What to expect in AI and machine learning in 2021?

The medicine
Cancer diagnostics
Pathologists are using AI to diagnose cancer more accurately. Data on different types of cancer are used to create a predictive model. For example, PathAI technology is used for this.

Creation of medicines
Creating new drugs is time-consuming because many tests have to be done to find a useful formula. AI helps with this. Atomwise is one example of a technology that enables the discovery of new molecules. It is being used to develop new drugs for 27 diseases in collaboration with Harvard and Stanford Universities and pharmaceutical companies.

Optimized handling of patient data
The number of patients around the world is growing daily. Automated systems are required to process their disease data. AI enables healthcare providers to optimize this processing. For example, OLIVE is a platform for automating healthcare tasks.

Finance
Money is going digital. The total amount of digital transactions is $ 4.4 million, and could be over 8 million by 2024.

AI-powered data on these transactions could help improve the financial industry in 2021. For example, Dataminr collects information from various text sources and presents the user with a graph of important events. Neural networks allow him to analyze text data.

Trading
Machine learning algorithms can be used for automated trading. In theory, given information about prices, volumes, dates, public sentiment (or weather), you can create a system that will beat the market (but this is not so easy). The algorithm can learn and adapt to changes in real time to make predictions more accurate. For example, Kayrros is a data analytics company to help market participants invest.

Fraud detection
Digital payments have certain risks. In 2018, $ 24.26 billion was lost due to scammers. Machine learning is ideal for dealing with them. The British company AimBrain uses machine learning to prevent account theft and detect fraudulent accounts.

The model can use training data to label each operation (suspicious or not). Then, using the metrics of accuracy and completeness, we can adjust the model for our risk profile by analyzing the costs of false positive and false negative predictions.

Banking
Banks use machine learning for customer service, risk prediction, risk prevention, and investment. Let’s say you can offer personalized offers based on a user’s financial behavior. Thus, if a client is looking for a home, they can make a special offer. Envestnet is a financial data collection and analysis company that provides financial management services.