Generative AI and the impact on health equity rapidly ageing societies such as Japan and China, countries with vast rural populations and localities where healthcare infrastructure is not mature. POTENTIAL GENERATIVE AI APPLICATIONS AI Revolution in Medicine by Peter Lee, Carey Goldberg and Isaac Kohane is a stimulating primer on GPT-4’s aptitude in health care. The authors identified numerous low-hanging fruit applications of generative AI in current healthcare delivery systems (though mostly using the US as an example) and biomedical R&D processes. Examples include the holistic analysis of a patient’s existing medical records; a virtual assistant to doctors that can provide medical references and analyses instantaneously; the automation of administrative tasks in a complicated payment environment; knowledge
By Vanessa Huang, general partner, and Dr Zhi Yang, chair, BVCF global effort, they could help catalyse health equity in the world’s hardest-to-reach places M any people globally have been fascinated by Generative AI-based tools could hold the key to better health care, lower costs and improved health outcomes – and with a coordinated the possibility of generative AI. Large language models (LLM), like GPT-4, hold the promise to increase efficiency, precision and productivity across virtually all segments of society. Like many, we explored the capability of GPT-4 and mused on its feasibility in healthcare delivery in emerging markets and the potential impact on health equity. The challenges are instantly clear: the disparities in internet access within and across countries, varied levels of computer literacy among different generations and populations, and the nascent state of regulations that can facilitate AI’s deployment in high-need areas but also safeguard data privacy, information integrity, fairness and accountability. The most immediate and practical applications for generative AI also appear to be in exactly the communities where many of these challenges are discernible:
support for therapeutics and medical devices R&D processes; ready access to medical information in an understandable language for the general public; portable personal health data and chronic disease monitoring for patients; and mental health support for isolated elderly populations and resource-constrained communities. work-in-progress phase, and a formally healthcare- trained LLM is not yet widely available. The logical expectation is that in the (near) future, there can be LLMs trained with local patient medical records, local oriented medical research papers, languages and cultural aspects. These localized health LLMs can then be the engine for scientists, researchers HEALTHCARE-TRAINED LLMS Generative AI as a technology is still in a and medical professionals to more effectively and accurately identify local disease trends and
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Health: A Political Choice – From Fragmentation to Integration
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