Sharecare is a digital health company that offers an artificial intelligence-powered mobile app for consumers. But it has a strong viewpoint on AI and how it is used. Sharecare believes that while other companies use augmented analytics and AI to understand data with business intelligence tools, they are missing out on the benefits of data fluency and federated AI. By using federated AI and data fluency, Sharecare says it digs deeper to find hidden similarities in the data that business intelligence tools would not be able to detect in health settings. To gain a deeper understanding of data fluency and federated AI, Healthcare IT News sat down with Akshay Sharma, executive vice president of artificial intelligence at Sharecare, for an in-depth interview. Q: What exactly is federated AI, and how is it different from any other form of AI? A: Federated AI, or federated learning, guarantees that the user's data stays on the device.
Torkzadehmahani, Reihaneh, Nasirigerdeh, Reza, Blumenthal, David B., Kacprowski, Tim, List, Markus, Matschinske, Julian, Späth, Julian, Wenke, Nina Kerstin, Bihari, Béla, Frisch, Tobias, Hartebrodt, Anne, Hausschild, Anne-Christin, Heider, Dominik, Holzinger, Andreas, Hötzendorfer, Walter, Kastelitz, Markus, Mayer, Rudolf, Nogales, Cristian, Pustozerova, Anastasia, Röttger, Richard, Schmidt, Harald H. H. W., Schwalber, Ameli, Tschohl, Christof, Wohner, Andrea, Baumbach, Jan
Artificial intelligence (AI) has been successfully applied in numerous scientific domains including biomedicine and healthcare. Here, it has led to several breakthroughs ranging from clinical decision support systems, image analysis to whole genome sequencing. However, training an AI model on sensitive data raises also concerns about the privacy of individual participants. Adversary AIs, for example, can abuse even summary statistics of a study to determine the presence or absence of an individual in a given dataset. This has resulted in increasing restrictions to access biomedical data, which in turn is detrimental for collaborative research and impedes scientific progress. Hence there has been an explosive growth in efforts to harness the power of AI for learning from sensitive data while protecting patients' privacy. This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy, and discusses their strengths, limitations, and open problems.
With the rapid development of computing technology, wearable devices such as smart phones and wristbands make it easy to get access to people's health information including activities, sleep, sports, etc. Smart healthcare achieves great success by training machine learning models on a large quantity of user data. However, there are two critical challenges. Firstly, user data often exists in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Secondly, the models trained on the cloud fail on personalization. In this paper, we propose FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds personalized models by transfer learning. It is able to achieve accurate and personalized healthcare without compromising privacy and security. Experiments demonstrate that FedHealth produces higher accuracy (5.3% improvement) for wearable activity recognition when compared to traditional methods. FedHealth is general and extensible and has the potential to be used in many healthcare applications.
Intel and the University of Pennsylvania on Monday announced they're launching a federation with 29 other healthcare and research institutions dedicated to training artificial intelligence models that can identify brain tumors. The group plans on training robust models using the largest brain tumor dataset to date. By employing the privacy-preserving technique of federated learning, the organizations will be able to contribute to that dataset without actually sharing their patient data. What is AI? Everything you need to know about Artificial Intelligence "AI shows great promise for the early detection of brain tumors, but it will require more data than any single medical center holds to reach its full potential," Jason Martin, a principal engineer for Intel Labs, said in a statement. Training robust neural networks for healthcare applications is easier said than done.
Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.