RESEARCH OF THE PROCESS OF VISUAL ART TRANSMISSION IN MUSIC AND THE CREATION OF COLLECTIONS FOR PEOPLE WITH VISUAL IMPAIRMENTS

Abstract

The article examines the use of neural networks in telemedicine for patient self-diagnosis, focusing on custom LSTM (Long Short-Term Memory) models and pre-trained BERT (Bidirectional Encoder Representations from Transformers) models. Telemedicine has become essential in modern healthcare, particularly due to the rise in remote consultations and diagnostics during global health crises. The study reviews research in this area, highlighting contributions from international and Ukrainian experts. It explores the effectiveness of neural networks in interpreting patient symptom descriptions and generating accurate diagnoses.
The custom LSTM model processes text-based symptom descriptions but faces challenges with limited datasets, impacting its diagnostic accuracy. On the other hand, the BERT model leverages transfer learning, achieving over 92% accuracy in predictions, thanks to its ability to understand complex language inputs. An enhanced LSTM model is also presented, incorporating Dropout and Batch Normalization layers, which help increase the robustness and accuracy of the predictions while managing the model's complexity.
The article also discusses the technical challenges of training these neural networks, including the variability of symptom descriptions and the need for large annotated medical datasets. Proposed solutions include domain-specific enhancements and data augmentation to improve the models' performance.
In comparing the three models, the study identifies that while the custom LSTM model is more flexible and simple, it struggles with generalization. The BERT model, by contrast, excels in understanding and accuracy but requires more computational resources. The enhanced LSTM model offers a balance between complexity and performance.
In conclusion, neural networks offer promising advances in telemedicine by providing accurate, AI-assisted self-diagnosis. Although challenges remain, such as data requirements and computing power, models like BERT are key to improving healthcare accessibility and accuracy in telemedicine applications.
Among the main advantages of using artificial intelligence for the healthcare system are in-depth diagnostic analysis, individual treatment regimens, and optimization of medical processes. However, modern technologies in the medical field are not yet able to completely replace experienced specialists.