Data scarcity, or more specifically, the lack of usable and high-quality data (that is, data that has been cleaned, processed, is of high quality in accuracy and relevance, and formatted, in each case to a widely and commonly accepted standard) is an endemic problem in relation to various industries and the healthcare and pharmaceutical industries are no exception. An article published by the World Economic Forum (WEF) identified that although the healthcare sector generates a 'staggering amount of data,' a whopping '97% of it goes unused.'

In the ideal world, we would see the availability of centralized digital repositories with complete, up-to-date, fully usable health information available to the community in an accountable and secure framework.

The lack of such a framework can lead to undesirable outcomes. One US commentator argued for a national health information exchange to deal with this issue, one that formally establishes required standards for creating, maintaining, and sharing these records, and one that creates the infrastructure and capability to gather, integrate, store, and use health records, arguing that the COVID-19 pandemic threw a spotlight on the dangers arising from the lack of such access.

Other commentators argue that data scarcity or 'health data poverty' from certain communities could result in unrepresentative datasets that produce poor quality and ineffective artificial intelligence (AI) developed and machine-learning medical solutions - essentially creating a digital divide where some of the best advances in medical innovation leave out (and leave behind) such communities. Indeed, persistent data scarcity in medical machine learning has led some authors to propose solutions such as reliance on synthetic data, but even such solutions need to ensure realism and avoid harmful biases.

The article by the WEF identified three themes of concern to address this: 'the sheer volume of data, data privacy concerns, and the need for interoperability.'

In this Insight article, Jeffrey Lim and Frederick Tay, from Joyce A. Tan & Partners LLC, look at this issue from a data privacy perspective and discuss ongoing work on the Health Information Bill to develop a potential national health data repository in Singapore. This Bill has the potential to provide the framework for the delivery of a high-quality, usable healthcare data exchange - to ask how one might see such legislation establish more than a national repository and database to service patients, but also build a sound, secure, and accountable framework for conducting data collection, use, and processing in the name of innovation, improving healthcare, pharmaceutical solutions, and the medical outcomes of therapies and treatments.

Along with the growth and increasing prevalence of artificial intelligence (AI), including generative AI, the privacy and ethical risks brought along by the new technology cannot be understated. Ada Chung Lai-Ling, Privacy Commissioner for Personal Data (PCPD), Hong Kong, China, looks at what steps organizations can take to ensure compliance and the guidance offered by the PCPD to help this.

In this Insight article, Vick Chien, Ken-Ying Tseng, and Evelyn Shih, from Lee and Li, Attorneys-at-Law, introduce Taiwan's progressive steps towards artificial intelligence (AI) governance. With the 2024 Draft Artificial Intelligence Basic Act (the Draft Act), Taiwan addresses legal challenges while promoting sustainable development, transparency, and innovation in AI technologies.

In this Insight article, Kritiyanee Buranatrevedhya and Phatrajarin Tanjaturon, from Baker & McKenzie Limited Attorneys at Law, introduce Thailand's National Science and Technology Development Agency (NSTDA) Ethical Guidelines for artificial intelligence (the Guideline), announced March 2022, a framework designed to promote responsible and ethical artificial intelligence (AI) practices.

A year in law and regulation of artificial intelligence (AI) is an eon. Whilst a robust regulatory framework or approach should be consistent, grounded in principles, and predictable, it must also evolve to address changing attitudes and perceptions. And whilst no one wants to kill the goose that lays the golden egg by diving headfirst into innovation-curbing regulation, the shift towards favoring some kind of regulation is noticeably gaining speed, with growing fears of social, economic, and political ills.

Now, with the EU successfully rolling out its extensive AI regulatory framework in the EU Artificial Intelligence Act (the EU AI Act), one question is whether other countries will follow suit to enact their own compatible or comparable 'hard law' in a classic demonstration of the 'Brussels effect.'

In his Insight article last year, Jeffrey Lim, Director at Joyce A. Tan & Partners LLC, described how Singapore has been a prime example of a jurisdiction that has positioned itself as a paragon of a regulation-lite, community-fostered, and inclusive multi-stakeholder jurisdiction. With its voluntary Model Governance Framework now in its second version and encompassing generative AI (genAI Model Framework), in this Insight article, Jeffrey explains how Singapore appears set to continue in this vein - an approach that has already yielded significant private sector in-country investment in AI capability building.