Sector: Health

Problem: For Earlier detection of cardiac issues and to allow us to impact the course of disease and improve outcomes. 

Solution: Caption Health has developed the AI platform that enables heart ultrasound access for early disease detection – when there is the highest potential for impact.   

Problem: When you go to a new doctor he makes you recount your medical history on a sheaf of form, It takes a lot of time because it is physically stored. 

Solution: Blockchain technology made it possible to transfer medical data while keeping patients’ records private and safe from tampering—even by the patients themselves.  

Problem: In the current scenario, it is difficult for one person to visit hospitals for minor health issues because it is time consuming in these hectic schedules and also for one to conserve their time and money. It is vital to find a remedy that will assist  in restoring his health with minimal time and financial expenditure. 

Solution: Care Angel is redefining healthcare management strategies with artificial intelligence through the use of Angel, the world’s first virtual nurse assistant. This unique virtual nurse assistant uses real-time clinical and non-clinical data and analytics to make confident recommendations that reduce unnecessary ED visits and readmissions. 

Problem: Clinical trials are critical to determining the safety and effectiveness of medicine and treatment regimes, but these trials are challenged by the cost of identifying suitable recruits and the limited scope of primary data in research. Increasingly, administrative data and other secondary-use patient data repositories are used to improve recruitment and longitudinal observations.

Solution: HEAVY.AI accelerates SQL queries to provide interactivity for big data medical research at an unprecedented scale. This enables medical researchers to analyze the entirety of primary and secondary-use patient data records for unparalleled epidemiological and clinical data insights that drive quality recruitment and data analysis in medical research settings. . HEAVY.AI helps researchers discover insights beyond their legacy medical data analysis software while simplifying clinical data management.

Problem: Of the $2 trillion spent on healthcare in the United States annually, an estimated $60 billion is lost to healthcare fraud. This results in higher premiums and out-of-pocket expenses for patients, as well as increased insurance costs for employers. The growing human toll: over six million Americans are addicted to prescription drugs, which account for over 40% of drug overdose deaths.

Solution: HEAVY.AI’s accelerated analytics platform cuts through billions of rows of records in milliseconds. This enables health scientists, data scientists, and other researchers and analysts to visualize, analyze, and truly interact with massive data sets to derive new insights aimed at public health services, abuse and overdose prevention, and criminal prosecution. For the first time, healthcare fraud analysis includes the geographic component of big data to determine which doctors, in which specialties prescribe, for example, specific quantities of opioids in selected cities, broken out by the drug prescribed. Multiple data sources can be overlaid on top of each other to render heatmaps in areas of high risk for fraud, waste, or abuse, and to identify available resources and services to render aid and reduce healthcare fraud cases.

Problem: Alzheimer’s disease is heartbreaking for those afflicted, and perhaps even more so for their families and friends. Deaths due to Alzheimer’s are sharply on the rise, increasing 123% between 2000 and 2015. Despite decades of research, there is no cure.

Solution: The Foundation for Precision Medicine is helping to lead the fight against Alzheimer’s and other brain diseases by using artificial intelligence and big data to enable clinical diagnoses before loved ones regress and it is too late to intervene.

Problem: Roboflow empowers developers to build their own computer vision applications, no matter their skill set or experience. They streamline the process of labeling the data and training the model.

Solution: Every developer should have computer vision available in their toolkit. Medical professionals process an overwhelming amount of information each and every day. Computer vision eases this burden by streamlining diagnostic and procedural operations.

Problem: The World Health Organization (WHO) has identified antibiotic resistance as an urgent serious global threat. A baumannii can develop resistance to all current antibiotics, including the last-line polymyxins. A major barrier in developing new-generation polymyxins is the lack of understanding of polymyxin interactions with bacterial and human kidney tubular cell membranes.

Solution: The first-ever quantitative membrane-based SAR (QSAR) and structure-toxicity relationship (QSTR) models have been developed for engineering and discovery of superior polymyxins against MDR A. Baumannii. This project will develop a cutting-edge systems approach by employing all-atom molecular dynamic simulations, Big Data, machine learning, and artificial intelligence to design new-generation polymyxins.

Problem: Zika virus is a mosquito-borne flavivirus that was first identified in Uganda in 1947 in monkeys. It was later identified in humans in 1952 in Uganda and the United Republic of Tanzania. Outbreaks of Zika virus disease have been recorded in Africa, the Americas, Asia, and the Pacific.

Solution: The approach adopted by the research team of IBM and Cary Institute was to deploy Ml learning models to identify the carriers of the virus.
The research team used physiological, behavioral, range, and social structure data to develop a Bayesian predictive Machine Learning model. The model predicted carriers with an accuracy of 82%. They produced an interactive map showing risk-prone areas.

Problem: In countries like India where diabetic cases are very high, problems like diabetic retinopathy are very common. These conditions can lead to blindness which could have been prevented. The technology and equipment to identify the retinal problem are not available in underdeveloped parts of the country. Also, this equipment is very costly. 145 million people across the globe suffer from Diabetic Retinopathy.

Solution: AI can be used to eradicate preventable blindness. AI helped in developing an affordable and portable eye scanning device. Companies can detect retinal diseases with the help of AI and create faster diagnoses. Forus Health developed and manufacture highly advanced medical devices, designed for the effective management of visual health. The technology solutions are built for the affordability and accessibility of both urban and rural patients.