Moscow Institute of Thermal Technology

Hurtful words

Haoran Zhang, Amy X. Lu, Mohamed Abdalla et al.

2020 · 119 Zit.

Retractions

Pierre Azoulay, Jeffrey L. Furman, Krieger et al.

2014 · 91 Zit.

Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings

Haoran Zhang, Amy X. Lu, Mohamed Abdalla et al.

2020 · 29 Zit.

Who should do as AI say? Only non-task expert physicians benefit from correct explainable AI advice

Susanne Gaube, Harini Suresh, Martina Raue et al.

2022 · 3 Zit.

Tissue Reassembly with Generative AI

Tingyang Yu, Chanakya Ekbote, N. V. Morozov et al.

2025 · 3 Zit.

Enabling AI innovation via data and model sharing: An overview of the NSF Convergence Accelerator Track D

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2022 · 2 Zit.

Research Priorities for Robust and Beneficial Artificial Intelligence

Stuart Russell, Daniel Dewey, Max Tegmark

2016 · 2 Zit.

Artificial Intelligence and Literature: A Dual-Edged Sword in Research and Education

Milica Denkovska, Anita Dimitrijovska Jankulovska

2025 · 0 Zit.

Implementing Generative AI in U.S. Hospital Systems

Ben Armstrong, Katherine C. Kellogg, Retsef Levi et al.

2024 · 0 Zit.

Investigating How Prompt Language Choice Affects the Security of ChatGPT-Generated Code

Yanan Guo, Ruiqiang Ma, Xu Cui et al.

2026 · 0 Zit.

Toward a conceptual framework for AI and robotics in aging in place: insights from constructivist grounded theory

Sheng-Hung Lee, Devin Liddell, Maria C. Yang et al.

2025 · 0 Zit.

Explainable AI as a Double-Edged Sword in Dermatology: The Impact on Clinicians versus The Public

Xuhai Xu, Haoyu Hu, Haoran Zhang et al.

2025 · 0 Zit.

Agent-Based Large Language Model System for Extracting Structured Data from Breast Cancer Synoptic Reports: A Dual-Validation Study

Steven N. Hart, Teya S. Bergamaschi

2025 · 0 Zit.