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Collaborative Decision-Making Assistant for Healthcare Professionals: A Human-Centered AI Prototype Powered by Azure Open AI

Published: 16 August 2023 Publication History

Abstract

This paper presents a demonstration of a collaborative decision-making assistant designed to support healthcare professionals in making informed and personalized treatment decisions for their patients.
The prototype highlights the integration of advanced AI algorithms, explainable AI techniques, and the utilization of mainly Microsoft related technology stacks, including ASP.Net Core and Azure Open AI services.
The significance of this prototype lies in its contribution to the field of human-computer interaction, design and critical perspectives, specifically within the sub-domain of Human-centered AI.
The prototype demonstration highlights innovation in the design, usage, sociotechnical context, and application of the prototype, and emphasizes commitment to ethical AI practices and responsible AI development, with considerations for fairness, transparency, and mitigating bias in AI algorithms, promoting the ethical use of AI in healthcare.

References

[1]
Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris Kotsiantis . Explainable AI: A Review of Machine Learning Interpretability Methods (2021). https://doi.org/10.3390/e23010018.
[2]
Microsoft. "Azure Open AI Services." https://azure.microsoft.com/en-us/services/cognitive-services/.
[3]
N'gbesso, Yolande. (2020). Integration of Artificial Intelligence in electronic health records: Impacts and Challenges. https://www.researchgate.net/publication/347447047.
[4]
Microsoft. "Azure Active Directory." https://azure.microsoft.com/en-us/services/active-directory/.
[5]
Krist AH, Tong ST, Aycock RA, Longo DR. Engaging Patients in Decision-Making and Behavior Change to Promote Prevention. Stud Health Technol Inform. 2017;240:284-302. 28972524; PMCID: PMC6996004.

Cited By

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  • (2024)Advancing Patient-Centered Shared Decision-Making with AI Systems for Older Adult Cancer PatientsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642353(1-20)Online publication date: 11-May-2024

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cover image ACM Conferences
COMPASS '23: Proceedings of the 6th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies
August 2023
170 pages
ISBN:9798400701498
DOI:10.1145/3588001
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 August 2023

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Author Tags

  1. Azure Open AI
  2. Collaborative decision-making
  3. Explainable AI
  4. Healthcare professionals
  5. Human-centered AI
  6. Sociotechnical context

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  • Research
  • Refereed limited

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COMPASS '23
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Overall Acceptance Rate 25 of 50 submissions, 50%

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Cited By

View all
  • (2024)Advancing Patient-Centered Shared Decision-Making with AI Systems for Older Adult Cancer PatientsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642353(1-20)Online publication date: 11-May-2024

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