Dissertations for Informatics (Knowledge and Data Management)

Permanent URI for this collection

https://bspace.buid.ac.ae/handle/1234/488

Browse

Browsing Dissertations for Informatics (Knowledge and Data Management) by Subject "algorithmic transparency"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Exploring the Impact of Explainable Artificial Intelligence on Decision-making in Healthcare
    (The British University in Dubai, 2023-07) MOHAMMAD, AHMAD HASAN
    As artificial intelligence (AI) advances in healthcare, there is an increasing need to understand how AI-driven decision-making affects healthcare workers and patients. The development of explainable artificial intelligence (XAI) systems, which attempt to give visible and interpretable explanations for AI algorithms' judgements, is a vital part of AI in healthcare. This study investigates the influence of XAI on healthcare decision-making and its potential to improve trust, acceptance, and collaboration between AI systems and human decision-makers. The study analyses the benefits and limitations of applying XAI in healthcare decision-making processes through an exhaustive analysis of current literature and empirical data. It investigates how XAI might increase AI algorithm transparency, allowing healthcare practitioners to better comprehend the reasoning behind AI-generated suggestions or forecasts. Furthermore, it investigates how XAI might help to enhance trust among healthcare professionals, patients, and other stakeholders, leading to better informed and collaborative decision-making processes. The study also tackles possible barriers to XAI deployment in healthcare. The complexity of AI algorithms, the interpretability of XAI explanations, and the integration of XAI systems into conventional healthcare procedures are among the hurdles. Furthermore, ethical aspects like as privacy, security, and bias mitigation are studied to guarantee that XAI is used responsibly in healthcare decision-making. The outcomes of this study lead to a better understanding of the influence of XAI on healthcare decision-making. This research seeks to give insights for policymakers, healthcare practitioners, and AI developers to support the responsible and successful integration of XAI into healthcare systems by shedding light on the benefits and issues connected with XAI. The ultimate objective is to use XAI to improve healthcare decision-making processes, improve patient outcomes, and allow the ethical and trustworthy deployment of AI in the healthcare sector.