Heather Cole-Lewis

Promoting Problem-Solving Among Low-Income Adults With Type 2 Diabetes: Cluster-Randomized Controlled Trial of a Mobile Health Intervention With SMS Text Messaging (Mobile Diabetes Detective)

Lena Mamykina, Arlene Smaldone, Suzanne R. Bakken, Heather Cole-Lewis et autres

BACKGROUND: Problem-solving is essential for the self-management of type 2 diabetes but remains challenging for underserved individuals. Although mobile health (mHealth) interventions can improve diabetes self-management, few focus on problem-solving. OBJECTIVE: This study evaluates the efficacy of Mobile Diabetes Detective (MoDD), a …

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Bridging the divide in digital therapeutics (DTx): Partnership strategies for broader representation across DTx development and deployment

Meelim Kim, Steven De La Torre, Uchechi A. Mitchell, Blanca Meléndrez et autres

While Digital Therapeutics (DTx) are widely considered a key strategy to reach certain populations with unmet healthcare needs, a range of differences in the impact and adoption of DTx still exists. These differences are not just rooted in access, but also in …

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Bridging the Divide in Digital Therapeutics (DTx): Partnership Strategies for Broader Representation Across DTx Development and Deployment

Meelim Kim, Steven De La Torre, Uchechi A. Mitchell, Blanca Meléndrez et autres

While Digital Therapeutics (DTx) are widely considered a key strategy to reach certain populations with unmet healthcare needs, a range of differences in the impact and adoption of DTx still exists. These differences are not just rooted in access, but also in …

Promoting Problem-Solving Among Low-Income Adults With Type 2 Diabetes: Cluster-Randomized Controlled Trial of a Mobile Health Intervention With SMS Text Messaging (Mobile Diabetes Detective) (Preprint)

Lena Mamykina, Arlene Smaldone, Suzanne Bakken, Heather Cole-Lewis et autres

BACKGROUND Problem-solving is essential for the self-management of type 2 diabetes but remains challenging for underserved individuals. Although mobile health (mHealth) interventions can improve diabetes self-management, few focus on problem-solving. OBJECTIVE This study evaluates the efficacy of Mobile Diabetes Detective (MoDD), a …

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Centering Equity in Health AI: LLM Design Strategies for Impactful Solutions (Preprint)

Akeiylah Dewitt, Andrea G. Parker, Christina Harrington, Heather Cole-Lewis

UNSTRUCTURED This article addresses the growing need to incorporate health equity considerations into the design and development of large language models (LLMs). While existing guidelines for artificial intelligence (AI) development often focus on ethical and technical aspects, they frequently overlook health equity, …

Toward expert-level medical question answering with large language models

K. K. Singhal, Tao Tu, Juraj Gottweis, Rory Sayres et autres

Large language models (LLMs) have shown promise in medical question answering, with Med-PaLM being the first to exceed a 'passing' score in United States Medical Licensing Examination style questions. However, challenges remain in long-form medical question answering and handling real-world workflows. Here, …

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Tackling Algorithmic Bias and Promoting Transparency in Health Datasets: The STANDING Together Consensus Recommendations

Joseph Alderman, Joanne Palmer, Elinor Laws, Melissa D. McCradden et autres

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Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations

Joseph Alderman, Joanne Palmer, Elinor Laws, Melissa D. McCradden et autres

Without careful dissection of the ways in which biases can be encoded into artificial intelligence (AI) health technologies, there is a risk of perpetuating existing health inequalities at scale. One major source of bias is the data that underpins such technologies. The …

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Safety principles for medical summarization using generative AI

Dillon Obika, Christopher Kelly, Ni Ding, Chris Farrance et autres

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A toolbox for surfacing health equity harms and biases in large language models

Stephen Pfohl, Heather Cole-Lewis, Rory Sayres, Darlene Neal et autres

Large language models (LLMs) hold promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities. Reliably evaluating equity-related model failures is a critical step toward developing systems that promote health equity. We present …

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Interdisciplinary Expertise to Advance Equitable Explainable AI

Chloe R. Bennett, Heather Cole-Lewis, Stephanie Farquhar, Naama Haamel et autres

The field of artificial intelligence (AI) is rapidly influencing health and healthcare, but bias and poor performance persists for populations who face widespread structural oppression. Previous work has clearly outlined the need for more rigorous attention to data representativeness and model performance …

Using generative AI to investigate medical imagery models and datasets

Oran Lang, Doron Stupp, Ilana Traynis, Heather Cole-Lewis et autres

BACKGROUND: AI models have shown promise in performing many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust of doctors in AI-based models, especially in …

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