Artificial intelligence in medicine: from digital transformation to real results

AI technologies in medicine are no longer perceived as something new—they have become an important part of modern healthcare. It is no longer just a buzzword, but a tool that helps medical institutions and companies in related industries improve the quality of diagnostics, optimize processes, and save specialists’ time. The use of artificial intelligence directly affects the speed and level of care provided — and therefore the lives of millions of people.

The use of AI in medicine: why it is already part of everyday practice

Whereas artificial intelligence used to be associated primarily with fantastical robots or mysterious algorithms, today it is a technology with measurable results and proven reliability. It is capable of processing large amounts of data that would be difficult for a human to analyze manually in a short period of time.

It helps automate routine processes, from the initial processing of laboratory tests to the analysis of medical images and support in making diagnoses. The volume of medical data is constantly growing, and without automation, accuracy decreases and the workload on specialists increases.

Despite its many advantages, AI is not a replacement for doctors, but a reliable assistant. It can be compared to a digital assistant that takes on repetitive tasks, freeing up specialists’ time for complex and non-standard cases.

Examples of AI implementation in medicine

Artificial intelligence is already actively used to analyze medical images, support decision-making, and work with large amounts of data. This simplifies diagnostics, speeds up information processing, and reduces the workload on staff.

In recent years, large medical and pharmaceutical companies have been increasingly introducing AI into clinical practice and research. For example:

Boston Scientific, in collaboration with Anumana, is developing an AI-based ECG diagnostic platform and has already begun to expand its functionality to perioperative and acute cardiac scenarios. Its BeatLogic algorithm is also used to analyze arrhythmias and monitor cardiac data, which helps predict exacerbations and optimize treatment.

In a study with LUX-Dx ICM implantable cardiac monitors, their AI system demonstrated accuracy comparable to that of expert electrophysiologists in classifying episodes of atrial arrhythmias.

Philips recently received FDA clearance for its new SmartSpeed Precise AI module, which speeds up MRI scans by up to 3× and enhances image clarity by up to 80%.

SmartSpeed technology has previously been integrated into deep learning and is used to increase the speed and resolution of MRI scans.

Automating test descriptions with AI: our experience

The Nomium team created an AI-based service for the client that integrates into medical processes, automates routine tasks, and frees up human and time resources.

Before implementation, doctors manually uploaded files, compared indicators with norms, generated descriptions, and sent the results to patients. With a high flow of patients, this took a significant amount of time. We proposed a solution that takes over part of the process: the system compares laboratory test results with reference values and draws conclusions based on the patient’s history. The doctor receives the results and validates them.

What the service includes:

• Automatic receipt and upload of laboratory test results directly from files and systems.
• Maintenance of patient history to track changes in indicators.
• Generation of preliminary descriptions using AI.
• Provision of drafts to doctors via Telegram bot for review.
• Recording of doctor feedback to improve the quality of interpretation.
• Sending final results to patients with explanations.

Result: the time from analysis upload to result receipt has been reduced, the workload on doctors has decreased, and patients receive clear and timely feedback.

The implementation of neural networks has a measurable effect:

• The accuracy and standardization of descriptions is improved.
• The information processing cycle is accelerated.
• The management of medical and business processes is improved.
• It reduces the workload on staff.
• It reduces costs through automation.
• The efficiency of organizations increases, and the healthcare system becomes more sustainable.

Prospects for the development of AI in medicine

Artificial intelligence is developing not only as an auxiliary tool for automation, but also as the basis for new directions in healthcare. Its application goes beyond individual tasks and is gradually forming entire practices.

Among the most promising areas are:

• Personalized medicine — the use of genetic and clinical data to select optimal treatment regimens.
• Telemedicine and remote diagnostics — support for online consultations and interpretation of medical tests.
• Robotic surgery — systems that help surgeons perform operations more accurately and safely.
• Intelligent support platforms — solutions for analyzing large data sets and accelerating clinical decision-making.

These approaches are already being used in various countries and are gradually becoming part of everyday medical practice.

Conclusion: artificial intelligence is already changing medicine

It is no longer an experiment but has become part of everyday healthcare practice. Today, it speeds up diagnosis, improves the accuracy of data interpretation, and helps doctors focus on clinical decisions.

Algorithms are already being used to analyze medical images, support diagnoses, and automate routine processes—from laboratory tests to clinical trials. This affects not only the efficiency of medical institutions, but also directly impacts the quality and speed of patient care.

In the coming years, the role of artificial intelligence will only grow, transforming digital medicine from a prospect into a new industry standard.

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At Nomium, we develop and implement solutions based on AI, blockchain, and other modern technologies.