Although I would have to admit that not all published survival models follow the best statistical practices, there are some important differences between what is typically considered modern machine learning and most epidemiological/clinical studies.
One is scale. Machine learning often involves tens of thousands or millions of observations, providing enough cases to afford setting aside separate training and test sets. (Frank Harrell has estimated that you require thousands of cases to do so without losing power over techniques like bootstrapping that use all the available data.)
The second is the intended use of the model. Machine learning is typically interested in prediction, survival modeling much less frequently. Although survival models can be used for prediction, they are more typically used to determine whether one or a few variables of particular interest are related to outcome when other variables are taken into account. Cox proportional hazards models provide a way to approach this problem, even with the correlated predictors that are common on clinical/epidemiological studies. Multicollinearity will tend to increase the standard errors for individual predictors, but if the proportional hazards assumption is met then the point estimates of the coefficients will be useful for thinking about the underlying biology even if their magnitudes are optimistic in magnitude.
There certainly are ways to use clinical/epidemiological data sets more efficiently and intelligently. Harrell's text on Regression Modeling Strategies and the associated
rms package in R illustrate those better approaches to validating models and assessing their optimism.
I think, however, that those who read studies reporting model results without attempts at further validation will tend to discount the magnitude of the result. As an experienced physician told me decades ago, only somewhat in jest: "When a report about a great new drug comes out, you'd better use it while it still works." Eventual regression to the mean is a reasonable expectation when striking, poorly validated results are published.