Equity has to be built into the deployment strategy from day one, not treated as a future retrofit. That means prioritizing inclusion not only in the data but in the delivery, and recognizing that inclusive deployment is the foundation for inclusive datasets.
At MedCity News’ INVEST Digital Health conference, healthcare experts explored strategies to mitigate automation bias — emphasizing the importance of vendor responsibility, use case-specific governance and clinician engagement.
Sheila Bond, MD, talked about the latest trends regarding integration of AI in healthcare.
AI should augment, not replace, human provider expertise. Explainability empowers healthcare professionals to work in tandem with AI, ensuring that patients receive the most accurate and informed care.
Navigating the regulatory and ethical requirements of different medical data providers across many different countries, as well as safeguarding patient privacy, is a mammoth task that requires extra resources and expertise.
Last week, HHS finalized a new rule that requires healthcare AI developers to provide more data about their products to customers, which could aid providers in determining AI tools’ risks and effectiveness. Some AI leaders believe the new guardrails are a step in the right direction, and others are skeptical about whether the new rules are necessary or will be effective.
To harness the best parts of AI — and avoid its worst possible outcomes — you need an AI strategy that not only includes technical implementation tactics but also prioritizes efforts to minimize bias, address ethical considerations, and build consumer trust and confidence.
Dickon Waterfield discusses why Lantern's navigation works.
A new study led by Stanford researchers tested four commercially available LLMs and found that they all could potentially cause harm by breeding inaccurate, racist information. For example, all models tried to justify race-based medicine when asked questions about calculating patients’ kidney function and lung capacity — two areas where race-based medicine practices used to be common but have since been scientifically refuted.
At HLTH, Mayo Clinic Platform President John Halamka gave a window into how his health system is mitigating generative AI risks. Some of the measures Mayo is taking include running analyses on how well algorithms perform across various subgroups and training models only on internal de-identified data.
By making large amounts of diverse data widely available, healthcare institutions can feel confident about the evaluation, creation, and validation of algorithms as they're transitioned from ideation to use.
Research conducted by the MIT Media Lab and Scale AI found that lighter skin tones vastly outnumber darker skin tones in the two of the most widely used dermatology atlases. This led to disparities in an AI model’s ability to diagnose skin conditions involving darker skin tones.
How Zelis hopes to solve the healthcare financial system for payers and providers.
A new partnership between Nvidia and the American College of Radiology will give a wider range of clinicians the ability to build, share and adapt their own AI models.