TY - JOUR
T1 - Ethical, legal, and regulatory landscape of artificial intelligence in Australian healthcare and ethical integration in radiography
T2 - A narrative review
AU - Chau, Minh
PY - 2024/12
Y1 - 2024/12
N2 - This narrative review explores the ethical, legal, and regulatory landscape of AI integration in Australian healthcare, focusing on radiography. It examines the current legislative framework, assesses the trust and reliability of AI tools, and proposes future directions for ethical AI integration in radiography. AI systems significantly enhance diagnostic radiography by improving diagnostic accuracy and efficiency in stroke detection, brain imaging, and chest reporting. However, AI raises substantial ethical concerns due to its 'black-box' nature and potential biases in training data. The Therapeutic Goods Administration's reforms in Australia, though comprehensive, fall short of fully addressing issues related to the trustworthiness and legal liabilities of AI tools. Adopting a comprehensive research strategy that includes doctrinal, comparative, and public policy analyses will facilitate an understanding of international practices, particularly from countries with similar legal systems, and help guide Australia in refining its regulatory framework. For an ethical future in radiography, a robust, multi-disciplinary approach is required to prioritize patient safety, data privacy, and equitable AI use. A framework that balances technological innovation with ethical and legal integrity is essential for advancing healthcare while preserving trust and transparency. Healthcare professionals, policymakers, and AI developers must collaborate to establish a resilient, equitable, and transparent healthcare system. Future research should focus on multi-disciplinary methodologies, combining doctrinal, comparative, and public policy research to provide comprehensive insights. This approach will guide Australia in creating a more inclusive and ethically sound legal framework for AI in healthcare, ensuring its ethical and beneficial integration into radiography.
AB - This narrative review explores the ethical, legal, and regulatory landscape of AI integration in Australian healthcare, focusing on radiography. It examines the current legislative framework, assesses the trust and reliability of AI tools, and proposes future directions for ethical AI integration in radiography. AI systems significantly enhance diagnostic radiography by improving diagnostic accuracy and efficiency in stroke detection, brain imaging, and chest reporting. However, AI raises substantial ethical concerns due to its 'black-box' nature and potential biases in training data. The Therapeutic Goods Administration's reforms in Australia, though comprehensive, fall short of fully addressing issues related to the trustworthiness and legal liabilities of AI tools. Adopting a comprehensive research strategy that includes doctrinal, comparative, and public policy analyses will facilitate an understanding of international practices, particularly from countries with similar legal systems, and help guide Australia in refining its regulatory framework. For an ethical future in radiography, a robust, multi-disciplinary approach is required to prioritize patient safety, data privacy, and equitable AI use. A framework that balances technological innovation with ethical and legal integrity is essential for advancing healthcare while preserving trust and transparency. Healthcare professionals, policymakers, and AI developers must collaborate to establish a resilient, equitable, and transparent healthcare system. Future research should focus on multi-disciplinary methodologies, combining doctrinal, comparative, and public policy research to provide comprehensive insights. This approach will guide Australia in creating a more inclusive and ethically sound legal framework for AI in healthcare, ensuring its ethical and beneficial integration into radiography.
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U2 - 10.1016/j.jmir.2024.101733
DO - 10.1016/j.jmir.2024.101733
M3 - Review article
C2 - 39111223
SN - 1939-8654
VL - 55
SP - 1
EP - 13
JO - Journal of Medical Imaging and Radiation Sciences
JF - Journal of Medical Imaging and Radiation Sciences
IS - 4
M1 - 101733
ER -