Healthcare Assistance Challenges-Driven Neurosymbolic AI

Kaushik Roy
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Kaushik Roy: Student in computer science at the AI Institute, University of South Carolina, USA

Biomedical Journal of Scientific & Technical Research, 2024, vol. 58, issue 2, 50012-50016

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.

Keywords: Journals on Medical Drug and Therapeutics; Journals on Emergency Medicine; Physical Medicine and Rehabilitation; Journals on Infectious Diseases Addiction Science and Clinical Pathology; Open Access Clinical and Medical Journal; Journals on Biomedical Science; List of Open Access Medical Journal; Journals on Biomedical Engineering; Open Access Medical Journal; Biomedical Science Articles; Journal of Scientific and Technical Research (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:abf:journl:v:58:y:2024:i:2:p:50012-50016

DOI: 10.26717/BJSTR.2024.58.009111

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