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Paper ID:IJSREM68439

Human centered design thinking approach for an AI based healthcare application

Stream/Area:

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Subject:

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Paper ID:

IJSREM68439

Volume:

10 |

10 |

Oct 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

L.SaaiKirthiga

L.SaaiKirthiga

Department of computer science and engineering

Kings College of Engineering

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R.Vigneshwari

R.Vigneshwari

Department of computer science and engineering

Kings College of Engineering

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R.Swathi

R.Swathi

Department of computer science and engineering

Kings College of Engineering

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J.Sandhiya

J.Sandhiya

Department of computer science and engineering

Kings College of Engineering

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S.Yazhini

S.Yazhini

Department of computer science and engineering

Kings College of Engineering

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A.Sandhiya

A.Sandhiya

Department of computer science and engineering

Kings College of Engineering

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Mentors

P

Prof.S.AbikayilAarthi

Department of computer science and engineering

Kings College of Engineering

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Abstract

Artificial intelligence (AI) is increasingly used in healthcare for risk prediction, triage, imaging and remote monitoring, yet many promising tools perform well in the laboratory but fail to become part of routine care. A common reason is that they are built around available data and algorithms rather than around the real needs, workflows and constraints of patients and clinicians. This paper proposes the use of human-centred design thinking (Empathize, Define, Ideate, Prototype and Test), consistent with the principles of ISO 9241-210, as a framework for developing an AI-based healthcare application. It describes how each stage can be applied to a clinical setting, with example research methods, personas and “How might we” questions; presents a modular system architecture with candidate modelling techniques, explainability and safety features; outlines an illustrative scenario, an implementation roadmap and an evaluation plan; and discusses ethical concerns such as patient safety, privacy, bias, transparency and accountability. The paper is conceptual: it offers a design approach and does not report experimental results. It concludes that combining design thinking with AI helps create healthcare applications that are safe, useful, trusted and adopted by the people who use them.

KEYWORDS

human-centred design design thinking artificial intelligence healthcare clinical decision support explainable AI usability patient safety digital health

Cite This Article

L.SaaiKirthiga, R.Vigneshwari, R.Swathi, J.Sandhiya, S.Yazhini, A.Sandhiya, Prof.S.AbikayilAarthi, "Human centered design thinking approach for an AI based healthcare application," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-6, Oct 2026.

Full Article/Conclusion

This paper proposed a human-centred design thinking approach for developing an AI-based healthcare application. By empathising with patients and clinicians, defining the right problem, deciding the division of work between AI and humans, prototyping early and testing repeatedly, designers can build applications that are not only technically capable but also safe, understandable, fair and useful in real care settings. Future work should implement a prototype following this framework, compare different modelling techniques, and evaluate the effect on clinical outcomes, clinician workload, engagement and fairness with real users.

References

Altman, M., Huang, T. T. K., & Breland, J. Y. (2018). Design thinking in health care. Preventing Chronic Disease, 15, Article 180128. Amershi, S., Weld, D., Vorvoreanu, M., et al. (2019). Guidelines for human-AI interaction. In Proceedings of CHI 2019. ACM. Beede, E., Baylor, E., Hersch, F., et al. (2020). A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. In Proceedings of CHI 2020. ACM. Brooke, J. (1996). SUS: A quick and dirty usability scale. In P. W. Jordan et al. (Eds.), Usability evaluation in industry (pp. 189–194). Taylor & Francis. Brown, T. (2008). Design thinking. Harvard Business Review, 86(6), 84–92. Cai, C. J., Winter, S., Steiner, D., Wilcox, L., & Terry, M. (2019). “Hello AI”: Uncovering the onboarding needs of medical practitioners for human-AI collaborative decision-making. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), Article 104. Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. Dorst, K. (2011). The core of ‘design thinking’ and its application. Design Studies, 32(6), 521–532. Esteva, A., Robicquet, A., Ramsundar, B., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. Floridi, L., Cowls, J., Beltrametti, M., et al. (2018). AI4People: An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707. Gulshan, V., Peng, L., Coram, M., et al. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402–2410. Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159–172.

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Publication Date

10/28/2026

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Double-Blind Peer Review

Review Rounds: 2

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