Explore the basics of AI and what it means for healthcare
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Explore the basics of AI and what it means for healthcare
Artificial intelligence (AI) is an emerging field that uses computers to understand human intelligence behavior, decision-making, and problem-solving.
This 7-minute educational video defines AI principles of machine learning and deep learning methods.
All videos are optimal with sound on and on maximum screen view.
AI introduction and how it works - (01:00)
Explaining artificial neural networks - (00:52)
Cloud computing and the power of AI - (00:44)
AI as a supportive tool in healthcare - (00:40)
The application of AI in healthcare has many potential benefits to physicians, patients, and healthcare systems alike. Learn more about terminology to discover suitable applications of AI.
Artificial intelligence (AI) is an overarching term for intelligent machines or technologies that can emulate the functions of the human brain, and replicate human capabilities such as decision-making, problem-solving, reasoning, visual perception and speech recognition. AI has the ability to learn through situations that are derived from patterns or features of data. Machine learning (ML), neural networks, and deep learning (DL) are all subsets of AI.
Machine learning (ML) is one of the most exciting and promising areas in AI. ML is a subset of AI. It employs algorithms that learn from data to make predictions or decisions, and its performance improves with experience. ML gives computers the ability to learn without being explicitly programmed. ML algorithms can be developed to be "locked" so that its function does not change, or "adaptive" so its performance can adapt over time based on new inputs.
Deep learning (DL) is a specialized subset of ML, using multi-layered (sometimes 100+ layers) deep neural networks to build algorithms that teach systems to perform tasks on their own, based on large sets of data. DL is one type of ML algorithm and therefore a subset of ML.
Training data is labeled data used to teach AI or machine learning algorithms to make proper decisions. The data must be robust to provide the most suitable outcomes for AI in clinical practice. The training data should be relevant to real-life scenarios and contain variability that ensures the viability of the AI being addressed and created.
For example, for a visual recognition problem, the training data or training set must properly represent all the variability that may be encountered. This can include the various perspectives on the subject, illumination, deformation, occlusions of object, background clutter, and interclass variation of the object. When the training data is robust, it will increase the likelihood of the AI algorithms meeting a solution.
Natural language processing (NLP) is a subfield of AI concerned with the interactions between computers and human language. In particular, how to program computers to process and analyze large amounts of data. NLP is used to comprehend speech or text to extract its meaning. The result is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. NLP can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
The definitions the FDA adheres to are as follows: a radiological CADe device is “intended to identify, mark, highlight or otherwise direct attention to portions of an image […] that may reveal abnormalities during interpretation of images by the clinician.” A CADx device is “intended to provide information beyond identifying […] abnormalities, such as an assessment of disease.” Whenever software is not intended to highlight an abnormality, it is not considered a CADe nor a CADx device. For example, segmentation of brain structures is not considered CADe, while the detection of a tumor candidate is considered CADe. An algorithm that adds information on tumor grade would make it a CADx device.
The advancement of AI in healthcare can complement physician decision-making. Data in healthcare is often unstructured and there is a large amount to source and scale for individual assessment. The use of AI can allow for improved efficiencies, streamline processes and information sharing, and enhance decisions — all in support of patient care.
AI in healthcare is very exciting because of the benefits it can offer to enhance the physician’s ability to care for the patient. However, it is important that enthusiasm doesn’t turn into misguided use of information. AI is not ready to work on its own. AI and physicians must work together to gain the greatest benefits for improving patient care. There are unrealistic predictions and a false sense of what AI can do as a complement in clinical practice. There are precise methods in testing and areas to evaluate the suitability for AI in clinical application. Here are some of the limitations of AI:
There are many areas to evaluate when considering the application of AI in healthcare. The above are some considerations on the appropriate solutions that can benefit the physician, healthcare system, and patient outcomes.
It is important to use high quality and variability of data within AI algorithms to help ensure it aligns to the real-life use cases. The data should be tested properly to validate the accuracy of outcomes.
Questions that should be considered include:
A detailed review of considerations will support the trust of an AI application and further confirm that it has value to clinical practice. AI will not replace physician decision-making; it is intended to enhance it.
The World Health Organization (WHO) issued its first global report about AI in health. Learn about the six guiding principles:
The U.S. Food and Drug Administration (FDA), and others have issued a report with 10 guiding principles about Good Machine Learning Practice (GMLP). The principles from the report include:
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Disclaimer: All content from healthcare professionals is their individual conclusions, unless otherwise cited. All speaker or author engagement for content is noted to acknowledge funding from Covidien LP, a Medtronic company, for any consulting engagement.