AI First Healthcare - Chapter 1 Excerpts

A tenet of this book’s central message is that artificial intelligence comprises more than machine learning (ML). If we think of AI solely in the context of ML, it’s doubtful that we ever build intelligent systems that mirror human intelligence in the performance of clinical or healthcare activities, or that we create AI that materially enhances patient experiences, reduces costs, and improves the health of people and the quality of life for healthcare workers. Most publicized implementations of AI showcase ML model successes, which explains why many see an equivalence between machine learning and AI. Furthermore, the most common AI applications utilizing deep learning, computer vision, or natural language processing all employ machine learning. The supposition that machine learning is the same as AI ignores or dismisses those. aspects of a software stack used to build intelligent systems that are not machine learning. Or worse, our imagination or knowledge of what’s possible with AI is limited to only those functions implementable by machine learning.

There are different types or subcategories of machine learning, such as supervised learning, unsupervised learning, and deep learning. In supervised learning, we train the computer using data that is labeled. If we want an ML model to detect a child’s mother, we provide a large number of pictures of the mother, labeling each photo as “the mother.” Or If we want an ML model to detect pneumonia in an X-ray, we take many X-rays of pneumonia and label each as such. In essence, we tag the data with the correct answer. It’s like having a supervisor who gives you all the right solutions for your test. A supervised machine learning algorithm learns from labeled training data. In effect, we supervise the training of the algorithm. With supervised machine learning, we effectively use a computer to identify, sort, and categorize things. If we need to pore through thousands of X-rays to identify pneumonia, we will likely perform this task more quickly with a computer using machine learning than with a doctor. However, just because the computer outperforms the doctor on this task doesn’t mean the computer can do better than a doctor in clinical care.

Much of the learning we do as humans is unsupervised, without the benefit of a teacher. We don’t give the answers to unsupervised learning models, and they don’t use labeled data. Instead, we ask the algorithm, the model, to discover the answer. Applying this to the example of the child’s mother, we provide the face recognition algorithm with features, like skin tone, eye color, facial shape, dimples, hair color, or distance between the eyes. Unsupervised machine learning recognizes the unique features of the child’s mother; learning from the data, it identifies “the mother” in images with high accuracy.