This is too nihilistic and is not really what experts like Ioannidis are proposing. Better to evaluate the studies (or find sources that evaluate the studies) individually for their sample size and statistical measures, such as whether or not they control for relevant covariates and do multiple hypothesis testing corrections.

You can download a video of Ioannidis' Mar '11 lecture on nutrition from http://videocast.nih.gov/PastEvents.asp?c=144 (it's big though, 250 MB). Some notes:

• Randomized trials have problems too.
• For example, they'll often inflate the effects by contrasting the most extreme groups (upper vs lower 20%).
• Or, just basic biases, like the winner's curse (large effects tend to come from studies with small sample sizes--you can see this by comparing the log of treatment effect vs the log of total sample size in the cochrane database) or publication bias (leading to missing data).
• Odds ratios in randomized trials also decrease over time.

Generally, Ioannidis wants massive testing via biobanks (sample sizes in the millions), longitudinal measurements, and large-scale global collaborations. These do not necessarily mean only randomized trials, and in fact they are pretty much impossible for that kind of data set. Epi can work too, it just needs to be done well.