# AI-Based Prediction of Myocardial Infarction Risk as an Element of Preventive Medicine
## 4 Discussion
### Directions for Further Research  
The main objectives of further studies should aim to develop and test in clinical practice the current concept of the prevention system and, in particular, remove the processing delay to provide real-time processing. A larger number of subjects would allow for better profiling of patients for their initial more accurate classification into the MI risk groups.  
Directions for further research include the area of patient-specific preventive MI medicine consisting of: follow-up visits (e.g., once per month), daily use of accessories such as scales, smart bands, smartphones with appropriate software connected to a central database and clinical AI system [27], use of modifiable risk factors to improve the patient'shealth status and MI risk assessment, and inclusion of non-modifiable MI risk factors in AI-based analysis.  
Artificial intelligence-based cIMT/PA segmentation methods are being used prototypically to monitor CVD/stroke risk and can be extended to parameters recommended for assessing atherosclerosis in carotid ultrasound [28].  
AI techniques provide quicker and more accurate tools for hazard identification, combining the features of extraction and classification which simplifies data analysis, and improves accuracy and robustness. For the aforementioned reasons, AI-based computational diagnostic techniques show great potential in helping healthcare professionals, families and caregivers of patients, and their application in everyday life benefits both patients and at-risk individuals [31].  
Opportunities to solve existing problems in medical imaging lie in the wider use of automated data capture, collection and AI-based analysis to predict future patient conditions. This data can be multi-modal, from a variety of sources, downloaded and transmitted in a variety of forms and time intervals, and the synergistic effect allows for a more complete picture in both healthy individuals and patients with various conditions. However, it should be noted that the results from this system will only be a second opinion for the doctor/diagnostic who will be making the specific diagnostic decision--a human being will always be responsible for this.  
Improved smart healthcare technology can be beneficial in the short term and will transform healthcare delivery in the long-term too, both for COVID-19 and other diseases (cardiovascular, etc.). Datasets and models sharing, preprint archiving, medical knowledge and experience sharing, e-learning and continuing medical education, medical robotics will grow in importance in the foreseeable future. This will bring an adaptation of personalized prevention, diagnosis, treatment, rehabilitation, and care [32]. Currently, data-driven approaches dominate over model-driven approaches [33; 34; 35], but it is hoped that hybrid approaches may be dominant in future biomedical reasoning and prediction [36; 37; 38]. An important method of development may be fuzzy-based models, useful for prediction purposes [39; 40]. Even the most complicated sources of the biosignals are possible to use within the proposed system, such as neuroprostheses and brain-computer interfaces [41; 42], even for influence of stress assessment [43]. AI models for cardiovascular-related diseases are gradually evolving into a form suitable for wearable and mobile devices [44]. Even the associations between meteorological factors and air pollutants, as well as COVID-19 and the number of acute myocardial infarctions (AMI), should be taken into account when analysing and predicting the data [45; 46].