Biophysics inspired artificial intelligence for colorectal cancer characterization

Table 1 Artificial intelligence methods in healthcare: A comparison of biophysics inspired machine learning and deep learning methods

Criteria	Biophysics inspired machine learning	Deep learning
Principle	Identification of discriminating features within data set prior to system training based on already proven biophysical properties	Discriminating features/patterns in data discovered through analysis of large databanks
Training corpus for system to accurately assess unseen cases	Small to moderate data cohorts	Large training data corpuses required
Explainability	Settings, e.g., parameter description and number, used in algorithms are easily described	Complex algorithms utilizing numerous parameters and hyperparameters to control the learning process mean such algorithms often poorly understood
Interpretability	Conclusions reached are easily appreciated and can be explained logically by an appropriately trained individual	Human comprehension of sophisticated algorithm predictions/results may be difficult (including for experts in the field)
Generalizability	Accurate extrapolation of results to unseen cases as well as adaptation of such systems to other similar uses	High degree of specialization within DL systems makes adaptation to other similar uses difficult
Bias	Well described, transparent and biophysics-based features help reduce or identify bias within such systems	Bias within training datasets may be perpetuated by DL systems through subtle mechanisms that may even be imperceptible to humans

DL: Deep learning.