A review of the role of artificial intelligence in healthcare

A Al Kuwaiti, K Nazer, A Al-Reedy, S Al-Shehri… - Journal of personalized …, 2023 - mdpi.com
Artificial intelligence (AI) applications have transformed healthcare. This study is based on a
general literature review uncovering the role of AI in healthcare and focuses on the following …

[HTML][HTML] Evaluation and mitigation of racial bias in clinical machine learning models: sco** review

J Huang, G Galal, M Etemadi… - JMIR Medical …, 2022 - medinform.jmir.org
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 …

[HTML][HTML] AI recognition of patient race in medical imaging: a modelling study

JW Gichoya, I Banerjee, AR Bhimireddy… - The Lancet Digital …, 2022 - thelancet.com
Background Previous studies in medical imaging have shown disparate abilities of artificial
intelligence (AI) to detect a person's race, yet there is no known correlation for race on …

AI pitfalls and what not to do: mitigating bias in AI

JW Gichoya, K Thomas, LA Celi… - The British Journal of …, 2023 - academic.oup.com
Various forms of artificial intelligence (AI) applications are being deployed and used in many
healthcare systems. As the use of these applications increases, we are learning the failures …

Guiding principles to address the impact of algorithm bias on racial and ethnic disparities in health and health care

MH Chin, N Afsar-Manesh, AS Bierman… - JAMA network …, 2023 - jamanetwork.com
Importance Health care algorithms are used for diagnosis, treatment, prognosis, risk
stratification, and allocation of resources. Bias in the development and use of algorithms can …

Checklist for evaluation of image-based artificial intelligence reports in dermatology: CLEAR derm consensus guidelines from the international skin imaging …

R Daneshjou, C Barata, B Betz-Stablein… - JAMA …, 2022 - jamanetwork.com
Importance The use of artificial intelligence (AI) is accelerating in all aspects of medicine and
has the potential to transform clinical care and dermatology workflows. However, to develop …

TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

GS Collins, KGM Moons, P Dhiman, RD Riley… - bmj, 2024 - bmj.com
The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual
Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting …

Reading race: AI recognises patient's racial identity in medical images

I Banerjee, AR Bhimireddy, JL Burns, LA Celi… - arxiv preprint arxiv …, 2021 - arxiv.org
Background: In medical imaging, prior studies have demonstrated disparate AI performance
by race, yet there is no known correlation for race on medical imaging that would be obvious …

Using artificial intelligence-enhanced sensing and wearable technology in sports medicine and performance optimisation

S Chidambaram, Y Maheswaran, K Patel… - Sensors, 2022 - mdpi.com
Wearable technologies are small electronic and mobile devices with wireless
communication capabilities that can be worn on the body as a part of devices, accessories …

AI fairness in data management and analytics: A review on challenges, methodologies and applications

P Chen, L Wu, L Wang - Applied Sciences, 2023 - mdpi.com
This article provides a comprehensive overview of the fairness issues in artificial intelligence
(AI) systems, delving into its background, definition, and development process. The article …