Artificial intelligence in medicine: how AI is revolutionizing treatment

The future is here, so get ready. Today, artificial intelligence is already sitting next to doctors—not literally, of course, but on a monitor screen, helping them: it reads CT scans better than humans, finds things that might escape the eye, makes suggestions, predictions, and analyses. And it does this not “by the book,” but based on the individual patient: their tests, symptoms, DNA, and lifestyle.

But! Important: AI is not a substitute for doctors. It is an enhancement for doctors. It does not replace the person in the white coat, but works alongside them. It does not take away work, but frees up hands. So that doctors are not administrators with stethoscopes, but those who truly treat and communicate, rather than fighting with paperwork.

In this article, we explore how AI has already made its way into medicine in earnest. What it can do, where it helps, and why neural networks are a real support to those who save lives every day. We discuss this with IT expert and founder of KISS Software, Yevhen Kasyanenko.

How is artificial intelligence used in medicine?

“It’s important to understand right away that AI doesn’t replace doctors, it helps them. It works as a reliable assistant: it speeds up diagnosis, suggests what to expect next, and helps choose the right treatment for a specific person, not just a standard one,” says our expert.

More on all this below.

AI in disease diagnosis

Where the human eye may “miss” a barely noticeable deviation, a neural network is capable of picking up on the smallest detail. And the point here is not to replace the doctor, but to ensure that nothing important is overlooked — especially when dealing with dozens of tests a day.

Personalized treatment

Neural networks can process huge amounts of data in seconds, and this applies not only to medical articles, but also, for example, to your test results.

Neural networks are most effective in the following areas:

• Genomic analysis. AI quickly decodes DNA data. It selects medications based on this data—with minimal side effects and maximum effectiveness.
• Predicting the body’s response. Analysis of medical history and quick comparison with millions of other cases in the database takes just a few minutes. This helps to avoid negative reactions to treatment.
• Assisting doctors. Neural networks are familiar with all current clinical studies and protocols and can help doctors make decisions based on them.

Working with AI makes doctors’ jobs much easier and faster, but it is still a living person who leads the process, which is important.

Drug development and clinical trials

Neural networks are radically changing the process of creating new drugs. What used to take months or years can now be done in minutes. AI makes processes easier and cheaper:

• It looks for chemical interactions that might work. Neural networks are really good at digging through tons of chemical data and finding combinations of substances that humans would never even think of. Sometimes these are unexpected but promising formulas that can help fight diseases. And all this is not done at random, but with a forecast: this may work, here are the risks.
• It helps in clinical trials. AI sees things that are difficult for humans to notice. For example, it can detect subtle dependencies: who the drug works on and how, where the effect may be, and where it may just be a coincidence. As a result, it helps to draw conclusions faster and more accurately.
• It predicts side effects even before the trials begin. This is pure magic, bordering on science fiction. The neural network “re-reads” all the medical experience stored in its memory and can say in advance: “There may be problems here.” Before the drug reaches people. This not only saves millions, but also saves lives.

“In the past, finding the right medicine required a lot of trial and error. Now, AI really shortens the process. It immediately weeds out options that are known to be ineffective and selects those that have a chance of success: effective, with minimal side effects. Fewer attempts – more results,” says Yevhen Kasyanenko.

The use of artificial intelligence in medicine: examples

In advanced clinics, doctors are no longer alone—neural networks work alongside them. These digital assistants help recognize complex diagnoses, suggest effective treatments, take on a lot of paperwork, and even enter operating rooms. Yes, artificial intelligence is already being used in surgery, making it more accurate than ever.

Sounds impressive? Now we will show you specific examples of how it all works in practice and why such technologies are the future.

Computer vision in diagnostics and oncology

AI is particularly effective in oncology. Here, delay can be fatal, so neural networks are becoming indispensable assistants because they:

• Detect oncological and cardiological pathologies. By analyzing images and ECG data, AI identifies tumors and the risk of heart attack and stroke.
• Perform genetic analysis of neoplasms. A neural network can quickly decode tumor DNA. Thanks to this, doctors can prescribe the right treatment earlier.
• Analyze medical data. The workload on doctors is reduced, and the effectiveness of treatment increases. In addition, the human factor and any errors are reduced to zero.

“Sometimes everything depends on a single detail in an image. The human eye may simply not notice it—due to fatigue, haste, or because it is too subtle. But computer vision does. A neural network will pick up on what is missing and tell the doctor, ‘Look here’. It is precisely these technologies that are really changing the rules of the game in cancer diagnosis today. And yes, they are the ones that can bring us closer to creating a real cure. Maybe even much sooner than we think,”says Yevhen Kasyanenko.

Algorithms for the functioning of artificial intelligence in medicine

The main superpower of AI in medicine is not only speed, but the ability to work with insane amounts of information. Where a person would simply drown in numbers, graphs, images, and analyses, a neural network, on the contrary, accelerates. It extracts the most important information, finds patterns that are not obvious, and can even predict how a disease will develop. Our brain is not capable of doing this; it is not designed for such a flow of information, but AI can do it, and it does it on the fly.

Machine learning and neural networks

The high efficiency of neural networks is due to their training. To do this, AI is “fed” thousands of complex medical cases. This opens up new possibilities:

• Detecting rare diseases. Like Dr. House from the famous TV series, AI sorts through symptoms and compares them with all medical databases. The only difference is that a neural network does this almost instantly.
• Predicting complications. Artificial intelligence knows and always remembers the entire history of the patient’s illness. It adjusts the therapy accordingly, taking into account all possible risks.
• Optimizing treatment. AI looks at what works best for a specific diagnosis and selects the treatment that is most suitable for the current patient.

Computer vision and image analysis

Artificial intelligence is capable of detecting the slightest deviations from the norm, for example:

• Real-time detection of pathologies. Right during the examination, AI integrated into diagnostic equipment is capable of analyzing the situation.
• X-ray analysis. Dentistry, surgery, and therapy are becoming easier and faster
• Determining the stage of the disease. With the help of AI, it is possible to more accurately determine the stage of the disease. Accordingly, it is possible to select an effective treatment.

Natural language processing

Language models are also used in medicine. They can analyze and generate medical records:

• Study medical history. Analyzing the patient’s entire history of interactions with medications and identifying risk factors. 
• Assisting in diagnosis. Comparing patient complaints and symptoms with reference books and medical protocols.
• Filling out medical documentation. The NLP system can take over routine paperwork, freeing up doctors’ time for actual treatment.

Artificial intelligence in medicine: pros and cons

Many people still view AI with caution, especially when it comes to health. Really, entrusting a machine with something that life depends on? It’s risky.

Yes, AI in medicine is a powerful tool, offering new opportunities and a huge advantage for doctors. But with it come risks: algorithm errors, incomplete data, ethics, and security. It is also important to talk about this — without panic, but honestly.

The advantages of neural networks in medicine

Summarizing the previous sections, the strengths of AI are:

• Acceleration of medical work and personalization of treatment. Patient data is analyzed and compared with medical databases in seconds.
• Increased accuracy and avoidance of errors. Algorithms are not subject to fatigue and see things that humans are unable to notice.
• Optimization of research costs. Drug development is accelerated and automated. This reduces costs for pharmaceutical companies.

Limitations and risks of using artificial intelligence algorithms in medicine

But not everything is so rosy. On the other side of the scale are equally important issues:

• Data bias and algorithm errors. AI training is based on real data. If an error or inaccuracy creeps in somewhere, it can affect the entire operation of the algorithm.
• Confidentiality and data security. Medical databases store sensitive personal information about everyone. Inadequate protection of such databases can be fatal.
• Implementation and regulation in clinical practice. Legislation does not fully understand what to do with AI. Certification mechanisms have not been worked out. Most medical personnel need additional training to work with neural networks.

“An important issue that is constantly discussed in connection with AI is the ethics of using personal information to train neural networks. This issue has not yet been fully resolved, but I am confident that all factors hindering the adoption of AI in medicine will soon be overcome,” notes our expert.

The future of artificial intelligence in medicine

Like our leader, the KISS Software team is confident that all the difficulties are temporary and related to the novelty of AI as a phenomenon. At the same time, the advantages are real and will only develop along with neural networks. New horizons are already emerging. Judge for yourself…

Genomics and AI: personalized medicine

The human genetic code is a huge database of everything. And a neural network can decode it in seconds. This opens up new possibilities:

• Predicting diseases. DNA encodes genetic predispositions to diseases such as cancer, diabetes, and autoimmune diseases.
• Individualized treatment. Based on human genetics, AI determines which drugs are more effective and have fewer side effects.
• New methods of therapy. AI instantly compares the mechanisms of action of various drugs and the specifics of diseases. It is capable of immediately finding more effective solutions.

In addition, the neural network database is constantly expanding. This increases the accuracy and range of their applications.

AI and robotics in surgery

Robots do not have shaky hands, they do not get tired after a nine-hour operation, and they are capable of working without eyes. Together with neural networks, robotic surgery is capable of taking a big step forward.

• AI-controlled robotic assistants help surgeons and reduce the invasiveness of operations.
• AI evaluates patient data before and during surgery. This gives the doctor the optimal strategy for operating on that particular patient.
• The accuracy of robotic assistants reduces the rehabilitation period after surgery. Fewer incisions mean less recovery time.

The use of AI in psychiatry

AI is capable of finding inconsistencies in behavior. Mental illnesses can be detected in their early stages and treated before it is too late.

Here are some of the capabilities of artificial intelligence in this context:

• Speech and behavior analysis. The slightest deviations in speech and facial expressions can indicate depression or other disorders. Patients usually try to hide this, but a neural network cannot be fooled.
• Disease prediction. AI allows doctors to intervene in the disease before serious symptoms appear.
• Psychological support. Artificial intelligence will not judge, will listen to all problems, and will give advice. When it is not possible or desirable to go to a person, a virtual therapist is the ideal choice.

Why work with KISS Software?

Under the leadership of Yevhen Kasyanenko, KISS Software not only develops but also successfully implements artificial intelligence in medicine, helping clinics and hospitals work more efficiently and accurately.

How we help in the development of AI solutions

We already have valuable experience in using neural networks to improve medical processes. We offer:

• Creation of AI algorithms for diagnosis and treatment. Our projects are tailored to the needs and specifics of a particular institution.
• Integration of neural networks into medical platforms and databases. We simplify the work of doctors by transferring data recording and analysis to AI.
• Support at all stages. We work with the customer on the idea, implement it into the system, and adapt it to the situation so that the AI solves the necessary tasks.

Conclusion

It seems that just a couple of years ago, artificial intelligence in medicine sounded incredible and distant. Now it is here: helping doctors, facilitating the work of clinics, speeding up treatment, and saving time and effort. All of this is real. And it’s not just about technology, but about the fact that it is starting to work for people.

If you are thinking about implementing AI in your clinic or project, you don’t have to jump in head first. You can simply discuss and understand what really works and what is just hype. The KISS team is already at work and ready to help. We develop medical AI applications that prioritize quality, safety, and real benefits. Everything is simple, transparent, and focused on results.