High-throughput technologies such as next-generation genomics, transcriptomics and proteomics are capable of generating massive amounts of data quickly, and at relatively low costs. It is tempting to use this data for various medical applications including preclinical disease detection and for prediction of disease predisposition. Pilot projects, initiated by various research groups and Google, are currently underway, but results with not be available for a few years. We here summarize some possible difficulties with these approaches, by using examples from already tried cancer and other screening programs. Population screening, especially with multiparametric algorithms, will identify at least some false positive parameters and screening programs will identify abnormal results in otherwise healthy individuals. Whole genome sequencing will identify genetic changes of unknown significance and may not predict accurately future disease predisposition if the disease is also influenced by environmental factors. In screening programs, if the disease is rare, the positive predictive value of the test will be low, even if the test has excellent sensitivity and specificity. False positive results may require invasive procedures to delineate. Furthermore, screening programs are not effective if the cancer grows quickly, and will identify indolent forms of the disease with slow-growing tumors. It has also been recently shown that for some cancers, more intensive and radical treatments do not usually lead to better clinical outcomes. We conclude that new omics testing technologies should avoid overdiagnosis and overtreatment and need to be evaluated for overall clinical benefit before introduction to the clinic.