Skip to main content
SpringerLink
Log in

Development of an Artificial Intelligent Health Chatbot for Improved Telemedicine

  • Conference paper
  • First Online:
Information Systems for Intelligent Systems (ISBM 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 379))

Included in the following conference series:

  • 88 Accesses

Abstract

Medical diagnosis is a crucial aspect of the healthcare industry, examining and identifying a patient's illness. Integrating an Artificial Intelligent Chatbot is a valuable tool in accurate illness diagnosis, as it can be equipped with comprehensive datasets and trained to provide appropriate and timely diagnoses, even remotely. The limitations and barriers of telemedicine hinder access to timely and quality healthcare services, particularly in areas with a shortage of healthcare professionals and for patients facing transportation, cost, and time constraints. There is a need to overcome these challenges by developing a user-friendly and effective healthcare chatbot that can provide personalised care, ensure the privacy and security of health information, and improve the accessibility and effectiveness of telemedicine services. This research aimed at developing a healthcare chatbot to improve telemedicine by diagnosing common diseases using an Artificial Intelligence model. By employing the Decision Tree Classifier Artificial Intelligence technique, a medical diagnosis model was developed from a comprehensive open-source medical dataset. A Python-built Telegram chatbot capable of medical diagnosis was developed using the trained model and deployed to Telegram through Telegram's application programming interface. After deployment, the chatbot accurately performs medical diagnosis and delivers an email to your registered doctor for further treatment. Overall, this work presents a valuable tool for patients and healthcare professionals, facilitating timely and accurate medical diagnosis.

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
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover 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. Reddy, S., Fox, J., Purohit, M.P.: Artificial intelligence-enabled healthcare delivery. J. R. Soc. Med.; SAGE Publications Ltd 112(1), 22–28 (2019). https://doi.org/10.1177/0141076818815510

  2. Garg, G., Patil, A., Kasudhan, K.S.: Naturalistic evaluation of pharmacotherapy consultations provided to hospital clinicians: a developing country’s perspective. J. Pharm. Technol. 35(4), 155–163 (2019). https://doi.org/10.1177/8755122519843013

    Article  Google Scholar 

  3. “j.mlwa.2020.100006”.

    Google Scholar 

  4. “Introduction to chatbots in healthcare”: https://infermedica.com/blog/articles/introduction-to-chatbots-in-healthcare. Accessed 18 Mar 2023

  5. Medical doctors (per 10 000 population). https://www.who.int/data/gho/data/indicators/indicator-details/GHO/medical-doctors-(per-10-000-population). Accessed 18 Mar 2023

  6. Haleem, A., Javaid, M., Singh, R.P., Suman, R.: Telemedicine for healthcare: capabilities, features, barriers, and applications. In: Sensors International, vol. 2. KeAi Communications Co. https://doi.org/10.1016/j.sintl.2021.100117. Accessed 1 Jan 2021

  7. Ranjitha, M., Nathan, K., Joseph, L.: Artificial intelligence algorithms and techniques in the computation of player-adaptive games. In: Journal of Physics: Conference Series, Institute of Physics Publishing (2020). https://doi.org/10.1088/1742-6596/1427/1/012006

  8. Pang, S., Gong, J.: C5.0 classification algorithm and application on individual credit evaluation of banks. Syst. Eng. Theory Pract. 29(12), 94–104 (2009). https://doi.org/10.1016/s1874-8651(10)60092-0

    Article  Google Scholar 

  9. Machine Learning: What it is and why it matters | SAS. https://www.sas.com/en_us/insights/analytics/machine-learning.html. Accessed 24 Mar 2023

  10. Barletta, V.S., Caivano, D., Colizzi, L., Dimauro, G., Piattini, M.: Clinical-chatbot Ahp evaluation based on ‘quality in use’ of Iso/Iec 25010. SSRN Electron. J. (2022). https://doi.org/10.2139/ssrn.4041111

    Article  Google Scholar 

  11. Bali, M., Mohanty, S., Chatterjee, D.S., Sarma, M., Puravankara, R.: Diabot: a predictive medical chatbot using ensemble learning. Int. J. Recent. Technol. Eng. (IJRTE) 8(2), 6334–6340 (2019). https://doi.org/10.35940/ijrte.B2196.078219

  12. Srivastava, S., Prabhakar, T.V.: Hospitality of chatbot building platforms. In: Proceedings of the 2nd ACM SIGSOFT International Workshop on Software Qualities and Their Dependencies, New York, NY, USA: ACM, pp. 12–19 (2019). https://doi.org/10.1145/3340495.3342751

  13. L. Chinmayychakraborty, Sohmenneditors, S.V.: EAI/Springer Innovations in Communication and Computing Healthcare Informatics forrFighting COVID-19 and Future Epidemics. http://www.springer.com/series/15427

  14. Tofan, D., Galster, M., Avgeriou, P., Schuitema, W.: Past and future of software architectural decisions – a systematic mapping study. Inf. Softw. Technol. 56(8), 850–872 (2014). https://doi.org/10.1016/j.infsof.2014.03.009

    Article  Google Scholar 

  15. Mjaaland, H.L., Vinay, R., Setty, J., Anand, A.: Detecting Fake News and Rumors in Twitter Using Deep Neural Networks Vinay Jayarama Setty (2020)

    Google Scholar 

  16. Frank, U.: Conceptual Modelling as the Core of the Information Systems Discipline-Perspectives and Epistemological Challenges ECIS2018: Beyond Digitization-Facets of Socio-Technical Change View project. https://www.researchgate.net/publication/2837472

  17. Padmanabhan, B.: Spring 2012 EECS810-Principles of Software Engineering

    Google Scholar 

  18. Windle, P.E.: Secondary data analysis: is it useful and valid? J. Perianesth. Nurs. 25(5), 322–324 (2010). https://doi.org/10.1016/j.jopan.2010.07.005

    Article  Google Scholar 

  19. Data Collection. https://ori.hhs.gov/education/products/n_illinois_u/datamanagement/dctopic.html. Accessed: 24 Mar 2023

  20. García, S., Luengo, J., Herrera, F.: Intelligent Systems Reference Library 72 Data Preprocessing in Data Mining. http://www.springer.com/series/8578

Download references

Author information

Authors and Affiliations

  1. School of International Service, American University, Washington, USA

    Morayo E. Awomoyi

  2. Department of Electrical and Information Engineering, Covenant University, Ogun State, Ota, Nigeria

    Samuel Joshua & Kennedy Okokpujie

  3. Africa Centre of Excellence for Innovative and Transformative STEM Education, Lagos State University, Ojo, Lagos State, Nigeria

    Kennedy Okokpujie

  4. OVA Foundation, 21651 Millington, Maryland, YO42, USA

    Adenugba Vincent Akingunsoye

  5. Department of Mechanical and Mechatronics, Engineering, Afe Babalola University, Ado Ekiti, 360001, Nigeria

    Adeyinka O. M. Adeoye & Imhade P. Okokpujie

  6. Department of Mechanical and Industrial Engineering Technology, University of Johannesburg, Johannesburg, 2028, South Africa

    Imhade P. Okokpujie

Authors
  1. Morayo E. Awomoyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samuel Joshua
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kennedy Okokpujie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adeyinka O. M. Adeoye
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adenugba Vincent Akingunsoye
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Imhade P. Okokpujie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kennedy Okokpujie .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Khon Kaen University, Khon Kaen, Thailand

    Chakchai So In

  2. National Institute of Technology Raipur, Raipur, Chhattisgarh, India

    Narendra D. Londhe

  3. Institute of Management, Nirma University, Ahmedabad, Gujarat, India

    Nityesh Bhatt

  4. Estonian Business School, Helsinki, Finland

    Meelis Kitsing

Rights and permissions

Reprints and permissions

Copyright information

© 2024 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

Awomoyi, M.E., Joshua, S., Okokpujie, K., Adeoye, A.O.M., Akingunsoye, A.V., Okokpujie, I.P. (2024). Development of an Artificial Intelligent Health Chatbot for Improved Telemedicine. In: So In, C., Londhe, N.D., Bhatt, N., Kitsing, M. (eds) Information Systems for Intelligent Systems. ISBM 2023. Smart Innovation, Systems and Technologies, vol 379. Springer, Singapore. https://doi.org/10.1007/978-981-99-8612-5_48

Download citation

Publish with us

Policies and ethics

Search

Navigation