Vol. 3 No. 2 (2023): Journal of Deep Learning in Genomic Data Analysis
Articles

Human-Centric Design of AI-driven Clinical Decision Support Systems: Designs AI-driven clinical decision support systems with a focus on user-centered design principles to enhance usability and adoption

PDF
  • Dr. Kimiko Tanaka
Dr. Kimiko Tanaka
Professor of Computer Science, Waseda University, Japan
Cover

Published 20-12-2023

Keywords

  • Clinical Decision Support Systems,
  • Human-Centric Design,
  • Patient Outcomes
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Kimiko Tanaka, “Human-Centric Design of AI-driven Clinical Decision Support Systems: Designs AI-driven clinical decision support systems with a focus on user-centered design principles to enhance usability and adoption”, Journal of Deep Learning in Genomic Data Analysis, vol. 3, no. 2, pp. 24–34, Dec. 2023, Accessed: Dec. 23, 2024. [Online]. Available: https://thelifescience.org/index.php/jdlgda/article/view/47

Abstract

This research paper explores the critical role of human-centric design in the development of AI-driven Clinical Decision Support Systems (CDSS). With the increasing complexity of healthcare systems and the growing volume of clinical data, AI has emerged as a valuable tool to assist healthcare professionals in making informed decisions. However, the effectiveness of AI-driven CDSS depends not only on the accuracy of the underlying algorithms but also on how well these systems are designed to fit into the workflow and decision-making processes of healthcare providers. This paper discusses the key principles of human-centric design and presents a framework for designing AI-driven CDSS that prioritize user needs and preferences. By incorporating human-centric design principles into the development process, AI-driven CDSS can enhance usability, increase acceptance among healthcare professionals, and ultimately improve patient outcomes.

PDF

Downloads

Download data is not yet available.

References

  1. Gadhiraju, Asha, and Kummaragunta Joel Prabhod. "Reinforcement Learning for Optimizing Surgical Procedures and Patient Recovery." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 105-140.
  2. Pushadapu, Navajeevan. "The Importance of Remote Clinics and Telemedicine in Healthcare: Enhancing Access and Quality of Care through Technological Innovations." Asian Journal of Multidisciplinary Research & Review 1.2 (2020): 215-261.
  3. Potla, Ravi Teja. "AI and Machine Learning for Enhancing Cybersecurity in Cloud-Based CRM Platforms." Australian Journal of Machine Learning Research & Applications 2.2 (2022): 287-302.
  4. Thatoi, Priyabrata, et al. "Natural Language Processing (NLP) in the Extraction of Clinical Information from Electronic Health Records (EHRs) for Cancer Prognosis." International Journal 10.4 (2023): 2676-2694.
  5. Bao, Y.; Qiao, Y.; Choi, J.E.; Zhang, Y.; Mannan, R.; Cheng, C.; He, T.; Zheng, Y.; Yu, J.; Gondal, M.; et al. Targeting the lipid kinase PIKfyve upregulates surface expression of MHC class I to augment cancer immunotherapy. Proc. Natl. Acad. Sci. USA 2023, 120, e2314416120.
  6. Krothapalli, Bhavani, Lavanya Shanmugam, and Jim Todd Sunder Singh. "Streamlining Operations: A Comparative Analysis of Enterprise Integration Strategies in the Insurance and Retail Industries." Journal of Science & Technology 2.3 (2021): 93-144.
  7. Gayam, Swaroop Reddy. "Artificial Intelligence for Natural Language Processing: Techniques for Sentiment Analysis, Language Translation, and Conversational Agents." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 175-216.
  8. Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Compliance and Regulatory Reporting in Banking: Advanced Techniques, Models, and Real-World Applications." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 151-189.
  9. Putha, Sudharshan. "AI-Driven Natural Language Processing for Voice-Activated Vehicle Control and Infotainment Systems." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 255-295.
  10. Sahu, Mohit Kumar. "Machine Learning Algorithms for Personalized Financial Services and Customer Engagement: Techniques, Models, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 272-313.
  11. Kasaraneni, Bhavani Prasad. "Advanced Machine Learning Models for Risk-Based Pricing in Health Insurance: Techniques and Applications." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 170-207.
  12. Kondapaka, Krishna Kanth. "Advanced Artificial Intelligence Models for Predictive Analytics in Insurance: Techniques, Applications, and Real-World Case Studies." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 244-290.
  13. Devan, Munivel, Bhavani Krothapalli, and Mahendher Govindasingh Krishnasingh. "Hybrid Cloud Data Integration in Retail and Insurance: Strategies for Seamless Interoperability." Journal of Artificial Intelligence Research 3.2 (2023): 103-145.
  14. Kasaraneni, Ramana Kumar. "AI-Enhanced Pharmacoeconomics: Evaluating Cost-Effectiveness and Budget Impact of New Pharmaceuticals." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 291-327.
  15. Pattyam, Sandeep Pushyamitra. "AI-Driven Data Science for Environmental Monitoring: Techniques for Data Collection, Analysis, and Predictive Modeling." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 132-169.
  16. Kuna, Siva Sarana. "Reinforcement Learning for Optimizing Insurance Portfolio Management." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 289-334.
  17. Prabhod, Kummaragunta Joel. "The Role of Machine Learning in Genomic Medicine: Advancements in Disease Prediction and Treatment." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 1-52.
  18. Pushadapu, Navajeevan. "Optimization of Resources in a Hospital System: Leveraging Data Analytics and Machine Learning for Efficient Resource Management." Journal of Science & Technology 1.1 (2020): 280-337.
  19. Potla, Ravi Teja. "Integrating AI and IoT with Salesforce: A Framework for Digital Transformation in the Manufacturing Industry." Journal of Science & Technology 4.1 (2023): 125-135.
  20. Gayam, Swaroop Reddy, Ramswaroop Reddy Yellu, and Praveen Thuniki. "Artificial Intelligence for Real-Time Predictive Analytics: Advanced Algorithms and Applications in Dynamic Data Environments." Distributed Learning and Broad Applications in Scientific Research 7 (2021): 18-37.
  21. Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Customer Behavior Analysis in Insurance: Advanced Models, Techniques, and Real-World Applications." Journal of AI in Healthcare and Medicine 2.1 (2022): 227-263.
  22. Putha, Sudharshan. "AI-Driven Personalization in E-Commerce: Enhancing Customer Experience and Sales through Advanced Data Analytics." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 225-271.
  23. Sahu, Mohit Kumar. "Machine Learning for Personalized Insurance Products: Advanced Techniques, Models, and Real-World Applications." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 60-99.
  24. Kasaraneni, Bhavani Prasad. "AI-Driven Approaches for Fraud Prevention in Health Insurance: Techniques, Models, and Case Studies." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 136-180.
  25. Kondapaka, Krishna Kanth. "Advanced Artificial Intelligence Techniques for Demand Forecasting in Retail Supply Chains: Models, Applications, and Real-World Case Studies." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 180-218.
  26. Kasaraneni, Ramana Kumar. "AI-Enhanced Portfolio Optimization: Balancing Risk and Return with Machine Learning Models." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 219-265.
  27. Pattyam, Sandeep Pushyamitra. "AI-Driven Financial Market Analysis: Advanced Techniques for Stock Price Prediction, Risk Management, and Automated Trading." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 100-135.
  28. Kuna, Siva Sarana. "The Impact of AI on Actuarial Science in the Insurance Industry." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 451-493.
  29. Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Dynamic Pricing in Insurance: Advanced Techniques, Models, and Real-World Application." Hong Kong Journal of AI and Medicine 4.1 (2024): 258-297.

Similar Articles

You may also start an advanced similarity search for this article.