Medical AI

Natural Language Processing is one of the key values provided by AI. By processing unstructured data, such as text and speech, it is possible to take insights and facts out.

It may seem that NLP technology is intended only for speech or text recognition, but in fact, these are the most superficial tasks that can be completed. This technology can be used for much deeper and more complex tasks, like classification, machine translation, sentiment analysis, chatbot building, data extraction, summarization, and text generation. Natural Language Processing solutions can perform different tasks at speeds unmatched by humans. This technology is widely used in medicine to streamline workflows and improve efficiency.

Computer vision is a field of artificial intelligence related to the analysis, classification and recognition of images and videos. CV-systems are usually based on ML algorithms, with the help of which they learn to distinguish one object from another, to see patterns and regularities. We can develop computer vision models to analyze, extract and interpret visual data, which can be extremely helpful for pathologists. Our services include:

1. Image and video recognition;
2. Object detection and classification;
3. Image segmentation;
4. Face detection and recognition;
5. Video analytics;
6. Robotics;
7. Industrial automation;
8. Security and surveillance;
9. Self-driving cars;
10. Augmented Reality.

Artificial Intelligence recommendation systems are ML algorithms that make recommendations based on thousands of data points. AI recommenders are widely used to predict the user’s choice, analyze his past behavior, the behavior of similar users, and the overall popularity of the product, and offer relevant suggestions to users. AI recommenders can be used for e-commerce, media, entertainment, social media, finance, education, and healthcare.

AI recommendation systems play an extremely important role in medicine. They can be used to recommend personalized treatment plans to patients based on their medical history and genetic data. Using collaborative filtering, AI recommenders analyze user behavior in a group to make suggestions. Content-based filtering recommends items based on past interests.

Data science is an interdisciplinary field that uses algorithms, systems, processes and scientific methods to extract facts and insights from structured and unstructured data. It makes use of various techniques from statistics, ML and computer science to make predictions and analyze data. Here are examples of common use cases for data science:

1. Prediction and forecasting using historical data to make predictions about future events or trends. For medicine, it allows researchers to get predictions about the patient’s diagnosis and development of various diseases;
2. Customer analytics. Data science is used to analyse customers’ habits and purchases to personalize recommendations;
3. Business intelligence. Using data about the company’s performance to get insights about its efficiency;
4. Fraud detections. Data science is used to identify suspicious behavior patterns;
5. Natural Language Processing;
6. AI Recommender systems;
7. Anomaly detection.
8. Healthcare informatics.

As you can see, Data Science can be used in any industry. In medicine, it is used to make sense of large amounts of data, extract insights and make the right decisions.

Data engineering and big data refer to a huge array of structured and unstructured information, as well as methods of processing and analysis. Data engineering and big data are increasingly used in various industries, including medicine, to collect, store, transform, query, and analyze large amounts of data. These technologies are perfect for gaining insights and making decisions, combining data from various sources, and creating a unified data structure for future analysis.

Data engineering and big data are used in multiple industries, including marketing, finance, retail, etc. In healthcare & medicine, data engineering and big data can be used to collect patients’ records, develop personalized treatments, analyze trends in patient health and manage hospital performance.

Voice analysis can be applied to multiple industries, including medicine, blockchain, development, and machine learning. In the blockchain field, speech analysis can help authenticate users, verify identity, and detect malicious activity. Additionally, voice analysis can be used to verify that a user is who they claim to be. In the development space, speech analysis is used to detect user intent and import user experience. Speech analysis is also widely used in machine learning to train models and enhance the accuracy of predictions.

In the medical space, speech analysis is used to detect various diseases and diagnose medical conditions. For instance, speech analysis can be used to identify changes in the patient’s voice connected to various respiratory infections or to monitor changes in speech patterns that could indicate the presence of neurological disorders. In addition, voice analysis is useful for tracking the effectiveness of treatment.

MLOps is a set of practices for comprehensive and automated lifecycle management of machine learning systems widely used for medicine, blockchain, development, etc.

The main contribution of MLOps to medicine is developing automated medical diagnosis systems that help extract actionable insights. For instance, pathologists can turn patient data into accurate predictions and build predictive models for various diseases. Generally, MLOps are used in medicine for disease identification & diagnosis, disease prediction, drug discovery & manufacturing, medical imaging, smart health records, and personalized treatment.