Google pronounces funding for AI-enabled digital well being initiatives

Google introduced it’s funding 15 AI-powered initiatives, together with digital well being initiatives to enhance supplier expertise and affected person entry to care, through its dedication to advancing the United Nations Sustainable Growth Objectives

Every challenge obtained $3 million in technical help, money assist and Google Cloud credit. A handful of initiatives obtained Fellowships, the place a group of Google staff works with a corporation professional bono full-time for as much as six months. 

Of the 15 AI initiatives funded, the next eight digital well being endeavors have been awarded funding:

RAD-AID supplies low-source hospitals with an AI-enabled platform that helps triage sufferers, primarily concerning respiratory illness and breast most cancers. The platform additionally helps interpret X-rays and scans and supply check outcomes. 

Wuqu’ Kawoq and secure+natal are collaborating to develop a machine learning-enabled toolkit to assist midwives in rural areas of Guatemala detect neonatal problems in real-time, comparable to poor fetal progress and fetal stress throughout supply. The toolkit will encompass an ultrasound and blood strain monitor related to at least one’s smartphone. 

MATCH (Music Attuned Know-how – Care through eHealth) is a challenge constructed out of the College of Melbourne and CSIRO that mixes music and wearable sensor expertise to lower agitation in sufferers with dementia. Google’s grant will assist the group develop the sensor expertise and AI-enabled adaptive music system.  

Makerere AI Lab will develop a 3D printed adapter that processes pictures utilizing AI and is appropriate with a telephone or microscope. The purpose is to assist suppliers in Uganda diagnose sufferers with sicknesses, comparable to tuberculosis, malaria and most cancers, in low- and middle-income nations the place lab technicians are scarce.   

IDinsight with Attain Digital Well being developed a pure language-enabled question-answering service for expectant moms in South Africa, which supplies solutions to inquiries and important well being data.  

Causal Foundry seeks to develop a smartphone-based device that makes use of machine studying to assist group well being suppliers in Sub-Saharan Africa handle affected person data and habits modifications associated to being pregnant and childbirth.

Jacaranda Well being delivers an SMS-based digital well being platform that solutions questions for expectant moms in Sub-Saharan Africa. The platform supplies behavioral nudges and features a pure language-powered helpdesk that helps triage sufferers and connects them to human brokers. The funding will probably be used to refine the machine studying mannequin inside the platform.  

The College of Surrey and Signapse will use generative AI to translate on-line and offline textual content in actual time for deaf folks within the U.S. and U.Okay. and supply photorealistic movies in signal language, allowing extra accessible entry to healthcare and different data.  


Google has its personal machine studying expertise, dubbed Med-PaLM 2, aimed toward bettering healthcare data entry. Med-PaLM 2 makes use of the tech firm’s giant language mannequin to reply medical questions. 

In March, Med-PaLM 2 was examined on U.S. Medical Licensing Examination-style questions and carried out at an “skilled” test-taker stage with 85%+ accuracy. It additionally obtained a passing rating on the MedMCQA dataset, a multiple-choice dataset designed to handle real-world medical entrance examination questions. 

One month later, Google introduced it could make Med-PaLM 2 out there to pick Google Cloud prospects to discover use instances, share suggestions and for restricted testing. 

The corporate additionally introduced a brand new AI-enabled Claims Acceleration Suite, created to assist with the method of prior authorization and claims processing of medical health insurance. The Suite converts unstructured knowledge (datasets not organized in a pre-defined method) into structured knowledge (datasets extremely organized and simply decipherable).

In July, a research carried out by Google researchers and printed in Nature revealed that Med-PaLM supplied long-form solutions aligned with scientific consensus on 92.6% of questions submitted, which aligns with clinician-generated solutions at 92.9%.

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