This page highlights international guidelines and resources.
Global AI Law and Policy Tracker
by
Artificial Intelligence in Healthcare: Applications, Risks, and Ethical and Societal Impacts
by
In recent years, the use of artificial intelligence (AI) in medicine and healthcare has been praised for the great promise it offers, but has also been at the centre of heated controversy. This study offers an overview of how AI can benefit future healthcare, in particular increasing the efficiency of clinicians, improving medical diagnosis and treatment, and optimising the allocation of human and technical resources. The report identifies and clarifies the main clinical, social and ethical risks posed by AI in healthcare, more specifically: potential errors and patient harm; risk of bias and increased health inequalities; lack of transparency and trust; and vulnerability to hacking and data privacy breaches. The study proposes mitigation measures and policy options to minimise these risks and maximise the benefits of medical AI, including multi-stakeholder engagement through the AI production lifetime, increased transparency and traceability, in-depth clinical validation of AI tools, and AI training and education for both clinicians and citizens.
Task Force Report on Artificial Intelligence and Emerging Digital Technologies
by
Artificial Intelligence in Health Professions Education: Proceedings of a Workshop
by
The National Academies Global Forum on Innovation in Health Professional Education hosted a multi-day workshop series in March and April 2023 to explore the potential of artificial intelligence (AI) in health professions education. Speakers at the workshops provided background on AI; discussed the social, cultural, policy, legal, and regulatory considerations to integrating AI into health care and training; considered the skills health professionals will need as educators and providers to effectively use AI in practice; and explored needs for educating the next generation of health workers. Speakers took consideration of the bias, burden, health equity concerns that introducing AI into clinical education would bring. This Proceedings of a Workshop summarizes the discussions held during the workshop.
AI in Health: Huge Potential, Huge Risks
by
Collective Action for Responsible AI in Health
by
Artificial intelligence will have profound impacts across health systems, transforming health care, public health, and research. Responsible AI can accelerate efforts toward health systems being more resilient, sustainable, equitable, and person-centred. This paper provides an overview of the background and current state of artificial intelligence in health, perspectives on opportunities, risks, and barriers to success. The paper proposes several areas to be explored for policy makers to advance the future of responsible AI in health that is adaptable to change, respects individuals, champions equity, and achieves better health outcomes for all. The areas to be explored relate to trust, capacity building, evaluation, and collaboration. This recognises that the primary forces that are needed to unlock the value from artificial intelligence are people-based and not technical. The OECD is ready to support efforts for co-operative learning and collective action to advance the use of responsible AI in health.
Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-modal Models
by
Artificial Intelligence (AI) refers to the capability of algorithms integrated into systems and tools to learn from data so that they can perform automated tasks without explicit programming of every step by a human. Generative AI is a category of AI techniques in which algorithms are trained on data sets that can be used to generate new content, such as text, images or video. This guidance addresses one type of generative AI, large multi-modal models (LMMs), which can accept one or more type of data input and generate diverse outputs that are not limited to the type of data fed into the algorithm. It has been predicted that LMMs will have wide use and application in health care, scientific research, public health and drug development. LMMs are also known as “general-purpose foundation models”, although it is not yet proven whether LMMs can accomplish a wide range of tasks and purposes.
Regulatory Considerations on Artificial Intelligence for Health
by
This publication is a general, high-level and nonexclusive overview of key regulatory considerations in topic areas developed by the WG-RC to support the overarching FG-AI4H framework. Recognizing that a single publication cannot address the specifics of the various AI systems that can be used for therapeutic development or health-care applications in general, the WG-RC’s overview will highlight some of the key regulatory principles and concepts – such as risk–benefit assessments and considerations for the evaluation and monitoring of the performance of AI systems developed using AI technologies. Throughout the process of developing this publication, the WG-RC took into consideration different stakeholder perspectives, as well as different global and regional settings. The WG-RC’s overview is not intended as guidance, as a regulatory framework or policy. Rather, it is meant as a listing of key regulatory considerations and a resource for all relevant stakeholders – including developers who are exploring and using AI technologies and developing AI systems, regulators who might be in the process of identifying approaches to manage and facilitate AI systems, manufacturers who design and develop AI systems that are embedded in medical devices, and health practitioners who deploy and use such medical devices and AI systems.
Quick links
About ANZCA
Connect with us
ANZCA
FPM
Copyright © Australian and New Zealand College of Anaesthetists.
Library
