Influence of Skin Type and Wavelength on Light Wave Reflectance
PPG sensors detecting changes in blood flow are assessed for effectiveness on dark and light skin tones. Studies have shown that green light lacks precision and accuracy, and may not read at all when measuring heart rate in darker skin types.
Accuracy in Wrist-Worn, Sensor-Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort
This research paper assesses the accuracy of seven commercially available wrist-worn devices, which measure an individual’s heart rate and energy expenditure. The study shows, among other biases, that device error was higher for those with darker skin tones.
Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives
This paper examines AI-based tools for healthcare from a consumer’s perspective. AI will be integral to our healthcare systems and will be integrated across several aspects of clinical care, with particular focus on care planning, this paper examines patients’ perspectives on the integration of AI into the healthcare system.
Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19
In terms of infection rate, hospitalisation and mortality, the Covid-19 pandemic presents a disproportionate impact on minorities. Many believe that artificial intelligence could be a solution to guide clinical decision making to overcome this novel disease. However, the rapid dissemination of underdeveloped, and potentially biased models could exacerbate the disparities gap by making race affect […]
Addressing Bias: Artificial Intelligence in Cardiovascular Medicine
Medical paper which examines the potential of Artificial Intelligence in cardiovascular medicine; it could hugely benefit patient diagnosis and treatment of what is the leading cause of morbidity and mortality worldwide. However, AI algorithms are still subject to their own biases, and predictive models might worsen health disparities through biases in the data training the […]
Assessing and Mitigating Bias in Medical Artificial Intelligence
Deep learning algorithms using data from homogeneous populations may be difficult to generalise, and potentially exacerbate racial disparities in health and health care. This research paper explores (1) whether the accuracy of a deep learning algorithm varies depending on race / ethnicity, and (2) whether this performance is down to racial variation or external health […]
The Need for Use of Race Correction in Clinical Algorithms
Physicians still lack consensus on the meaning of race within medical science; there is an ongoing debate as to whether racial and ethnic categories can reflect underlying population genetics, and be clinically useful. Despite ongoing debates, this belief has become embedded within medical practice. This research paper examines how the algorithms used by clinicians to […]
Addressing Bias: Artificial Intelligence in Cardiovascular Medicine
Medical report which examines the potential of Artificial Intelligence in cardiovascular medicine; it could hugely benefit patient diagnosis and treatment of what is the leading cause of morbidity and mortality worldwide. However, AI algorithms are still subject to their own biases, and predictive models might worsen health disparities through biases in the data training the […]
Intelligence Unleashed – An argument for education
This research paper gives arguments for how AI can benefit our education system. It argues that AI can support teachers in giving children the best education whilst not taking away from the humanity of it. AI can be beneficial in aspects such as online tutoring, collaborative learning, and tackling achievement gaps. While it does not […]
Black Loans Matter: fighting bias for AI fairness in lending
A detailed summary of research by Mark Weber, Mikhail Yurochkin, Sherif Botros and Vanio Markov, breaking down the lack of racial justice in the current US financial system which leads to the loss of the right to financial security, along with examination of solutions.
