Abstract
Across the globe, the demand over a good quality healthcare is in the rise. Patients require rigorous treatments and thorough followups. Meanwhile, the advent of artificial intelligence has opened up various opportunities for healthcare providers to meet their patients’ demands. With the use of artificial intelligence, data can be harnessed to provide digital guidance, design care management programs, as well as predict the upcoming health crisis. While artificial intelligence for managing patients’ health and well-being may seem ready to be implemented, patients as well as health institutions still devote a preponderant importance to the clinician at the center of care. In this chapter, we explore the position of artificial intelligence in the management of health and well-being, where the human (patient) to human (clinician) interaction is key to its success. Yet, patients feel ready to get support from artificial intelligence. We first describe opportunities of how artificial intelligence is already used in the management of patients’ health. We then describe the hurdles impeding the Human-AI interaction between the artificial intelligent health management systems and the user.
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References
Adadi A, Berrada M (2018) Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6:52138–52160. https://doi.org/10.1109/access.2018.2870052
Alber M, Tepole AB, Cannon WR, De S, Dura-Bernal S, Garikipati K, Karniadakis G, Lytton WW, Perdikaris P, Petzold L, Kuhl E (2019) Integrating machine learning and multiscale modeling–perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. Nat Partner J Digit Med 2(1):1–11. https://doi.org/10.1038/s41746-019-0193-y
Amershi S, Inkpen K, Teevan J, Kikin-Gil R, Horvitz E, Weld D, Vorvoreanu M, Fourney A, Nushi B, Collisson P, Suh J, Iqbal S, Bennett PN (2019)Guidelines for human-AI interaction. In: Proceedings of the 2019 CHI conference on human factors in computing systems—CHI 19. ACM Press. [Online]. Available: https://doi.org/10.1145/3290605.3300233
Amira A, Agoulmine N, Bensaali F, Bermak A, Dimitrakopoulos G (2019) Special issue: empowering eHealth with smart internet of things (IoT) medical devices. J Sens Act Netw 8(2):33. https://doi.org/10.3390/jsan8020033
Chancellor S, Lin Z, Goodman EL, Zerwas S, Choudhury MD (2016) Quantifying and predicting mental illness severity in online pro-eating disorder communities. In: Proceedings of the 19th ACM conference on computer-supported cooperative work & social computing—CSCW 16. ACM Press. [Online]. Available: https://doi.org/10.1145/2818048.2819973
Dove G, Halskov K, Forlizzi J, Zimmerman J (2017) UX design innovation. In: Proceedings of the 2017 CHI conference on human factors in computing systems - CHI 17. ACM Press. [Online]. Available: https://doi.org/10.1145/3025453.3025739
Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference on—ITCS 12. ACM Press. [Online]. Available: https://doi.org/10.1145/2090236.2090255
Fernandez-Luque L, Aupetit M,  Palotti J, Singh M, Fadlelbari A, Baggag A, Khowaja K, Al-Thani D (2019) Health lifestyle data-driven applications using pervasive computing. In: Big data, big challenges: a healthcare perspective. Springer International Publishing, pp 115–126. [Online]. Available: https://doi.org/10.1007/978-3-030-06109-8_10
Fogg BJ (2002) Persuasive technology: using computers to change what we think and do. Ubiquity 2002:2. [Online]. Available: https://doi.org/10.1145/764008.763957
Gillies M, Lee B, dAlessandro N, Tilmanne J, Kulesza T, Caramiaux B, Fiebrink R, Tanaka A, Garcia J, Bevilacqua F, Heloir A, Nunnari F, Mackay W, Amershi S (2016) Human-centred machine learning. In: Proceedings of the 2016 CHI conference extended abstracts on human factors in computing systems - CHI EA 16. ACM Press. [Online]. Available: https://doi.org/10.1145/2851581.2856492
In: Proceedings of the conference on fairness, accountability, and transparency, FAT* 2019, Atlanta, GA, USA, 29–31 Jan 2019. ACM. [Online]. Available: https://dl.acm.org/citation.cfm?id=3287588
Inkpen K, Chancellor S, Choudhury MD, Veale M, Baumer EPS (2019) Where is the human? In: Extended abstracts of the 2019 conference on human factors in computing systems CHI—EA 19. ACM Press. [Online]. Available: https://doi.org/10.1145/3290607.3299002
Khowaja K, Banire B, Al-Thani D, Sqalli MT, Aqle A, Shah A, Salim SS (2020) Augmented reality for learning of children and adolescents with autism spectrum disorder (asd): A systematic review. IEEE Access 8:78779–78807
Kirsch A (2017) Explain to whom? Putting the user in the center of explainable AI. In: Proceedings of the first international workshop on comprehensibility and explanation in AI and ML 2017 co-located with 16th international conference of the italian association for artificial intelligence (AI*IA 2017), Bari, Italy. [Online]. Available: https://hal.archives-ouvertes.fr/hal-01845135
Koh H, Tan G (2005) Data mining applications in healthcare. J Healthc Inf Manage JHIM 19:64–72
Kurabe K, Kato Y, Koike M, Jinno K, Yamashita K, Kito K, Sqalli MT, Tatsuno K (2016) A robot controller for power distribution line maintenance robot working by task-level command,, In: 2016 IEEE/SICE international symposium on system integration (SII). IEEE. [Online]. Available: https://doi.org/10.1109/sii.2016.7844038
Lazer D, Kennedy R, King G, Vespignani A (2014) The parable of google flu: traps in big data analysis. Science 343(6176):1203–1205. https://doi.org/10.1126/science.1248506
Marvel FA, Wang J, Martin SS (2018) Digital health innovation: a toolkit to navigate from concept to clinical testing. JMIR Cardio 2(1):e2. [Online]. Available: https://doi.org/10.2196/cardio.7586
Norman DA (2002) The design of everyday things. Basic Books Inc, New York, NY, USA
Olteanu A, Castillo C, Diaz F, Kıcıman E (2019) Social data: biases, methodological pitfalls, and ethical boundaries. Front Big Data 2. [Online]. Available: https://doi.org/10.3389/fdata.2019.00013
Pedreschi D, Giannotti F, Guidotti R, Monreale A, Ruggieri S, Turini F (2019) eaningful explanations of black box AI decision systems. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 9780–9784. [Online]. Available: https://doi.org/10.1609/aaai.v33i01.33019780
Piwek L, Ellis DA, Andrews S, Joinson A (2016) The rise of consumer health wearables: promises and barriers. PLOS Med 13(2):e1001953. https://doi.org/10.1371/journal.pmed.1001953
Shah P, Kendall F, Khozin S, Goosen R, Hu J, Laramie J, Ringel M, Schork N (2019) Artificial intelligence and machine learning in clinical development: a translational perspective. Nat Partner J Digit Med 2(1). https://doi.org/10.1038/s41746-019-0148-3
Sqalli MT, Al-Thani D (2019) AI-supported health coaching model for patients with chronic diseases. In: 2019 16th International symposium on wireless communication systems (ISWCS). IEEE [Online]. Available: https://doi.org/10.1109/ISWCS.2019.8877113
Sqalli MT, Tatsuno K, Kurabe K, Ando H, Obitsu H, Itakura R, Aoto T, Yoshino K (2016) Improvement of a tele-presence robot autonomous navigation using SLAM algorithm. In: 2016 International symposium on micro-nanomechatronics and human science (MHS). IEEE. [Online]. Available: https://doi.org/10.1109/mhs.2016.7824221
Turakhia MP, Desai M, Hedlin H, Rajmane A, Talati N, Ferris T, Desai S, Nag D, Patel M, Kowey P, Rumsfeld JS, Russo AM, Hills MT, Granger CB, Mahaffey KW, Perez MV (2019) Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: the apple heart study. Am Heart J 207:66–75. https://doi.org/10.1016/j.ahj.2018.09.002
Wiegand T, Krishnamurthy R, Kuglitsch M, Lee N, Pujari S, Salathé M, Wenzel M, Xu S (2019) WHO and ITU establish benchmarking process for artificial intelligence in health. The Lancet 394(10192):9–11. https://doi.org/10.1016/s0140-6736(19)30762-7
Wilbanks JT, Topol EJ (2016) Stop the privatization of health data. Nat Int Weekly J Sci 535
Yamashita K, Kato Y, Kurabe K, Koike M, Jinno K, Kito K, Tatsuno K, Sqalli MT (2016) Remote operation of a robot for maintaining electric power distribution system using a joystick and a master arm as a human robot interface medium. In: 2016 International symposium on micro-nanomechatronics and human science (MHS). IEEE. [Online]. Available: https://doi.org/10.1109/mhs.2016.7824229
Yang Q, Scuito A, Zimmerman J, Forlizzi J, Steinfeld A (2018) Investigating how experienced UX designers effectively work with machine learning. In: Proceedings of the 2018 on designing interactive systems conference 2018 - DIS 18. ACM Press. [Online]. Available: https://doi.org/10.1145/3196709.3196730
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Sqalli, M.T., Al-Thani, D., Qaraqe, M., Fernandez-Luque, L. (2021). Perspectives on Human-AI Interaction Applied to Health and Wellness Management: Between Milestones and Hurdles. In: Househ, M., Borycki, E., Kushniruk, A. (eds) Multiple Perspectives on Artificial Intelligence in Healthcare. Lecture Notes in Bioengineering. Springer, Cham. https://doi.org/10.1007/978-3-030-67303-1_4
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