholar]
- Jiang, L.; Wu, Z.; Xu, X.; Zhan, Y.; Jin, X.; Wang, L.; Qiu, Y. Opportunities and challenges of artificial intelligence in the medical field: Current application, emerging problems, and problem-solving strategies. J. Int. Med. Res. 2021, 49, 03000605211000157. [Google Scholar] [CrossRef]
- Taddy, M. The technological elements of artificial intelligence. In The Economics of Artificial Intelligence: An Agenda; University of Chicago Press: Chicago, IL, USA, 2018; pp. 61–87. [Google Scholar]
- Challen, R.; Denny, J.; Pitt, M.; Gompels, L.; Edwards, T.; Tsaneva-Atanasova, K. Artificial intelligence, bias and clinical safety. BMJ Qual. Saf. 2019, 28, 231–237. [Google Scholar] [CrossRef]
- Parikh, R.B.; Obermeyer, Z.; Navathe, A.S. Regulation of predictive analytics in medicine. Science 2019, 363, 810–812. [Google Scholar] [CrossRef]
- Kingston, J. Artificial intelligence and legal liability. arXiv 2018, arXiv:1802.07782. [Google Scholar]
- Geis, J.R.; Brady, A.P.; Wu, C.C.; Spencer, J.; Ranschaert, E.; Jaremko, J.L.; Langer, S.G.; Borondy Kitts, A.; Birch, J.; Shields, W.F.; et al. Ethics of artificial intelligence in radiology: Summary of the joint European and North American multisociety statement. Can. Assoc. Radiol. J. 2019, 70, 329–334. [Google Scholar] [CrossRef]
- Caliskan, A.; Bryson, J.J.; Narayanan, A. Semantics derived automatically from language corpora contain human-like biases. Science 2017, 356, 183–186. [Google Scholar] [CrossRef] [Green Version]
- King, T.C.; Aggarwal, N.; Taddeo, M.; Floridi, L. Artificial intelligence crime: An interdisciplinary analysis of foreseeable threats and solutions. Sci. Eng. Ethics 2020, 26, 89–120. [Google Scholar] [CrossRef] [Green Version]
- Lewis, P.R.; Marsh, S. What is it like to trust a rock? A functionalist perspective on trust and trustworthiness in artificial intelligence. Cogn. Syst. Res. 2022, 72, 33–49. [Google Scholar] [CrossRef]
- Gelhaus, P. Robot decisions: On the importance of virtuous judgment in clinical decision making. J. Eval. Clin. Pract. 2011, 17, 883–887. [Google Scholar] [CrossRef]
- Basu, T.; Engel-Wolf, S.; Menzer, O. The ethics of machine learning in medical sciences: Where do we stand today? Indian J. Dermatol. 2020, 65, 358. [Google Scholar] [CrossRef]
- Amato, F.; López, A.; Peña-Méndez, E.M.; Vaňhara, P.; Hampl, A.; Havel, J. Artificial neural networks in medical diagnosis. J. Appl. Biomed. 2013, 11, 47–58. [Google Scholar] [CrossRef] [Green Version]
- Nallam, P.; Bhandari, S.; Sanders, J.; Martin-Hammond, A. A question of access: Exploring the perceived benefits and barriers of intelligent voice assistants for improving access to consumer health resources among low-income older adults. Gerontol. Geriatr. Med. 2020, 6, 2333721420985975. [Google Scholar] [CrossRef] [PubMed]
- Rajkomar, A.; Dean, J.; Kohane, I. Machine learning in medicine. N. Engl. J. Med. 2019, 380, 1347–1358. [Google Scholar] [CrossRef] [PubMed]
- Kunneman, M.; Montori, V.M.; Castaneda-Guarderas, A.; Hess, E.P. What is shared decision making? (and what it is not). Acad. Emerg. Med. 2016, 23, 1320–1324. [Google Scholar] [CrossRef] [PubMed]
- Barbour, A.B.; Frush, J.M.; Gatta, L.A.; McManigle, W.C.; Keah, N.M.; Bejarano-Pineda, L.; Guerrero, E.M. Artificial intelligence in health care: Insights from an educational forum. J. Med. Educ. Curric. Dev. 2019, 6, 2382120519889348. [Google Scholar] [CrossRef]
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Sciarretta, E.; Mancini, R.; Greco, E. Artificial Intelligence for Healthcare and Social Services: Optimizing Resources and Promoting Sustainability. Sustainability 2022, 14, 16464. https://doi.org/10.3390/su142416464
Sciarretta E, Mancini R, Greco E. Artificial Intelligence for Healthcare and Social Services: Optimizing Resources and Promoting Sustainability. Sustainability. 2022; 14(24):16464. https://doi.org/10.3390/su142416464
Chicago/Turabian StyleSciarretta, Eliseo, Riccardo Mancini, and Emilio Greco. 2022. "Artificial Intelligence for Healthcare and Social Services: Optimizing Resources and Promoting Sustainability" Sustainability 14, no. 24: 16464. https://doi.org/10.3390/su142416464