Katherine Heller

Do no harm: a roadmap for responsible machine learning for health care

2019 · 894 Zit. · Nature Medicine

The value of standards for health datasets in artificial intelligence-based applications

2023 · 235 Zit. · Nature Medicine

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

2019 · 201 Zit. · JMIR Medical Informatics

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

2024 · 85 Zit. · The Lancet Digital Health

Tackling bias in AI health datasets through the STANDING Together initiative

2022 · 77 Zit. · Nature Medicine

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

2024 · 63 Zit. · Nature Medicine

Author Correction: Do no harm: a roadmap for responsible machine learning for health care

2019 · 28 Zit. · Nature Medicine

A mixed-methods feasibility study of a novel AI-enabled, web-based, clinical decision support system for the treatment of major depression in adults

2023 · 13 Zit. · Journal of Affective Disorders Reports

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

2024 · 12 Zit. · NEJM AI

Evaluating the Usability and Impact of an Artificial Intelligence-Powered Clinical Decision Support System for Depression Treatment

2020 · 3 Zit. · Biological Psychiatry

Evaluating the Perceived Utility of an Artificial Intelligence-Powered Clinical Decision Support System for Depression Treatment Using a Simulation Centre

2021 · 2 Zit.

Nteasee: Understanding Needs in AI for Health in Africa - A Mixed-Methods Study of Expert and General Population Perspectives

2025 · 1 Zit.

Stop treating `AGI' as the north-star goal of AI research

2025 · 1 Zit. · ArXiv.org

Nteasee: Understanding Needs in AI for Health in Africa -- A Mixed-Methods Study of Expert and General Population Perspectives

2024 · 1 Zit. · arXiv (Cornell University)

Participatory Personalization in Classification

2023 · 0 Zit. · arXiv (Cornell University)