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Healthcare Assistance Challenges-Driven Neurosymbolic AI

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  • Kaushik Roy

    (Student in computer science at the AI Institute, University of South Carolina, USA)

Abstract

Although Artificial Intelligence technology has proven effective in providing healthcare assistance by analyzing health data, it still falls short in supporting decision-making. This deficiency largely stems from the predominance of opaque neural networks, particularly in mental health care AI applications, which raise concerns about their unpredictable and unverifiable nature. This skepticism hinders the transition from information support to decision support. This presentation will explore neurosymbolic approaches that combine neural networks with symbolic control and verification mechanisms. These approaches aim to unlock AI’s full potential by enhancing information analysis and decision-making support for healthcare assistance.

Suggested Citation

  • Kaushik Roy, 2024. "Healthcare Assistance Challenges-Driven Neurosymbolic AI," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 58(2), pages 50012-50016, August.
    • Handle: RePEc:abf:journl:v:58:y:2024:i:2:p:50012-50016
    DOI: 10.26717/BJSTR.2024.58.009111
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