artificial intelligence | roboticsfinder

Why do we seek external advice for our health? In part, we expect providers to know more than us. This is partially through the experience of treating other patients that have had similar struggles, and also by having the background knowledge and mental data storage to make decisions that will guide us toward our goals. So, what if there was an external system that could help guide the decisions that providers make to improve our health?

Artificial intelligence is going to change the way that clinicians diagnose and treat, for the better. Any human work has the risk of error and knowledge can be variable between practitioners. Patients oftentimes don’t expect to find their providers searching their conditions or related questions on Google, but the truth is that this is common practice. Seeing a condition for the first time, with a client expecting you to provide guidance for them and keep them safe is difficult and sometimes external resources are necessary. In my first years of practice, Google Scholar and the search engine became my close alliances while I navigated many new diagnoses and patient questions.

IBM Watson seeks to provide an external network of both data and experience. It is growing to become an amazing resource for answering questions, compiling data, and helping drive logical decisions in medicine. A large cloud of data storage that learns as it compiles more data, this promises to be a close resource for clinicians by making our decisions more precise and valuable for patients. Watson’s storage allows for a diverse amount of data storage, including personal data along with genomic and clinical research. When searching for information, it will be extremely valuable and efficient to have all this information stored in one known resource.

Quality care is a combination of data and experience. Both are extremely valuable, and the combination is what makes medical decisions supreme. Without research we are unable to learn as a society and defend the decisions that we make. And it is through experience, working with thousands of people, that we begin to see patterns and apply them to our practice. People often ask, ‘have you seen this before?’ It comforts them when the answer is yes, and this is because experience gives us the power of efficiency. With a resource such as IBM Watson, providers will have the benefits of current research and data to pair with our experience.

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Scientific gains are in a very exciting time as we progress artificial intelligence and machine learning. AI systems are dynamic, and respond to the subject as they gain more data, which increases our database of knowledge. There are many aspects of human development that we still have limited knowledge of, and while we have insight on the amount of sleep of infants, there is much less knowledge of the quality. It has been shown that baby fatigue affects maternal sleep patterns and behavior. By gaining insight on these sleep patterns, we may find a way to modify them for maximum improvement of both infant and parental sleep behavior.

Knit health is using machine learning to better understand the sleep patterns of infants and gain insight on how these patterns may affect health and other issues. By integrating a deep learning system with a baby-cam, the company uses metrics to measure and detect sleep patterns. Some of these metrics include breathing, movement, and physical presence, all fueling the knowledge toward the quality of sleep. The camera detects the physical presence of the baby as well as motion and positional changes to provide the system with information to learn sleep patterns.

What is really interesting is that because the company is in the early stages of data collection, the data that it gains will grow with its costumers. Parents of infants will have the opportunity to use machine learning essentially in real time to gain insight on human behavior and development, all while potentially increasing quality of family life.

With their product in early beta, the company is seeking parents of young children to participate in beta testing. This is a great opportunity to gain insight on a product before it is released.

Artificial intelligence for diagnostics: IBM Watson

Machine Learning for Pediatrics: Knit Health