If you just returned from HIMSS this year you most likely hear one of these acronyms every hour you were there. What are they? What is the difference between them? If you google these questions you will get all kinds of definitions and explanations. Here are the basics, from the leading text book around the world, Artificial Intelligence: A Modern Approach.
Artificial Intelligence is composed of six different disciplines:
- Natural Language Processing to enable it to communicate successfully in English
- Knowledge Representation to store what it knows or hears
- Automated Reasoning to use the stored information to answer questions and to draw new conclusions
- Machine Learning to adapt to new circumstances and to detect and extrapolate patterns
- Computer Vision to perceive objects
- Robotics to manipulate objects and move about
To build a generally intelligent agent, you need machine learning in addition to the other aspects mentioned above.
Machine Learning is roughly the science of prediction. Given certain knowns (features), you wish to predict some unknowns (targets). The unknown could be structured (e.g. numeric) or unstructured (e.g. a string response).
Deep Learning is a sub field of machine learning where concepts are learned hierarchically. The simplest concepts emerge first, followed by more complicated concepts that build on the simpler ones. Usually, this leads to a simple layered hierarchy of concepts.
So how is AI Changing Healthcare?
This is the first of our ongoing reporting on how AI is being integrated into healthcare technology. We are seeking out the thought leaders and innovations that are moving the needle forward.
HIT Think How artificial intelligence is already paying dividends in healthcare
By Mark R. Weber – The power of AI starts with large data sets, something that’s become more evident in healthcare. Automated patient records, information sharing across entities and the full digitization of business operations has ushered in the era of big data. Read the article.
Can Machine Learning Find Medical Meaning in a Mess of Genes?
By Jordana Cepelewicz – “We don’t have much ground truth in biology.” According to Barbara Engelhardt, a computer scientist at Princeton University, that’s just one of the many challenges that researchers face when trying to prime traditional machine-learning methods to analyze genomic data. Learn more.
Healthcare IT in the Age of Artificial Intelligence and Machine Learning
By Matt Ferrari, Chief Technology Officer, ClearDATA – AI and machine learning are making possible exciting breakthroughs in patient care by finding meaning in massive data sets. All three major public cloud providers – AWS, Google, and Microsoft – were at HIMSS talking about deep products they are developing, along with current and potential uses cases for AI and machine learning. Read the article.
Operationalizing NLP to Support Value-based Care
Watch the webinar – Healthcare providers are facing an urgent need to streamline operations while improving quality of care and patient satisfaction. With a wealth of technology hype around AI, Natural Language Processing (NLP) and big data, how are providers to know what investments to make and how to bring these technologies into production use?
Nuance and Epic Team to Deliver Array of AI-powered Healthcare Virtual Assistants
Nuance brings next level conversational AI to Epic EHR with new Dragon Medical Capabilities. In a press release,
Nuance and Epic announced that Nuance’s new artificial intelligence (AI)-powered virtual assistant platform is now integrated into Epic’s EHR. With Nuance’s new virtual assistant technology, Epic’s EHR further improves caregiver productivity and efficiency across the continuum of care.