Skip to main content
SpringerLink
Log in

Artificial Intelligence Applications in Healthcare

  • Conference paper
  • First Online:
Proceedings of Eighth International Congress on Information and Communication Technology (ICICT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 694))

Included in the following conference series:

Abstract

The demand of healthcare services is rising, for e.g., Europe and the US are experiencing shortage of healthcare professionals. Artificial intelligence (AI) holds a promise to assist healthcare professionals in wide range of tasks. There is already a large amount of clinical and non-clinical evidence that AI algorithms can analyze both structured and unstructured clinical data (including images) data from electronic medical records (EMRs) with the characterization and prognosis of the disease. However, there is lack of study that provides an overview on what are the clinical applications that currently exist. This study provides an overview on the most powerful AI applications in healthcare, including those that are directly related to healthcare as well as those that are part of the healthcare value chain, such as drug development and ambient assisted living. Moreover, this article also provides an overview on the ethical concerns that may arise with the use of AI in healthcare domain.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. van de Sande D, Van Genderen ME, Smit JM, Huiskens J et al (2022) Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter. BMJ Health Care Inform 29(1)

    Google Scholar 

  2. Happonen A, Tikka M, Usmani U (2021) A systematic review for organizing hackathons and code camps in Covid-19 like times: literature in demand to understand online hackathons and event result continuation. In: 2021 International conference on data and software engineering (ICoDSE), pp 7–12. IEEE. https://doi.org/10.1109/ICoDSE53690.2021.9648459

  3. Bazoukis G, Hall J, Loscalzo J, Antman EM, Fuster V, Armoundas AA (2022) The inclusion of augmented intelligence in medicine: a framework for successful implementation. Cell Rep Med 3(1):100485

    Article  Google Scholar 

  4. Holzinger A, Langs G, Denk H, Zatloukal K, Müller H (2019) Causability and explainability of artificial intelligence in medicine. Wiley Interdisc Rev: Data Min Knowl Discov 9(4):e1312

    Google Scholar 

  5. Kaul V, Enslin S, Gross SA (2020) History of artificial intelligence in medicine. Gastrointest Endosc 92(4):807–812

    Article  Google Scholar 

  6. Kulkarni S, Seneviratne N, Baig MS, Khan AHA (2020) Artificial intelligence in medicine: where are we now? Acad Radiol 27(1):62–70

    Article  Google Scholar 

  7. Becker A (2019) Artificial intelligence in medicine: what is it doing for us today? Health Policy Technol 8(2):198–205

    Article  Google Scholar 

  8. Jotterand F, Bosco C (2022) Artificial intelligence in medicine: a sword of Damocles? J Med Syst 46(1):1–5

    Article  Google Scholar 

  9. Alrefaei AF, Hawsawi YM, Almaleki D, Alafif T, Alzahrani FA, Bakhrebah MA (2022) Genetic data sharing and artificial intelligence in the era of personalized medicine based on a cross-sectional analysis of the Saudi human genome program. Sci Rep 12(1):1–10

    Article  Google Scholar 

  10. Usmani UA, Usmani MU (2014) Future market trends and opportunities for wearable sensor technology. Int J Eng Technol 6(4):326

    Article  Google Scholar 

  11. Lopez-Jimenez F, Attia Z, Arruda-Olson AM, Carter R, Chareonthaitawee P et al (2020) Artificial intelligence in cardiology: present and future. In: Mayo clinic proceedings, vol 95, no 5, pp 1015–1039. Elsevier

    Google Scholar 

  12. Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R et al (2018) Artificial intelligence in cardiology. J Am Coll Cardiol 71(23):2668–2679

    Article  Google Scholar 

  13. de Marvao A, Dawes TJ, Howard JP, O’Regan DP (2020) Artificial intelligence and the cardiologist: what you need to know for 2020. Heart 106(5):399–400

    Article  Google Scholar 

  14. Seetharam K, Shresthra S, Mills JD, Sengupta PP (2019) Artificial intelligence in nuclear cardiology: adding value to prognostication. Curr Cardiovasc Imaging Rep 12(5):1–6

    Article  Google Scholar 

  15. Siegersma KR, Leiner T, Chew DP, Appelman Y, Hofstra L, Verjans JW (2019) Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist. Neth Hear J 27(9):403–413

    Article  Google Scholar 

  16. Usmani UA, Happonen A, Watada J (2022) A review of unsupervised machine learning frameworks for anomaly detection in industrial applications. LNNS, vol 507, Chapter: 11, pp 158–189. https://doi.org/10.1007/978-3-031-10464-0_11

  17. Usmani UA, Happonen A, Watada J (2023) Enhancing artificial intelligence control mechanisms: current practices. In: Real life applications and future views, lecture notes in networks and systems, vol 559, pp 287–306. https://doi.org/10.1007/978-3-031-18461-1_19

  18. Hogarty DT, Su JC, Phan K, Attia M, Hossny M et al (2020) Artificial intelligence in dermatology-where we are and the way to the future: a review. Am J Clin Dermatol 21(1):41–47

    Article  Google Scholar 

  19. Young AT, Xiong M, Pfau J, Keiser MJ, Wei ML (2020) Artificial intelligence in dermatology: a primer. J Investig Dermatol 140(8):1504–1512

    Article  Google Scholar 

  20. Li CX, Shen CB, Xue K, Shen X, Jing Y, Wang ZY et al (2019) Artificial intelligence in dermatology: past, present, and future. CMJ 132(17):2017–2020

    Google Scholar 

  21. Cirillo D, Núñez-Carpintero I, Valencia A (2021) Artificial intelligence in cancer research: learning at different levels of data granularity. Mol Oncol 15(4):817–829

    Article  Google Scholar 

  22. Usmani UA, Happonen A, Watada J (2022) Enhanced deep learning framework for fine-grained segmentation of fashion and apparel. In: Intelligent computing, SAI 2022, Lecture Notes in Networks and Systems, vol 507, Chapter: 3, pp 29–44. https://doi.org/10.1007/978-3-031-10464-0_3

  23. Aractingi S, Pellacani G (2019) Computational neural network in melanocytic lesions diagnosis: artificial intelligence to improve diagnosis in dermatology? Eur J Dermatol 29(1):4–7

    Google Scholar 

  24. Patel S, Wang JV, Motaparthi K, Lee JB (2021) Artificial intelligence in dermatology for the clinician. Clin Dermatol 39(4):667–672

    Article  Google Scholar 

  25. Usmani UA, Haron NS, Jaafar J (2021) A natural language processing approach to mine online reviews using topic modelling. In: International conference on computing science, communication and security, pp 82–98. Springer, Cham

    Google Scholar 

  26. Stiff KM, Franklin MJ, Zhou Y, Madabhushi A, Knackstedt TJ (2022) Artificial intelligence and melanoma: a comprehensive review of clinical, dermoscopic, and histologic applications. Pigm Cell Melanoma Res

    Google Scholar 

  27. Usmani UA, Watada J, Jaafar J, Aziz IA, Roy A (2021) A reinforcement learning algorithm for automated detection of skin lesions. Appl Sci 11(20):9367

    Article  Google Scholar 

  28. Nicholson C, Davies JM, George R, Smith B, Pace V et al (2018) What are the main palliative care symptoms and concerns of older people with multimorbidity? A comparative cross-sectional study using routinely collected phase of illness, Australia-modified Karnofsky performance status and integrated Palliative care outcome scale data. Ann Palliat Med 7(Suppl 3):S164–S175

    Article  Google Scholar 

  29. Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S et al (2021) Artificial intelligence in gastroenterology and hepatology: status and challenges. World J Gastroenterol 27(16):1664

    Article  Google Scholar 

  30. Kröner PT, Engels MM, Glicksberg BS, Johnson KW, Mzaik O et al (2021) Artificial intelligence in gastroenterology: a state-of-the-art review. World J Gastroenterol 27(40):6794

    Article  Google Scholar 

  31. Mungo B, Molena D, Stem M, Yang SC, Battafarano RJ et al (2015) Does neoadjuvant therapy for esophageal cancer increase postoperative morbidity or mortality? Dis Esophagus 28(7):644–651

    Article  Google Scholar 

  32. Greenhill AT, Edmunds BR (2020) A primer of artificial intelligence in medicine. Tech Innov Gastrointest Endosc 22(2):85–89

    Article  Google Scholar 

  33. Yang YJ, Bang CS (2019) Application of artificial intelligence in gastroenterology. World J Gastroenterol 25(14):1666

    Article  Google Scholar 

  34. Zhu J, Wang L, Chu Y, Hou X, Xing L et al (2015) A new descriptor for computer-aided diagnosis of EUS imaging to distinguish autoimmune pancreatitis from chronic pancreatitis. Gastrointest Endosc 82(5):831–836

    Article  Google Scholar 

  35. Qiu W, Duan N, Chen X, Ren S, Zhang Y et al (2019) Pancreatic ductal adenocarcinoma: machine learning-based quantitative computed tomography texture analysis for prediction of histopathological grade. Cancer Manage Res 11:9253

    Article  Google Scholar 

  36. Usmani UA, Jaafar J (2022) Machine learning in healthcare: current trends and the future. In: International conference on artificial intelligence for smart community: AISC 2020, 17–18 Dec, Universiti Teknologi Petronas, Malaysia, pp 659–675. Springer Nature Singapore, Singapore

    Google Scholar 

  37. Agrebi S, Larbi A (2020) Use of artificial intelligence in infectious diseases. In: Artificial intelligence in precision health, pp 415–438. Academic Press

    Google Scholar 

  38. Sharma A, Rani S, Gupta D (2020) Artificial intelligence-based classification of chest X-ray images into COVID-19 and other infectious diseases. Int J Biomed Imaging

    Google Scholar 

  39. Usmani UA, Watada J, Jaafar J, Aziz IA, Roy A (2021) A reinforced active learning algorithm for semantic segmentation in complex imaging. IEEE Access 9:168415–168432

    Article  Google Scholar 

  40. Usmani UA, Watada J, Jaafar J, Aziz IA (2022) A systematic review of privacy-preserving blockchain in e-medicine. In: Biomedical and other applications of soft computing, pp 25–40

    Google Scholar 

  41. Shimizu H, Nakayama KI (2020) Artificial intelligence in oncology. Cancer Sci 111(5):1452–1460

    Article  Google Scholar 

  42. Chua IS, Gaziel-Yablowitz M, Korach ZT, Kehl KL, Levitan NA et al (2021) Artificial intelligence in oncology: path to implementation. Cancer Med 10(12):4138–4149

    Article  Google Scholar 

  43. Osamor VC, Okezie AF (2021) Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis. Sci Rep 11(1):1–11

    Article  Google Scholar 

  44. Rudie JD, Rauschecker AM, Bryan RN, Davatzikos C, Mohan S (2019) Emerging applications of artificial intelligence in neuro-oncology. Radiology 290(3):607–618

    Article  Google Scholar 

  45. Scheetz J, Rothschild P, McGuinness M, Hadoux X, Soyer HP et al (2021) A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Sci Rep 11(1):1–10

    Article  Google Scholar 

  46. Forghani R, Savadjiev P, Chatterjee A, Muthukrishnan N, Reinhold C, Forghani B (2019) Radiomics and artificial intelligence for biomarker and prediction model development in oncology. Comput Struct Biotechnol J 17:995

    Article  Google Scholar 

  47. Stenzinger A, Alber M, Allgäuer M, Jurmeister P, Bockmayr M et al (2021) Artificial intelligence and pathology: from principles to practice and future applications in histomorphology and molecular profiling. In: Seminars in cancer biology. Academic Press

    Google Scholar 

  48. Sun TQ, Medaglia R (2019) Mapping the challenges of artificial intelligence in the public sector: evidence from public healthcare. Gov Inf Q 36(2):368–383

    Article  Google Scholar 

  49. Lin E, Lin CH, Lane HY (2020) Precision psychiatry applications with pharmacogenomics: artificial intelligence and machine learning approaches. Int J Mol Sci 21(3):969

    Article  Google Scholar 

  50. Doraiswamy PM, Blease C, Bodner K (2020) Artificial intelligence and the future of psychiatry: insights from a global physician survey. Artif Intell Med 102:101753

    Article  Google Scholar 

  51. Ray A, Bhardwaj A, Malik YK, Singh S, Gupta R (2022) Artificial intelligence and psychiatry: an overview. Asian J Psychiatry, p 103021

    Google Scholar 

  52. Brunn M, Diefenbacher A, Courtet P, Genieys W (2020) The future is knocking: how artificial intelligence will fundamentally change psychiatry. Acad Psychiatry 44(4):461–466

    Article  Google Scholar 

  53. Sarzynska-Wawer J, Wawer A, Pawlak A, Szymanowska J, Stefaniak I et al (2021) Detecting formal thought disorder by deep contextualized word representations. Psychiatry Res 304:114135

    Article  Google Scholar 

  54. Jotterand F, Bosco C (2020) Keeping the “Human in the Loop” in the age of artificial intelligence. Sci Eng Ethics 26(5):2455–2460

    Article  Google Scholar 

  55. Burr C, Morley J, Taddeo M, Floridi L (2020) Digital psychiatry: risks and opportunities for public health and wellbeing. IEEE-TTS 1(1):21–33

    Google Scholar 

  56. Torous J, Bucci S, Bell IH, Kessing LV, Faurholt-Jepsen M et al (2021) The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry 20(3):318–335

    Article  Google Scholar 

  57. Usmani UA, Watada J, Jaafar J, Aziz IA, Roy A (2021) A reinforcement learning based adaptive ROI generation for video object segmentation. IEEE Access 9:161959–161977

    Article  Google Scholar 

  58. Beli M, Bobi V, Badža M, Šolaja N, Ðuri-Jovii M, Kosti VS (2019) Artificial intelligence for assisting diagnostics and assessment of Parkinson’s disease—a review. Clin Neurol Neurosurg 184:105442

    Article  Google Scholar 

  59. Usmani UA, Roy, A, Watada J, Jaafar J, Aziz IA (2022) Enhanced reinforcement learning model for extraction of objects in complex imaging. In: Intelligent computing, pp 946–964. Springer, Cham

    Google Scholar 

  60. Greenwald MF, Danford ID, Shahrawat M, Ostmo S, Brown J et al (2020) Evaluation of artificial intelligence-based telemedicine screening for retinopathy of prematurity. JAAPOS 24(3):160–162

    Google Scholar 

  61. Greenwald MF, Danford ID, Shahrawat M, Ostmo S, Brown J, Kalpathy-Cramer J et al (2020) Evaluation of artificial intelligence-based telemedicine screening for retinopathy of prematurity. JAAPOS 24(3):160–162

    Google Scholar 

  62. Albert L, Capel I, García-Sáez G, Martín-Redondo P, Hernando ME et al (2020) Managing gestational diabetes mellitus using a smartphone application with artificial intelligence (SineDie) during the COVID-19 pandemic: much more than just telemedicine. Diabetes Res Clin Pract 169:108396

    Article  Google Scholar 

  63. Elsner P, Bauer A, Diepgen TL, Drexler H, Fartasch M et al (2018) Position paper: telemedicine in occupational dermatology-current status and perspectives. JDDG: J der Deutschen Dermatologischen Gesellschaft 16(8):969–974

    Google Scholar 

  64. Yuan KC, Tsai LW, Lee KH, Cheng YW, Hsu SC, Lo YS, Chen RJ (2020) The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit. Int J Med Inform 141:104176

    Article  Google Scholar 

  65. Lauritsen SM, Kalør ME, Kongsgaard EL, Lauritsen KM, Jørgensen MJ, Lange J, Thiesson B (2020) Early detection of sepsis utilizing deep learning on electronic health record event sequences. Artif Intell Med 104:101820

    Article  Google Scholar 

  66. Zhang J, Kowsari K, Harrison JH, Lobo JM, Barnes LE (2018) Patient2vec: a personalized interpretable deep representation of the longitudinal electronic health record. IEEE Access 6:65333–65346

    Article  Google Scholar 

  67. Zhao J, Feng Q, Wu P, Lupu RA, Wilke RA, Wells QS, Denny JC, Wei WQ (2019) Learning from longitudinal data in electronic health record and genetic data to improve cardiovascular event prediction. Sci Rep 9(1):1–10

    Google Scholar 

  68. Shi S, He D, Li L, Kumar N, Khan MK, Choo KKR (2020) Applications of blockchain in ensuring the security and privacy of electronic health record systems: a survey. Comput Secur 97:101966

    Article  Google Scholar 

  69. Basile AO, Yahi A, Tatonetti NP (2019) Artificial intelligence for drug toxicity and safety. Trends Pharmacol Sci 40(9):624–635

    Article  Google Scholar 

  70. Mak KK, Pichika MR (2019) Artificial intelligence in drug development: present status and future prospects. Drug Discov Today 24(3):773–780

    Article  Google Scholar 

  71. Zhavoronkov A, Vanhaelen Q, Oprea TI (2020) Will artificial intelligence for drug discovery impact clinical pharmacology? Clin Pharmacol Ther 107(4):780–785

    Article  Google Scholar 

  72. Bender A, Cortes-Ciriano I (2021) Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data. Drug Discov Today 26(4):1040–1052

    Article  Google Scholar 

  73. Hassanzadeh P, Atyabi F, Dinarvand R (2019) The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev 151:169–190

    Article  Google Scholar 

  74. Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P (2021) Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 25(3):1315–1360

    Article  Google Scholar 

  75. Zhou Y, Wang F, Tang J, Nussinov R, Cheng F (2020) Artificial intelligence in COVID-19 drug repurposing. Lancet Digit Health 2(12):e667–e676

    Article  Google Scholar 

  76. Gruson D, Helleputte T, Rousseau P, Gruson D (2019) Data science, artificial intelligence, and machine learning: opportunities for laboratory medicine and the value of positive regulation. Clin Biochem 69:1–7

    Article  Google Scholar 

  77. Santti U, Happonen A, Auvinen H (2020) Digitalization boosted recycling: gamification as an inspiration for young adults to do enhanced waste sorting. In: AIP conference proceedings, vol 2233, Iss 1, pp 1–12. https://doi.org/10.1063/5.0001547

  78. Happonen A, Santti U, Auvinen H, Räsänen T, Eskelinen T (2020) Digital age business model innovation for sustainability in University Industry Collaboration Model. In: E3S web of conferences, vol 211, Article 04005, pp 1–11. https://doi.org/10.1051/e3sconf/202021104005

  79. Happonen A, Ghoreishi M (2022) A mapping study of the current literature on digitalization and industry 4.0 technologies utilization for sustainability and circular economy in textile industries. Lecture Notes in Networks and Systems, vol 217, Chapter 63, pp 697–711. https://doi.org/10.1007/978-981-16-2102-4_63

  80. Ghoreishi M, Happonen A (2022) The case of fabric and textile industry: the emerging role of digitalization, internet-of-things and industry 4.0 for circularity. Lecture Notes in Networks and Systems, vol 216, pp 189–200. https://doi.org/10.1007/978-981-16-1781-2_18

  81. Vatousios A, Happonen A (2022) Transforming HR and improving talent profiling with qualitative analysis digitalization on candidates for career and team development efforts. In: Intelligent computing. Lecture Notes in Networks and Systems, vol 283, Chapter 78, Springer, pp 1149–1166. https://doi.org/10.1007/978-3-030-80119-9_78

  82. Vaddepalli K, Palacin V, Porras J, Happonen A (2023) Taxonomy of data quality metrics in digital citizen science. Lecture Notes in Networks and Systems, vol 578, pp 391–410. https://doi.org/10.1007/978-981-19-7660-5_34

  83. Zhang J, Zhang ZM (2023) Ethics and governance of trustworthy medical artificial intelligence. BMC Med Inform Decis Making 23(1):1–15

    Google Scholar 

  84. Vayena E, Blasimme A, Cohen IG (2018) Machine learning in medicine: addressing ethical challenges. PLoS Med 15(11):e1002689

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Usman Ahmad Usmani

  2. LUT University, Yliopistonkatu 34, 53850, Lappeenranta, Finland

    Ari Happonen

  3. Waseda University, Tiergartenstr. 17, Heidelberg, 69121, Japan

    Junzo Watada

  4. University of Turku, 20500, Turku, Finland

    Jayden Khakurel

Authors
  1. Usman Ahmad Usmani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ari Happonen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junzo Watada
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jayden Khakurel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ari Happonen .

Editor information

Editors and Affiliations

  1. Department of Design Engineering and Mathematics, Middlesex University London, London, UK

    Xin-She Yang

  2. Department of Biomedical Engineering, University of Reading, England, UK

    R. Simon Sherratt

  3. Department of Computer Science and Engineering, Techno International Newtown, Chakpachuria, West Bengal, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Usmani, U.A., Happonen, A., Watada, J., Khakurel, J. (2023). Artificial Intelligence Applications in Healthcare. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 694. Springer, Singapore. https://doi.org/10.1007/978-981-99-3091-3_89

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-3091-3_89

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3090-6

  • Online ISBN: 978-981-99-3091-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation