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Reforms: Consensus-based recommendations for machine-learning-based science
Machine learning (ML) methods are proliferating in scientific research. However, the
adoption of these methods has been accompanied by failures of validity, reproducibility, and …
adoption of these methods has been accompanied by failures of validity, reproducibility, and …
[HTML][HTML] Evaluation and mitigation of racial bias in clinical machine learning models: sco** review
Background Racial bias is a key concern regarding the development, validation, and
implementation of machine learning (ML) models in clinical settings. Despite the potential of …
implementation of machine learning (ML) models in clinical settings. Despite the potential of …
MINIMAR (MINimum Information for Medical AI Reporting): Develo** reporting standards for artificial intelligence in health care
The rise of digital data and computing power have contributed to significant advancements
in artificial intelligence (AI), leading to the use of classification and prediction models in …
in artificial intelligence (AI), leading to the use of classification and prediction models in …
Digital twins for predictive oncology will be a paradigm shift for precision cancer care
To the Editor—In medicine, digital twin models use real-time data to adjust treatment,
monitor response and track lifestyle modifications. Similarly, cancer patient digital twins …
monitor response and track lifestyle modifications. Similarly, cancer patient digital twins …
Biomedical ethical aspects towards the implementation of artificial intelligence in medical education
The increasing use of artificial intelligence (AI) in medicine is associated with new ethical
challenges and responsibilities. However, special considerations and concerns should be …
challenges and responsibilities. However, special considerations and concerns should be …
The AI life cycle: a holistic approach to creating ethical AI for health decisions
MY Ng, S Kapur, KD Blizinsky… - Nature medicine, 2022 - nature.com
The AI life cycle: a holistic approach to creating ethical AI for health decisions | Nature Medicine
Skip to main content Thank you for visiting nature.com. You are using a browser version with …
Skip to main content Thank you for visiting nature.com. You are using a browser version with …
Reforms: Reporting standards for machine learning based science
Machine learning (ML) methods are proliferating in scientific research. However, the
adoption of these methods has been accompanied by failures of validity, reproducibility, and …
adoption of these methods has been accompanied by failures of validity, reproducibility, and …
Assessment of adherence to reporting guidelines by commonly used clinical prediction models from a single vendor: a systematic review
Importance Various model reporting guidelines have been proposed to ensure clinical
prediction models are reliable and fair. However, no consensus exists about which model …
prediction models are reliable and fair. However, no consensus exists about which model …
Promoting Equity In Clinical Decision Making: Dismantling Race-Based Medicine: Commentary examines promoting equity in clinical decision-making
As the use of artificial intelligence has spread rapidly throughout the US health care system,
concerns have been raised about racial and ethnic biases built into the algorithms that often …
concerns have been raised about racial and ethnic biases built into the algorithms that often …
Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare
High-quality research is essential in guiding evidence-based care, and should be reported
in a way that is reproducible, transparent and where appropriate, provide sufficient detail for …
in a way that is reproducible, transparent and where appropriate, provide sufficient detail for …