Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of disparities in performance between subgroups. Since not all sources of biases in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess how those biases are encoded in models, and how capable bias mitigation methods are at ameliorating performance disparities. In this article, we introduce a novel analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models. We developed and tested this framework for conducting controlled in silico trials to assess bias in medical imaging AI using a tool for generating synthetic magnetic resonance images with known disease effects and sources of bias. The feasibility is showcased by using three counterfactual bias scenarios to measure the impact of simulated bias effects on a convolutional neural network (CNN) classifier and the efficacy of three bias mitigation strategies. The analysis revealed that the simulated biases resulted in expected subgroup performance disparities when the CNN was trained on the synthetic datasets. Moreover, reweighing was identified as the most successful bias mitigation strategy for this setup, and we demonstrated how explainable AI methods can aid in investigating the manifestation of bias in the model using this framework. Developing fair AI models is a considerable challenge given that many and often unknown sources of biases can be present in medical imaging datasets. In this work, we present a novel methodology to objectively study the impact of biases and mitigation strategies on deep learning pipelines, which can support the development of clinical AI that is robust and responsible.

CARPL.ai Joins Stanford Affiliates Program to Expand Deployment of Medical Imaging AI in the Clinical Realm Stanford, CA, June 16, 2022: CARPL.ai, a technology platform that connects Artificial Intelligence (AI) applications and healthcare providers announced their participation in Stanford University's prestigious Industry Affiliates Program through the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). The announcement came as part of Stanford AIMI Symposium 2022, one of the world's leading conferences on AI in medicine. The 3rd annual symposium was a hybrid event, held both in person at Stanford and live streamed for online attendees. Curt Langlotz, Professor of Radiology and Biomedical Informatics and Director of Stanford AIMI, said, "AIMI faculty have been working with CARPL for almost three years, including our most recent work on cryptographic inferencing for AI models. We are excited to formalize our relationship with CARPL.ai and look forward to our affiliation with them."

Feature | Artificial Intelligence | April 29, 2022 | By Sanjay Parekh, Ph.D. … One of the components of Signify Research’s Machine Learning in …

Healthcare providers and their patients stand to benefit dramatically from AI technologies, thanks to their ability to leverage data at scale to reveal new insights. But for AI developers to perform the research that will feed the next wave of breakthroughs, they first need the right data and the tools to use it. Powerful new techniques are now available to extract and utilize data from complex objects like medical imaging, but leaders must know where to invest their organizations' resources to fuel this transformation. When a layperson envisions creating an AI model, most of what they picture is concentrated in step four: feeding data into the system and analyzing it to arrive at a breakthrough. But experienced data scientists know the reality is much more mundane--80% of their time is spent on "data wrangling" tasks (the comparatively dull work of steps one, two, and three)--while only 20% is spent on analysis. Many facets of the healthcare industry have yet to adjust to the data demands of AI, particularly when dealing with medical imaging.

A tsunami of medical imaging procedures – all with higher image counts and resolutions to analyze – are drowning the limited number of radiologists available to interpret them. This makes their chosen healthcare profession increasingly difficult. This abundance of medical imaging data can be put to work to train today's efficient Convolutional Deep Neural Network models[1] running on the latest NVIDIA GPU processors to assist clinicians in their diagnostic tasks. And none too soon, because all that medical image data must be read by increasingly overwhelmed diagnostic clinicians. For the last decade, most of the focus in artificial intelligence (AI) has been on GPU processing, and rightfully so, with all the advancements going on there.

Medical imaging AI, which can help diagnose health problems doctors don't alway see, is only getting more sophisticated--and more lucrative. Just last month, Tel-Aviv-based Aidoc raised $65 million for it's AI-powered medical imaging platform and other local companies are attracting investors at a rapid clip. The software can find, and in some cases, diagnose polyps, tumors or anomalies that may otherwise go undetected by the human eye – a feat that has the potential to save lives. Beyond its most promising attributes, AI-driven technology could also dramatically decrease wait times at hospitals and doctors' offices by automating some of the most tedious work, allowing doctors to see and treat more patients. But critics of the unregulated technology say results can be inconsistent.