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The overview covers early diagnosis of cognitive impairment, cognitive rehabilitation, longitudinal monitoring, falls and safety detection, balance and mobility training, cardiovascular risk assessment, and caregiver support. The abstract also reports early successes such as new startups, acquisitions, venture capital funding, business alliances, and design innovation awards, and details NIH funding challenges including regulatory oversight, mentorship, recruitment barriers, and limited data for ML/AI 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technologies does MassAITC support for successful aging and home care?","Question",{"text":63,"@type":64},"MassAITC supports pilot studies using mobile technologies, wearables, and contactless sensors that incorporate machine learning and AI approaches.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which research topics are addressed in the supported studies?",{"text":68,"@type":64},"Topics include early diagnosis of cognitive impairment, cognitive rehabilitation, longitudinal monitoring of chronic conditions, falls and safety detection, balance and mobility training, cardiovascular risk assessment and management, and caregiver support.",{"name":70,"@type":61,"acceptedAnswer":71},"What early successes and funding challenges are described?",{"text":72,"@type":64},"The abstract highlights successes such as new startups, acquisitions, venture capital funding, business alliances, and design awards, alongside challenges from industry investigators including regulatory oversight 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SUPPORTS TECHNOLOGIES INCORPORATING MACHINE LEARNING AND AI TO SUPPORT SUCCESSFUL AGING AND CARE OF PLWD  \nNiteesh Choudhry, Harvard Medical School, Boston,  \nMassachusetts, United States  \nMassAITC supports pilot studies on using mobile technologies, wearables, and contactless sensors that incorporate emerging machine learning and AI approaches to support successful aging and the care of individuals with ADRD in their home environments. We will provide an overview of studies supported by our Center, which have addressed a broad range of topics including the early diagnosis of cognitive impairment, cognitive rehabilitation, longitudinal monitoring of chronic conditions of aging, falls and safety detection, balance and mobility training, cardiovascular risk assessment and management, and caregiver support. We will describe some of the early successes resulting from these awards including the formation of new start-up companies (e.g. Billion Labs Inc.), company acquisitions (e.g. Kintoby Rippl and TellUs You Care by AIP Healthcare), venture capital funding (e.g. Butlr and Moneta Health), capital and business alliances (e.g. Butlr and Ricoh), and design innovation awards (e.g. MindMics) . We will also discuss some of the unique challenges arising from the funding of predominantly industry investigators within the context of a NIH granting mechanism. These have included the timelines and requirements for regulatory oversight, including IRB approvals, the management of conflicts of interest and intellectual property interests, the need to provide mentorship about study design and the conduct of human subjects research, difficulties recruiting individuals with ADRD for investigators not working in environments without an established pool of potential participants, and limitations of the amount of data available to build ML/AI models within the constraints of the size of the individual awards.","cbCaifFHH70FoXsM","https://ap.wps.com/l/cbCaifFHH70FoXsM","pdf",449578,"English","# Overview of MassAITC pilot studies\n## Technologies and AI/ML approaches\n## Research topics covered\n## Early successes and challenges","[{\"question\":\"What technologies does MassAITC support for successful aging and home care?\",\"answer\":\"MassAITC supports pilot studies using mobile technologies, wearables, and contactless sensors that incorporate machine learning and AI approaches.\"},{\"question\":\"Which research topics are addressed in the supported studies?\",\"answer\":\"Topics include early diagnosis of cognitive impairment, cognitive rehabilitation, longitudinal monitoring of chronic conditions, falls and safety detection, balance and mobility training, cardiovascular risk assessment and management, and caregiver support.\"},{\"question\":\"What early successes and funding challenges are described?\",\"answer\":\"The abstract highlights successes such as new startups, acquisitions, venture capital funding, business alliances, and design awards, alongside challenges from industry investigators including regulatory oversight timelines, IRB approvals, conflicts and IP management, mentorship needs, recruitment difficulties, and limited data for ML/AI models.\"}]","MASS AITC Supports Technologies - Incoroporating Machine Learning and AI to Support Successful Aging and Care of PLWD - Abstract | PDF",1790635573]