AIMC Topic: Healthcare Disparities

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Use of artificial intelligence to address health disparities in low- and middle-income countries: a thematic analysis of ethical issues.

Public health
OBJECTIVES: Artificial intelligence (AI) is reshaping health and medicine, especially through its potential to address health disparities in low- and middle-income countries (LMICs). However, there are several issues associated with the use of AI tha...

Machine learning evaluation of inequities and disparities associated with nurse sensitive indicator safety events.

Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
PURPOSE: To use machine learning to examine health equity and clinical outcomes in patients who experienced a nurse sensitive indicator (NSI) event, defined as a fall, a hospital-acquired pressure injury (HAPI) or a hospital-acquired infection (HAI).

Challenges of artificial intelligence in medicine and dermatology.

Clinics in dermatology
Artificial intelligence (AI) in medicine and dermatology brings additional challenges related to bias, transparency, ethics, security, and inequality. Bias in AI algorithms can arise from biased training data or decision-making processes, leading to ...

Efficient adversarial debiasing with concept activation vector - Medical image case-studies.

Journal of biomedical informatics
BACKGROUND: A major hurdle for the real time deployment of the AI models is ensuring trustworthiness of these models for the unseen population. More often than not, these complex models are black boxes in which promising results are generated. Howeve...

Towards Precision Medicine in Spinal Surgery: Leveraging AI Technologies.

Annals of biomedical engineering
This critique explores the implications of integrating artificial intelligence (AI) technology, specifically OpenAI's advanced language model GPT-4 and its interface, ChatGPT, into the field of spinal surgery. It examines the potential effects of alg...

Implications of predicting race variables from medical images.

Science (New York, N.Y.)
AI-predicted race variables pose risks and opportunities for studying health disparities.