# Artificial Intelligence and Women Researchers in the Czech Republic
## 3 AI Project Applications in Medicine  
Modern health care is highly specialized. Complex examination of a single patient involves many expert consultations and laboratory tests. Medical knowledge, examinations and treatment are distributed functionally, geographically and also temporally. There is a need for reliable and consistent information flow among all participating subjects, with the aim to satisfy the global goal--improved health of a patient. Of course, the necessary information flow is not predictable in its extent or structure, but develops and changes in time due to new knowledge and reactions. To satisfy these requirements and provide adequate decision support, the use of flexible intelligent software support is becoming increasingly desirable. Many of these systems utilize artificial intelligence methods as a suitable approach to big heterogeneous multidimensional and multimodal data analysis. When we look at the topics of our research projects during the past decades we can find several common lines, namely, large multimodal and multidimensional data, continuous data (e.g., signals), machine learning, feature extraction, feature selection using optimization methods and semi-automated classification methods [29; 30; 31; 32]. The first application areas in medicine were those that are data intensive. In our case, the collaboration started in cardiology, electrophysiology, neurology and diabetology. Later, rehabilitation was added. With the advancement of the Internet of Things and wearables, new areas opened in home care and telemedicine [33]. In the following, we present the most important relevant projects we have been involved in and their results. We selected as examples those projects in which the principal investigator or even the co-investigator was a woman. This information is shown at each project. **Knowledge-based support of diagnostics and prediction in cardiology** (PI female--[PERSON])  
In this project, a set of theoretically well-developed methods and algorithms for knowledge-based support of diagnostics and prediction in cardiology was designed and developed [34; 35]. Developed methods were implemented. The implemented system allows to analyze data from multi-electrode records of surface ECG [36], solve inverse problem, integrate results of further methods and suggest diagnosis on their basis [37; 38; 39]. A database of interpreted data was developed. The project supported especially interconnection of basic research in the area of artificial intelligence with application of developed methods to medicine. A real-world task provided a platform for verification of both the functionality of the proposed methods and the medical application itself. The project results are used in electrophysiology and constituted a good theoretical platform for a successive project in cardiology, as described in the next paragraph. **Features of electromechanical dyssynchrony that predict effect of cardiac resynchronization therapy** (PI female--[PERSON]; co-investigator--[PERSON])  
Impaired coordination between the ventricles or uncoordinated contraction of individual ventricular walls against each other (dyssynchrony) contribute to further heart failure progression in some patients. Reduction or elimination of dyssynchrony through cardiac resynchronization therapy (CRT) is able to slow or even stop pathologic remodeling, improve heart failure symptoms and induce reverse remodeling, leading to a decreased hospitalization rate and mortality. However, our abilities to identify the presence and type of dyssynchrony are still limited and although the primary goal of CRT is to restore the normal ventricular contraction sequence (i.e., to eliminate mechanical dyssynchrony), our current CRT indication criteria are based solely on an electrical dyssynchrony, specifically on assumption that it is present based on a 12-lead ECG.