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Paper ID:IJSREM68673

AarogyaSathi: Design and Preliminary Evaluation of a Multilingual Public Health Information Chatbot

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68673

Volume:

10 |

11 |

Nov 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM68673

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

Salman Mohammad Khan

Salman Mohammad Khan

Computer Science & Engineering (Data Science)

Bhagwan Parshuram Institute of Technology

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Sidharth Singh

Sidharth Singh

Computer Science & Engineering (Data Science)

Bhagwan Parshuram Institute of Technology

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ASK

Aditya Sam Koshy

Computer Science & Engineering (Data Science)

Bhagwan Parshuram Institute of Technology

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Dhairya Summi

Dhairya Summi

Computer Science & Engineering (Data Science)

Bhagwan Parshuram Institute of Technology

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Abstract

AarogyaSathi is a public health information chatbot that our team built in response to Smart India Hackathon problem statement SIH25049. It brings together a curated knowledge base, multilingual retrieval, language model answers, claim verification, emergency phrase rules, and an experimental detector that tracks symptom reports by pincode. Here we describe the version running on Oracle Cloud and ask how far the available tests support it. We read the source code, inspected the live service, reran the repository’s component benchmarks, and sent the system synthetic conversations. All 37 automated tests in the 26 October run passed, and the supplied evaluation sets gave 30/30 correct intent labels, 15/15 expected retrieval targets, and 34/34 expected claim verdicts. Training overlap and configuration differences limit the interpretation of these scores. Of the 30 intent questions, 14 also appear in the training data, and the benchmark thresholds differ from the production settings. The conversations turned up problems the benchmarks missed. A sentence saying the user had no chest pain triggered an emergency response, and an Odia question about prevention got no useful answer. Reading the code revealed a further error, in how vaccination ages are converted. The web interface worked, and a subsequent Meta WhatsApp test-number demonstration confirmed replies for greeting, dengue-information, and claim-checking prompts. Integration with official outbreak data re-mained unverified. Our findings are preliminary engineering evidence about how the implemented response paths behave and where further validation is needed. Clinical effectiveness was not assessed.

KEYWORDS

public health information; multilingual chatbot; retrieval augmented generation; health misinformation; proto-type evaluation

Cite This Article

Salman Mohammad Khan, Sidharth Singh, Aditya Sam Koshy, Dhairya Summi, "AarogyaSathi: Design and Preliminary Evaluation of a Multilingual Public Health Information Chatbot," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 11, pp. 1-6, Nov 2026. https://doi.org/10.55041/IJSREM68673

Full Article/Conclusion

AarogyaSathi is a deployed web prototype with a demon-strated WhatsApp test-number interface that combines curated health information, multilingual retrieval, claim checking, emergency rules, and experimental geographic signals. We reproduced the supplied component benchmarks successfully, but training overlap and configuration differences limit how far they can be interpreted. The live synthetic conversations exposed an emergency-negation error, unrelated emergency advice, and a failed Odia answer, and source inspection found faulty vaccination age mapping. These findings give us concrete priorities for improving the system. The What-sApp follow-up established basic bidirectional delivery for an approved test recipient.

References

[1]     World Health Organization et al., “Managing the COVID-19 infodemic: Promoting healthy behaviours and mitigating the harm from misinformation and disinformation,” Sep. 23, 2020. [Online]. Available: https://www.who.int/news/item/23-09-2020-managing-the-c ovid-19-infodemic-promoting-healthy-behaviours-and-mitigating-the-h arm-from-misinformation-and-disinformation. Accessed: Oct. 29, 2026. [2]     DRIEMS University, “Smart India Hackathon problem statements,” SIH25049, Government of Odisha, 2025. [Online]. Available: https://www.driems.ac.in/wp-content/uploads/2025/09/Smart-India-Hac kathon_PROBLEM-STATEMENTS.pdf. Accessed: Oct. 29, 2026. [3]     H. L. Semigran, J. A. Linder, C. Gidengil, and A. Mehrotra, “Evaluation of symptom checkers for self diagnosis and triage: Audit study,” BMJ, vol. 351, Art. no. h3480, 2015, doi: 10.1136/bmj.h3480. [4]     P. Lewis et al., “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in Advances in Neural Information Processing Systems, vol. 33, 2020. [Online]. Available: https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc 26945df7481e5-Abstract.html [5]     F. Feng, Y. Yang, D. Cer, N. Arivazhagan, and W. Wang,“Language-agnostic BERT sentence embedding,” in Proc. ACL, 2022,pp. 878–891, doi: 10.18653/v1/2022.acl-long.62. [6]     L. Laranjo et al., “Conversational agents in healthcare: A systematic review,” Journal of the American Medical Informatics Association, vol. 25, no. 9, pp. 1248–1258, 2018, doi: 10.1093/jamia/ocy072. [7]     M. Milne-Ives et al., “The effectiveness of artificial intelligence conversational agents in health care: Systematic review,” Journal of Medical Internet Research, vol. 22, no. 10, Art. no. e20346, 2020, doi: 10.2196/20346. [8]     National Health Mission, Ministry of Health and Family Welfare, Government of India, “National Immunization Schedule.” Accessed: Oct. 29, 2026. [Online]. Available: https://nhm.gov.in/New_Updates_2018/NHM_Components/Immunizat ion/report/National_%20Immunization_Schedule.pdf [9]     National Centre for Disease Control, Government of India, “Integrated Disease Surveillance Programme.” Accessed: Oct. 26, 2026. [Online]. Available: https: //ncdc.mohfw.gov.in/includes/About/CentresAndDivision/IDSP.php [10]    World Health Organization, Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-Modal Models, 2024, ISBN 978-92-4-008475-9. [Online]. Available: https://www.who.int/publications/i/item/9789240084759. Accessed: Oct. 29, 2026.

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Publication Date

11/2/2026

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Review Rounds: 2

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