Puja Myles

Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness

2020 · 464 Zit. · BMJ

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

2024 · 99 Zit. · The Lancet Digital Health

Machine learning and AI research for Patient Benefit: 20 Critical\n Questions on Transparency, Replicability, Ethics and Effectiveness

2018 · 17 Zit. · arXiv (Cornell University)

Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness

2018 · 17 Zit. · arXiv (Cornell University)

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

2024 · 13 Zit. · NEJM AI

Data quality, provenance and transparency in real-world data : Aligning quality standards with data governance legal frameworks

2025 · 1 Zit. · Journal of data protection & privacy.

Probabilistic Versus Deep Generative Models: A Fairness Centred Evaluation of Synthetic Healthcare Tabular Data

2026 · 0 Zit. · International Journal of Computational Intelligence Systems

Synthetic data for development of AI as a medical device (AIaMDs): Regulatory considerations

2025 · 0 Zit.

Integrating Explainable AI in Medical Devices: Technical, Clinical and Regulatory Insights and Recommendations

2025 · 0 Zit. · ArXiv.org