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1716891 Vol 9 · Issue 10 Download Paper

HealHive: A Secure Anonymous AI-Driven Mental Health Support Platform

Sachin Hiranwale Swaraj Shelar Satya Pragyan Barik Suchit Parte Vansh Asnani

Subject area: Science,Engineering and Technology  ·  Area of research: Mental Health

DOI: 10.64388/IREV9I10-1716891

Abstract

The occurrence of mental disorders is on the rise in India; however, a significant portion of the population does not approach any professionals owing to factors like stigma, confidentiality issues, affordability concerns, and unavailability of trustworthy therapists. Although several online platforms have come into existence for dealing with such challenges, they fail to ensure anonymity, intelligent therapist matchmaking, and safety precautions. This study suggests HealHive, an AI-driven, anonymous, and secure online platform aimed at helping users deal with their mental issues using advanced technology. The system uses NLP for the evaluation of the user's responses, classifies the severity level of their mental disorder, and recommends further courses of action. In case of low-risk patients, the AI takes charge, while high-risk patients are handed over to verified therapists via intelligent matchmaking services. Anonymity and security are two of the key concerns for users of online systems, and therefore, HealHive uses anonymized verification, encryption of communications, and intelligent moderation based on AI for preventing any unsafe activities. Through comparative analysis with other systems, the advantages of the model under consideration become apparent.

Keywords

Analysis, Mental Health, AI Chatbot, Anonymity, Therapist Matching, Secure Communication, NLP, Digital Healthcare

References

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[2] Balcombe L., et al., “Artificial Intelligence-Enabled Chatbots in Mental Health: A Narrative Review,” Digital Health, vol. 9, pp. 1–15, 2023.

[3] Olawade D.B., et al., “Enhancing Mental Health with Artificial Intelligence: Current Trends and Future Prospects,” Healthcare Analytics, vol. 4, pp. 1–10, 2024.

[4] Haque M.D.R., et al., “Applications of Chatbots in Mental Health: A Systematic Review,” Healthcare, vol. 11, no. 12, pp. 1–18, 2023.

[5] Zhang Q., et al., “Generative AI Chatbots in Mental Health: Systematic Review and Meta-Analysis,” Journal of Medical Internet Research, vol. 27, pp. 1–14, 2025.

[6] Nyakhar S., et al., “Effectiveness of AI Chatbots in Alleviating Symptoms of Depression and Anxiety: Meta-Analysis,” Journal of Medical Internet Research, vol. 27, no. 1, pp. 1–12, 2025.

[7] Wang Y., et al., “A Cognitive Behavioral Therapy-Based AI Chatbot for Mental Health Support: Randomized Controlled Trial,” JMIR mHealth and uHealth, vol. 13, no. 1, pp. 1–13, 2025.

[8] Pichowicz W., et al., “Performance of AI Chatbots in Crisis Detection and Mental Health Support,” Scientific Reports, vol. 15, pp. 1–11, 2025.

[9] Yoo D.W., et al., “Exploring the Values and Harms of AI Chatbots in Mental Health,” arXiv preprint arXiv:2504.18932, pp. 1–12, 2025.

[10] Stanford Human-Centered AI Institute, “Exploring the Risks of AI in Mental Health Care,” Stanford University, 2025.

How to cite this paper

Sachin Hiranwale, Swaraj Shelar, Satya Pragyan Barik, Suchit Parte, Vansh Asnani "HealHive: A Secure Anonymous AI-Driven Mental Health Support Platform" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 3467-3471 https://doi.org/10.64388/IREV9I10-1716891
Sachin Hiranwale, Swaraj Shelar, Satya Pragyan Barik, Suchit Parte, Vansh Asnani "HealHive: A Secure Anonymous AI-Driven Mental Health Support Platform" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026, doi: https://doi.org/10.64388/IREV9I10-1716891
Sachin Hiranwale, Swaraj Shelar, Satya Pragyan Barik, Suchit Parte, Vansh Asnani (2026). HealHive: A Secure Anonymous AI-Driven Mental Health Support Platform. Iconic Research And Engineering Journals, 9(10). doi: https://doi.org/10.64388/IREV9I10-1716891
Sachin Hiranwale, Swaraj Shelar, Satya Pragyan Barik, Suchit Parte, Vansh Asnani "HealHive: A Secure Anonymous AI-Driven Mental Health Support Platform" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026. Crossref, https://doi.org/10.64388/IREV9I10-1716891
@article{1716891,
      author = {Sachin Hiranwale, Swaraj Shelar, Satya Pragyan Barik, Suchit Parte, Vansh Asnani},
      title = {HealHive: A Secure Anonymous AI-Driven Mental Health Support Platform},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {3467-3471},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716891.pdf},
      abstract = {The occurrence of mental disorders is on the rise in India; however, a significant portion of the population does not approach any professionals owing to factors like stigma, confidentiality issues, affordability concerns, and unavailability of trustworthy therapists. Although several online platforms have come into existence for dealing with such challenges, they fail to ensure anonymity, intelligent therapist matchmaking, and safety precautions. This study suggests HealHive, an AI-driven, anonymous, and secure online platform aimed at helping users deal with their mental issues using advanced technology. The system uses NLP for the evaluation of the user's responses, classifies the severity level of their mental disorder, and recommends further courses of action. In case of low-risk patients, the AI takes charge, while high-risk patients are handed over to verified therapists via intelligent matchmaking services. Anonymity and security are two of the key concerns for users of online systems, and therefore, HealHive uses anonymized verification, encryption of communications, and intelligent moderation based on AI for preventing any unsafe activities. Through comparative analysis with other systems, the advantages of the model under consideration become apparent.},
      keywords = {Analysis, Mental Health, AI Chatbot, Anonymity, Therapist Matching, Secure Communication, NLP, Digital Healthcare},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716891}
  }