[HTML][HTML] Algorithmic encoding of protected characteristics in chest X-ray disease detection models
Background It has been rightfully emphasized that the use of AI for clinical decision making
could amplify health disparities. An algorithm may encode protected characteristics, and …
could amplify health disparities. An algorithm may encode protected characteristics, and …
[HTML][HTML] The impact of artificial intelligence on health equity in oncology: scoping review
Background The field of oncology is at the forefront of advances in artificial intelligence (AI)
in health care, providing an opportunity to examine the early integration of these …
in health care, providing an opportunity to examine the early integration of these …
Risk of bias in chest radiography deep learning foundation models
Purpose To analyze a recently published chest radiography foundation model for the
presence of biases that could lead to subgroup performance disparities across biologic sex …
presence of biases that could lead to subgroup performance disparities across biologic sex …
[HTML][HTML] Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review …
Background Machine learning and deep learning models have been increasingly used to
predict long-term disease progression in patients with chronic obstructive pulmonary …
predict long-term disease progression in patients with chronic obstructive pulmonary …
[HTML][HTML] Development of a novel dementia risk prediction model in the general population: A large, longitudinal, population-based machine-learning study
Background The existing dementia risk models are limited to known risk factors and
traditional statistical methods. We aimed to employ machine learning (ML) to develop a …
traditional statistical methods. We aimed to employ machine learning (ML) to develop a …
What's fair is… fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning …
M Mccradden, O Odusi, S Joshi, I Akrout… - Proceedings of the …, 2023 - dl.acm.org
The problem of algorithmic bias represents an ethical threat to the fair treatment of patients
when their care involves machine learning (ML) models informing clinical decision-making …
when their care involves machine learning (ML) models informing clinical decision-making …
Academic machine learning researchers' ethical perspectives on algorithm development for health care: a qualitative study
Objectives We set out to describe academic machine learning (ML) researchers' ethical
considerations regarding the development of ML tools intended for use in clinical care …
considerations regarding the development of ML tools intended for use in clinical care …
Expanding the role of justice in secondary research using digital psychological data.
J Herington, K Li, AR Pisani - American Psychologist, 2024 - psycnet.apa.org
Secondary analysis of digital psychological data (DPD) is an increasingly popular method
for behavioral health research. Under current practices, secondary research does not …
for behavioral health research. Under current practices, secondary research does not …
[HTML][HTML] The silent trial-the bridge between bench-to-bedside clinical AI applications
As more artificial intelligence (AI) applications are integrated into healthcare, there is an
urgent need for standardization and quality-control measures to ensure a safe and …
urgent need for standardization and quality-control measures to ensure a safe and …
Risk of bias in chest x-ray foundation models
Foundation models are considered a breakthrough in all applications of AI, promising robust
and reusable mechanisms for feature extraction, alleviating the need for large amounts of …
and reusable mechanisms for feature extraction, alleviating the need for large amounts of …