Medicine (Paper)

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COCIR analyses application of medical device legislation to Artificial Intelligence

“The European Commission has shown its ambition in the area of artificial intelligence (AI) in its recent White Paper on Artificial Intelligence – a European approach to excellence and trust. This White Paper is at the same time a precursor of possible legislation of AI in products and services in the European Union. However, COCIR sees no need for novel regulatory frameworks for AI-based devices in Healthcare, because the requirements of EU MDR and EU IVDR in combination with GDPR are adequate to ensure that same excellence and trust.” (COCIR paper).

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Diagnostic uncertainty calibration: towards reliable machine predictions in medical domain

We further formalize the metrics for higher-order statistics, including inter-rater disagreement, in a unified way, which enables us to assess the quality of distributional uncertainty. In addition, we propose a novel post-hoc calibration method that equips trained neural networks with calibrated distributions over class probability estimates. With a large-scale medical imaging application, we show that our approach significantly improves the quality of uncertainty estimates in multiple metrics.

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Machine learning in medicine: a practical introduction

Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data

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TechDispatch #1/2020: Contact Tracing with Mobile Applications

In public health, contact tracing is the process to identify individuals who have been in contact with infected persons. Proximity tracing with smartphone applications and sensors could support contact tracing. It involves processing of sensitive personal data.

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A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

This paper contributes the first human-centered observational study of a deep learning system deployed directly in clinical care with patients. Through field observations and interviews at eleven clinics across Thailand, we explored the expectations and realities that nurses encounter in bringing a deep learning model into their clinical practices. First, we outline typical eye-screening workflows and challenges that nurses experience when screening hundreds of patients. Then, we explore the expectations nurses have for an AI-assisted eye screening process. Next, we present a human-centered, observational study of the deep learning system used in clinical care, examining nurses’ experiences with the system, and the socio-environmental factors that impacted system performance. Finally, we conclude with a discussion around applications of HCI methods to the evaluation of deep learning algorithms in clinical environments.

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Artificial Intelligence and Machine Learning in Software as a Medical Device: discussion Paper and Request for Feedback

Artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care. The FDA is considering a total product lifecycle-based regulatory framework for these technologies.

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The need for a system view to regulate artificial intelligence/machine learning-based software as medical device

FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition