Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta …
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 …
Medical artificial intelligence for clinicians: the lost cognitive perspective
The development and commercialisation of medical decision systems based on artificial
intelligence (AI) far outpaces our understanding of their value for clinicians. Although …
intelligence (AI) far outpaces our understanding of their value for clinicians. Although …
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 …
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 …
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 …
Evaluation of AI solutions in health care organizations—the OPTICA tool
N Dagan, S Devons-Sberro, Z Paz, L Zoller, A Sommer… - NEJM AI, 2024 - ai.nejm.org
Regulatory bodies are struggling to determine effective ways to regulate artificial intelligence
(AI)-driven health care solutions, which repeatedly exhibit suboptimal performance and …
(AI)-driven health care solutions, which repeatedly exhibit suboptimal performance and …
An improved framework for detecting thyroid disease using filter-based feature selection and stacking ensemble
In recent years, machine learning (ML) has become a pivotal tool for predicting and
diagnosing thyroid disease. While many studies have explored the use of individual ML …
diagnosing thyroid disease. While many studies have explored the use of individual ML …
What's fair is… fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning …
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 …
A normative framework for artificial intelligence as a sociotechnical system in healthcare
Artificial intelligence (AI) tools are of great interest to healthcare organizations for their
potential to improve patient care, yet their translation into clinical settings remains …
potential to improve patient care, yet their translation into clinical settings remains …
Ethical considerations for artificial intelligence in medical imaging: data collection, development, and evaluation
The development of artificial intelligence (AI) within nuclear imaging involves several
ethically fraught components at different stages of the machine learning pipeline, including …
ethically fraught components at different stages of the machine learning pipeline, including …