As mentioned above, there are a number of statistical methods available. The funnel plot is probably the oldest, but it has its "issues" (Lau, J., et al (2006). BMJ (Clinical Research Ed), 333(7568), 597–600)
An interesting, newer method is Ioannidis's test for excess significance (Ioannidis, J. P. A., & Trikalinos, T. A. (2007). Clinical Trials (London, England), 4(3), 245–253).
However, all these techniques amount to "statistical divining rods", telling you whether the results seem too good to be true.
If you really want to determine whether publication bias is present, you need to know (a) the results (not just the existence) of unpublished studies and (b) whether the results in the published literature have undergone "statistical alchemy" and, if so, what the true "unspun" results are.
Since you say you're working in medicine, you have a unique opportunity, at least if you're working with drugs. You can use FDA drug approval packages, as I have in my own work. you may find these papers of interest:
Turner, E. H., Matthews, A. M., Linardatos, E., Tell, R. A., & Rosenthal, R. (2008). Selective publication of antidepressant trials and its influence on apparent efficacy. The New England Journal of Medicine, 358(3), 252–260. doi:10.1056/NEJMsa065779
Turner, E. H., Knoepflmacher, D., & Shapley, L. (2012). Publication Bias in Antipsychotic Trials: An Analysis of Efficacy Comparing the Published Literature to the US Food and Drug Administration Database. PLoS Medicine, 9(3), e1001189. doi:10.1371/journal.pmed.1001189
Turner, E. H. (2013a). How to access and process FDA drug approval packages for use in research. BMJ (Clinical Research Ed), 347(oct14 2), f5992–f5992. doi:10.1136/bmj.f5992
Hope this helps.