How AI is used and applied in healthcare settings presents tremendous potential and significant risk. To explore the topic further, Apolitical convened some of the most eager minds in national health agencies, regulatory agencies, the medical sector, industry and academia to discuss how AI is being used in the health sector around the world — and how governments are regulating AI’s application to health.

The events were convened with the support of Health Canada and included participants from 12 different countries, including New Zealand, India, South Africa and the United States.

These rich discussions drew attention to key areas public servants should pay attention to as AI makes its way into the healthcare space. Here are our top 5 takeaways:

  1. **AI is already being applied in innovative ways across the healthcare sector **

    Those who participated in the events shared how AI is already used in two domains. Natural Language Processing (NLP) — a subset of AI — can streamline analysis of text-dense processes, including helping doctors with consultation feedback, supporting emergency departments in triaging resources and providing people with easily accessible health guidance on stigmatised topics (e.g. sexual health) through private messaging. More generally, AI is being used in the analysis of medical imaging for early disease detection in radiology, cardiology and dermatology and is helping predict cancer progression from precancerous lesions. We’re excited to see what other innovative applications of AI appear next. If you have any you’d like to share, be sure to tell us in the comments section below.

  2. The regulatory dynamics are complex, requiring in-depth knowledge and collaboration

    The discussions underscored the need for regulation to keep up with AI advancements, acknowledging that this rapid pace poses a challenge for governments worldwide. Regulators should model AI literacy, demonstrating their understanding of the statistical and AI models themselves. Simultaneously, it is challenging to make good decisions about AI use when there is such a complex web of bodies and organisations releasing their own regulations. Until all these regulatory frameworks are harmonised, a lack of alignment will inhibit innovation in the healthcare space.

    Are you finding it hard to keep track of AI regulation? Could streamlining it help you make an informed decision about how to use AI?

  3. **Enthusiasm for AI is tempered by concern over possible risks **

    AI is built on mass data, raising questions about ownership. What is a communal versus an individual asset? Can individual patient data be kept private? Are there benefits to health data being made communal? These are some of the key questions posed during the event discussion.

    Event attendees also expressed their concern over AI potentially deepening inequality, expanding digital divides, for example. There is already bias in the healthcare setting. Will AI further cement this bias?

  4. **The future of AI use in healthcare hinges on interoperability **

    Effective AI systems rely on quality data. Without it, AI won’t be able to help doctors make informed decisions about people’s lives. Strides are being made, but improving data interoperability and portability is essential as AI is further introduced into healthcare settings.

  5. **AI has the potential to save lives **

    Introducing AI into the healthcare space is, of course, risky. But it also has life-saving potential, and event attendees expressed their excitement for what it might be able to do for vulnerable and underserved populations. AI might be able to create efficiencies which ultimately bring down the cost of healthcare.

What other opportunities are there for AI use in healthcare? Are there other risks which you think should be on governments’ radars, especially as they step up their regulatory game? What resources have you come across which have helped you think about AI and health? Let us know in the comments section below.

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