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Machine Learning in Healthcare: Current Trends and the Future

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International Conference on Artificial Intelligence for Smart Community

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 758))

Abstract

Today, an abundance of electronically stored medical image data and DL algorithms can be used to recognize and detect patterns and anomalies in this kind of dataset. Computers and algorithms can interpret the imaging data as a very qualified radiologist can see irregular skin, lesions, tumours and brain bleeds. Consequently, the use of AI/ML tools/platforms to help radiologists is poised to grow exponentially. This approach addresses a vital issue in the healthcare sector as well-trained radiologists are challenging to come by worldwide. These professional professionals are, in most cases, under tremendous pressure due to the influx of digital medical data. We analyze and address the current state of A.I. applications in healthcare. A.I. can be applied to various healthcare data forms (structured and unstructured). Popular A.I. techniques include machine learning for structured data such as classic support vectors and neural networks, modern in-depth learning unstructured data natural language processing.

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References

  1. Lipton RB, Scher AI, Steiner TJ, Bigal ME, Kolodner K, Liberman JN, Stewart WF (2003) Patterns of health care utilization for migraine in England and the United States. Neurology 60(3):441–448

    Article  Google Scholar 

  2. Islam MN, Inan TT, Rafi S, Akter SS, Sarker IH, Islam AKM (2020) A survey on the use of A.I. and ML for fighting the COVID-19 pandemic. arXiv preprint arXiv:2008.07449

  3. Lu C, Strout J, Gaudreau R, Wright B, Marcus FBDC, Buch V, Andriole K (2020) An overview and case study of the clinical A.I. model development life cycle for healthcare systems. arXiv preprint arXiv:2003.07678

  4. Kaur J, Mann KS (2017) AI-based healthcare platform for real-time, predictive and prescriptive analytics using reactive programming. J Phys: Conf Ser 933:012010

    Google Scholar 

  5. Tripathi G, Ahad MA, Paiva S (2020) S2HS-A blockchain-based approach for the smart healthcare system. Healthcare 8:100391

    Google Scholar 

  6. Yoon JE, Suh CJ (2019) Research trend analysis by using text-mining techniques on the convergence studies of A.I. and healthcare technologies. J Inf Technol Services 18(2):123–141

    Google Scholar 

  7. Gil-Lacruz M, Gracia-Pérez ML, Gil-Lacruz AI (2019) Learning by doing and training satisfaction: an evaluation by health care professionals. Int J Environ Res Public Health 16(8):1397

    Article  Google Scholar 

  8. Das S, Sanyal MK, Application of A.I. and soft computing in healthcare: a review and speculation 8:21

    Google Scholar 

  9. Clifford GD (2020) The future A.I. in healthcare: a tsunami of false alarms or a product of experts? arXiv preprint arXiv:2007.10502

  10. Troncoso EL (2020) The greatest challenge to using AI/ML for primary health care: mindset or datasets? Front Artif Intell 3:53

    Article  Google Scholar 

  11. Lysaght T, Lim HY, Xafis V, Ngiam KY (2019) AI-assisted decision-making in healthcare. Asian Bioethics Rev 11(3):299–314

    Article  Google Scholar 

  12. Drysdale E, Dolatabadi E, Chivers C, Liu V, Saria S, Sendak M, Wiens J, Brudno M, Hoyt A, Mazwi M (2019) Implementing A.I. in healthcare

    Google Scholar 

  13. Johnson SL (2019) A.I., machine learning, and ethics in health care. J Legal Med 39(4):427–441

    Article  Google Scholar 

  14. Stanfill MH, Marc DT (2019) Health information management: implications of artificial intelligence on healthcare data and information management. Yearb Med Inform 28(1):56

    Article  Google Scholar 

  15. Pawar U, O’Shea D, Rea S, O’Reilly R (2020) Explainable A.I. in healthcare. In: 2020 international conference on cyber situational awareness, data analytics and assessment (Cy- Bersa). pp 1–2

    Google Scholar 

  16. Panesar A (2019) Machine learning and A.I. for healthcare. Springer

    Google Scholar 

  17. Farroha J (2019) Security analysis and recommendations for A.I./ML-enabled automated cyber medical systems. Big Data: Learn Anal Appl 10989:109890

    Google Scholar 

  18. Adadi A, Berrada M (2020) Explainable A.I. for healthcare: from black box to interpretable models

    Google Scholar 

  19. Reddy S, Allan S, Coghlan S, Cooper P (2020) A governance model for the application of A.I. in health care. J Am Med Inf Assoc 27(3):491–497

    Google Scholar 

  20. Hernandez-Boussard T, Bozkurt S, Ioannidis J, Shah NH (2020) MINIMAL (MINimum Information for Medical A.I. Reporting): Developing reporting standards for artificial intelligence in health care. J Am Med Inf Assoc

    Google Scholar 

  21. Hunter P (2019) The advent of A.I. and deep learning in diagnostics and imaging: machine learning systems have the potential to improve diagnostics in healthcare and imaging systems in research. EMBO Rep 20(7):e48559

    Google Scholar 

  22. Greco L, Percannella G, Ritrovato P, Tortorella F, Vento M (2020) Trends in IoT based solutions for health care: moving A.I. to the Edge. Pattern Recognit Lett

    Google Scholar 

  23. Henriksen A, Bechmann A (2020) Building truths in A.I.: making predictive algorithms doable in healthcare. Inf Commun Soc 23(6):802–816

    Google Scholar 

  24. Halminen O, Tenhunen H, Heliste A, Seppälä T (2019) Factors affecting venture funding of healthcare A.I. companies. ICIMTH 268–271

    Google Scholar 

  25. Terry N (2019) Of regulating healthcare A.I. and robots. Available at SSRN 3321379

    Google Scholar 

  26. Srivastava SK, Singh SK, Suri JS (2020) State-of-the-art methods in healthcare text classification system: A.I. paradigm. Front Biosci (Landmark edition) 25:646–672

    Article  Google Scholar 

  27. Morley J, Machado C, Burr C, Cowls J, Taddeo M, Florida L (2019) The debate on the ethics of A.I. in health care: a reconstruction and critical review. Available at SSRN 3486518

    Google Scholar 

  28. Tan Y, Jin B, Yue X, Chen Y, Vincentelli AS (2020) exploiting uncertainties from ensemble learners to improve decision-making in healthcare A.I. arXiv preprint arXiv:2007.06063

  29. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vascular Neurology 2(4):230–243

    Article  Google Scholar 

  30. Ellahham S, Ellahham N, Simsekler MCE (2020) Application of artificial intelligence in the health care safety context: opportunities and challenges. Am J Med Qual 35(4):341–348

    Article  Google Scholar 

  31. Fritchman K, Saminathan K, Dowsley R, Hughes T, Cock MD, Nascimento A, Tere- Desai A (2018) Privacy-preserving scoring of tree ensembles: a novel framework for A.I. in health- care. In: 2018 IEEE international conference on big data (Big Data), pp 2413–2422

    Google Scholar 

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Author information

Authors and Affiliations

  1. Universiti Teknologi Petronas, Seri Iskandar, 32610, Perak, UTP, Malaysia

    Usman Ahmad Usmani & Jafreezal Jaafar

Authors
  1. Usman Ahmad Usmani
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  2. Jafreezal Jaafar
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Corresponding author

Correspondence to Usman Ahmad Usmani .

Editor information

Editors and Affiliations

  1. Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia

    Rosdiazli Ibrahim

  2. Department of Electrical and Electronics Engineering, Sri Sairam Engineering College, Chennai, India

    K. Porkumaran

  3. Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia

    Ramani Kannan

  4. Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia

    Nursyarizal Mohd Nor

  5. Department of Biomedical Engineering, Sona College of Technology, Salem, India

    S. Prabakar

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Usmani, U.A., Jaafar, J. (2022). Machine Learning in Healthcare: Current Trends and the Future. In: Ibrahim, R., K. Porkumaran, Kannan, R., Mohd Nor, N., S. Prabakar (eds) International Conference on Artificial Intelligence for Smart Community. Lecture Notes in Electrical Engineering, vol 758. Springer, Singapore. https://doi.org/10.1007/978-981-16-2183-3_64

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  • DOI: https://doi.org/10.1007/978-981-16-2183-3_64

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-2182-6

  • Online ISBN: 978-981-16-2183-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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