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We are developing easy to understand awareness, education and inclusion programmes about the risks and rewards of Artificial Intelligence. We aim to bridge the gap between those making decisions on how AI is used and controlled, and the wider public whose lives are being shaped by it.

The intent of the study was to determine the influence of skin type and wavelength on light reflectance for pulse rate detection. 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 […]

The ability to measure physical activity through wrist-worn devices provides an opportunity for cardiovascular medicine. However, the accuracy of commercial devices is largely unknown. The aim of this work is to assess the accuracy of seven commercially available wrist-worn devices in estimating hea… This research paper assesses the accuracy of seven commercially available wrist-worn devices, […]

Study reveals rampant racism in decision-making software used by US hospitals — and highlights ways to correct it. An algorithm widely used in US hospitals to allocate health care to patients has been systematically discriminating against black people, a sweeping analysis has found. The study concluded that the algorithm was less likely to refer black […]

Show Analytics Exchange: Podcasts from SAS, Ep The Health Pulse: AI and Bias in Healthcare – Mar 5, 2021 Data scientist Hiwot Tesfaye joins Greg for a conversation about the use of algorithms in healthcare and how models can introduce bias. They’ll discuss current examples of health care bias, who should be held responsible and […]

AI is helping healthcare organisations determine care management programs and treatment plans – who gets what care – but these models and algorithms can be biased and introduce discrimination in the allocation or denial of care.

This guest panel series examines the use of AI in assisting healthcare, with a particular focus on automating tasks, communicating diagnoses and allocating resources. It examines the sources of bias in AI integrated systems and what we can do to eliminate it.

This study sheds more light on factors affecting perceived risks and proposes some recommendations on how to practically reduce these concerns. The findings of this study provide implications for research and practice in the area of AI-based CDS. Regulatory agencies, in cooperation with healthcare… This paper examines AI-based tools for healthcare from a consumer’s perspective. […]

By Adriana Krasniansky Interest in artificially intelligent (AI) health care has grown at an astounding pace: the global AI health care market is expected to reach $17.8 billion by 2025 and AI-powered systems are being designed to support medical activities ranging from patient diagnosis and triagin… AI-powered systems are being designed to support medical activities […]

Abstract. The COVID-19 pandemic is presenting a disproportionate impact on minorities in terms of infection rate, hospitalizations, and mortality. 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. […]

A new article in the Journal of the American Medical Informatics Association points to the dissemination of “under-developed and potentially biased models” in response to the novel coronavirus. This article draws on recent medical research which shows how potentially biased models informing our health care systems have impacted COVID-19. These biased models could exacerbate the […]

Earlier this week, Google announced the arrival of a new AI app to help diagnose skin conditions. It plans to launch it in Europe later this year. This article discusses mobile apps that aid the self-diagnosis of skin conditions. The apps do intend to be inclusive of all skin types, however, the training data was […]