Enhancing medical-imaging artificial intelligence through holistic use of time-tested key imaging and clinical parameters: Future insights

Table 1 Comparison of symbolic artificial intelligence, statistical learning and deep learning (Nadkarni P & Merchant SA)

	Symbolic AI	Statistical learning (SL)	Deep learning (DL)
Entities manipulated	Both symbols and numbers	Numbers (most representing interval data, but some representing categories)	Same as SL, can be applied to the same problems
Algorithm design	Requires computer-science knowledge & traditional software skills, including user-interface design	Less customization needed, but problem-specific pre-processing of data (e.g., statistical standardization is necessary)	Same as SL
Domain expert role	Work closely and extensively with software developer, Evaluate output of algorithm for a set of test cases against desired output	To identify variables/features of interest, annotating training data, and evaluating results and individual features’ relative importance. Must evaluate results for novelty	Same as SL, but features can be discovered from raw data, so may not need designation. Annotation is more burdensome because much more data is typically needed
Data inputs	Expert and software work closely to design software and create test cases	Rows of data, annotated text, or images. For supervised learning, the output variable’s value for each instance is also supplied	Same as SL, in some forms of DL, notably for image processing, features are discovered from raw data
Partitioning of input data	(Not applicable)	Divided into training data and test data	Same as SL
Generalizability	Limited to modest: Typically required tailored solutions, especially for the user interface	More generalizable than symbolic AI, but success depends on careful feature selection, choice of method and whether the data matches the method’s assumptions (e.g., Gaussian distribution, additive effects)	DL methods are “non-parametric” and rely on few or no assumptions about the variables/features in the data

AI: Artificial intelligence; SL: Statistical learning; DL: Deep learning.