A System for Decision-Making Assistance Using Human–Computer Interactions for Cancer Detection

Authors

  • Prerna Mahajan Jain (Deemed to be University), Bangalore, Karnataka, India
  • Sunil Sharma Vivekananda Global University, Jaipur
  • Simarjeet Makkar Department of ISME, ATLAS SkillTech University, Mumbai, Maharashtra, India
  • Pooja Sharma Chitkara University, Rajpura, Punjab, India
  • Preeti Naval Maharishi University of Information Technology, Lucknow, India

Keywords:

Artificial intelligence, content-based image retrieval, cancer detection, decision making

Abstract

The increasing growth of telemedicine and recent developments in diagnostic artificial intelligence (AI) make it essential to think through the benefits and drawbacks of incorporating AI-based assistance into novel healthcare approaches. In this study, we expand on previous advances in the precision of image-based AI for skin cancer (SC) detection to consider the consequences of various presentations of AI-based assistance across varying degrees of clinical knowledge and various clinical processes. We search that less-experienced clinicians benefit the most from AI-based assistance of clinical decision-making (CDM) and that AI-based support enhances diagnostic precision above that of either AI. We also discover that in the context of mobile technologies, AI-based multiclass probabilities outperform content-based image retrieval (CBIR) presentations of AI. Together, our method and outcome present a basis for further study across the whole range of image-based diagnostics, with the ultimate goal of enhancing human-computer cooperation in clinical settings.

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Published

24.03.2024

How to Cite

Mahajan, P. ., Sharma, S. ., Makkar, S. ., Sharma, P. ., & Naval, P. . (2024). A System for Decision-Making Assistance Using Human–Computer Interactions for Cancer Detection . International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 798–803. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5212

Issue

Vol. 12 No. 19s (2024)

Section

Research Article

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