Currently, software products for use in medicine are actively developed. Among them, the dominant share belongs to clinical decision support systems (CDSS), which can be intelligent (based on mathematical models obtained by machine learning methods or other artificial intelligence technologies) or non-intelligent. For the state registration of CDSSs as software medical products, clinical trials are required, and the protocol of trial is developed jointly by the developer and an authorized medical organization. One of the mandatory components of the protocol is the calculation of the sample size. This article discusses the calculation of the sample size for the most common case, the binary outcome in diagnostic/screening and predictive systems. For diagnostic/screening models, cases of a non-comparative study, comparative study with testing of the superiority hypothesis, comparative study with testing of a hypothesis of non-inferiority in cross-sectional studies are considered. For predictive models, cases of randomized controlled trials of the complex intervention "prediction + prediction-dependent patient management" with testing of the hypothesis of superiority and non-inferiority are considered. It is emphasized that representativeness of the sample and other design components are no less important in clinical trials than sample size. They are even more important since systematic biases in clinical trials are primary, and even the most sophisticated statistical analysis cannot compensate for design defects. The reduction of clinical trials to external validation of models (i.e. evaluation of accuracy metrics on external data) seems completely unreasonable. It is recommended to perform clinical trials with the design adequate to the tasks, so that further clinical and economic analysis and comprehensive assessment of medical technologies are possible. The sample size calculation methods described in the article can potentially be applied to a wider range of medical devices.
Keywords: binary outcome; clinical decision support systems; clinical trials; diagnostic models; external validation; predictive models; sample size.