Artificial Intelligence, Machine Learning, and Medicine: A Little Background Goes a Long Way Toward Understanding

Citations Over TimeTop 16% of 2021 papers

Abstract

Artificial intelligence (AI) and machine learning refer to computers built and programed by humans to perform tasks according to our design. This is vital to keep in mind as we try to understand the application of AI to medicine. AI is a tool with strengths and limitations. The primary strength of AI is that it allows us to assimilate and process unlimited quantities of health care data. The limits of AI include the inability of machines to adapt in a human sense, the reality that machines lack human insight (i.e., clinical judgment or common sense), and the limitation that machine-learning algorithms are subject to the data on which they are trained. Thus, we must adapt to AI and machine learning. Next, because machine learning is a type of AI in which computers are programmed to improve the algorithms under which they function over time, we require insight to achieve an element of explainability about the key data underlining a particular machine-learning prediction. Finally, machine-learning algorithms require validation before they can be applied to data sets different from the data on which they were trained. As computers have become faster and more powerful, and as the availability of digital data has become immense, we can program our machines to analyze data and recognize patterns that, in sum, are a primary basis of medical diagnosis and treatment.

Related Papers

• → Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction(2019)71 cited
• → Application of Machine Learning in Animal Disease Analysis and Prediction(2020)26 cited
• → Sentiment Analysis by Using Supervised Machine Learning and Deep Learning Approaches(2020)3 cited
• → Breakdown of Machine Learning Algorithms(2022)1 cited
• → Weather and Forecasting64 cited