There is a dire need to implement the policies in order to regulate artificial intelligence (AI) and machine learning (ML) across every sector of the health care industry. It was highlighted by Brian Scarpelli (the senior global policy counsel for the Connected Health Initiative) and Sebastian Holst (principal with Qi-fense, a consulting group that works in AI and ML) during their presentation titled “A modest proposal for AI regulation in healthcare,” held during the HIMSS22 Global Health Conference in Orlando.
According Scarpelli and Holst, “ML properties do more than challenge domain-specific applications of technology. Many of these properties will force an evaluation and retooling of core manufacturing, quality, and risk frameworks that have effectively served as the foundation of today’s industry-specific regulations and policies.”
AI Needs to Regulate in Health Care Industry
They further highlighted that AI has a great potential to revolutionize health care in all facets. Plus, it can potentially reduce administrative burdens for providers and payer and allow for resources to be deployed within a health system to serve vulnerable patient populations.
AI has the power to manage public health emergencies like the COVID-19 pandemic as well as can improve both preventive care and diagnostic efficiency.
AI Needs to Regulate:
As AI is becoming frequent buzz phrase in today’s fast-faced world, so there is a need to address the potential legal and ethical challenges. According to Tania M. Martin-Mercado, MS, MPH, a clinical researcher who presented on “How implicit bias affects AI in healthcare, ”One of the major themes of the HIMSS22 conference has been the challenge of achieving health equity and eliminating implicit bias. That’s one of the major challenges of AI as well since AI solutions can be biased. Many sessions focused on how diverse teams are needed when creating AI solutions to ensure that the programs don’t carry the same biases as society, which could exacerbate current social problems.” During the presentation, she pointed to an example of “an online tool that estimates risk of breast cancer calculates a lower risk for Black or Latinx women than White even when every other risk factor is identical.”
Scarpelli highlighted the vision for successful AI should follow four principals such as to enhance access to health care, empower patients to manage their own health, strengthen the relationship patients have with their health care teams and reduce administrative and cognitive burdens for both physicians and patients.
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