AI in medicine – the greatest revolution of the 21st century?

Maciej Zieliński

23 Jan 2021
AI in medicine – the greatest revolution of the 21st century?

Will artificial intelligence change the face of modern medicine? All signs point to yes, and sooner than we might expect. 

A key feature of tools based on artificial intelligence technology is the ability to recognise relevant relationships in raw data. It turns out that this ability can be used in virtually all areas of medicine. Already, AI technology is enabling medical staff to tackle problems that are time-consuming and inefficient to solve on their own. On the Nextrope blog, we present what we believe are the 3 most interesting applications of AI in medicine.

 

Sorting out information chaos 

Dozens of clinical trials are published daily. Many experts and doctors agree that in such a plethora of data, it becomes increasingly difficult to select those relevant to the case of a particular patient. As in many other fields, also in medicine the demand for tools that will assist humans in combing through information seems to be higher than ever. This is where artificial intelligence comes to the rescue.

AI technologies allow doctors to find information in unstructured medical literature in a faster and more efficient way. This makes it easier for them to keep up with scientific developments, update procedures and make more accurate decisions about patient treatment. An example of such Technology is IBM Watson, used by doctors and researchers to extract valuable insights from millions of health reports, patient data records, clinical trials and medical journals.

Medical imaging 

Even tests carried out using the best quality equipment are always subject to the risk of observer error if the data acquired is analysed solely by a human. Such an error can be extremely costly for both patient and doctor. The use of AI algorithms can provide a much smaller margin of error than standard solutions. Machines are simply much more accurate than humans, faster and do not make mistakes resulting from, for example, fatigue. 

Recent studies, such as tests of an algorithm created by Google to detect early stages of breast cancer, show that artificial intelligence solutions are able to achieve results more than 50% better than traditional methods. Over the past few years, several solutions using AI in diagnostic imaging have hit the market. These include CT analysis tools from NVIDIAand Siemens, and Aidoc'sX-ray analysis software, which is already used in more than 100 hospitals worldwide.

Cancer diagnostics

In an effort to enable early diagnosis of various types of cancer, a Romanian start-up has developed the highly accurate AI-MICADIStool. Instead of the circulating DNA molecules or tumour cells commonly used for this purpose, it focuses on examining circulating microRNA molecules. It uses AI algorithms to analyse polymerase chain reaction (PCR), microarray and next-generation sequencing (NGS) data, which are important factors in detecting the earliest stages of cancer development. "By selecting relevant molecular alterations and using artificial intelligence, we have created the best available non-invasive method for diagnosis and early detection of many types of cancer" - explains Alexandru Floares, AI-MICADIS project coordinator. 

The tool is now able to not only detect 13 types of cancer, but also distinguish between malignant and benign forms. "Ultimately, we achieved a 99% accuracy rate and equipped the tool with the ability to distinguish between malignant and benign forms of the disease, "says Floares, "Furthermore, we have demonstrated that using data analytics and artificial intelligence to combine different tests outperforms a single large-scale test in terms of cost, efficiency and results."

AI in medicine - opportunities for development?

The use of AI in medicine has become a reality, not just an optimistic forecast for the future. Unfortunately, it will still be years before artificial intelligence is widely used in every hospital in the world.

 However, everything indicates that in the near future we can expect big increases in the market for AI solutions in medicine. According to the Yole report, its cumulative annual growth rate for 2020-2025 will be as high as 36%, making it one of the fastest growing markets in the IT sector. According to the company's analysts, its total value is expected to exceed $2.7 billion in 2025. Maybe a visit to a robot doctor isn't such a distant prospect after all?

Want to know more about using the latest IT technologies in medicine? Check out our article on howBlockchain is supporting the development of healthcare. And if you are interested in prospective areas of implementation of artificial intelligence algorithms, readour article on AI in education.

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Master UI Component Creation with AI: The Ultimate Guide for Developers

Gracjan Prusik

24 Mar 2025
Master UI Component Creation with AI: The Ultimate Guide for Developers

Introduction

Modern frontend development is evolving rapidly, and creating UI components with AI tools is helping developers save time while enhancing interface quality. With AI, we can not only speed up the creation of UI components but also improve their quality, optimize styles, and ensure better accessibility.

This article explores how creating UI components with AI is transforming frontend development by saving time and improving workflows. Specifically, we will discuss:

  • Generating components from images,
  • AI for style analysis and optimization,
  • Automatic style conversion and code migration,
  • AI in generating UI animations.

Creating UI Components with AI from Images

One of the interesting applications of AI in frontend development is the ability to generate components from an image. AI can recognize the structure of the interface and generate HTML/CSS or JSX code that matches the provided image. One of the most popular tools for UI creation is Lovable.

For testing, let's see how the tool performs in creating a simple contact form from the page you are currently on, which is Nextrope.

Query:

"Recreate the image I've sent you in Next.js using CSS."

Sample image used in the query:

The result received:

Tworzenie komponentów UI - Wygenerowany widok

Lovable did an excellent job transforming the image into code. The view is fully responsive. It’s important to remember that the more precise the request, the better the AI will replicate the expected view. However, even with a simple command, the tool performed surprisingly well.

Of course, AI has its limitations. It still makes quite a few mistakes. The generated code still requires review and fixes from the developer, but in some cases, the entire process is significantly sped up—correcting the code often takes less time than creating the component from scratch.

Optimizing UI Components with AI Tools for Style Improvements

This problem of inaccurate code reproduction by AI can be partially addressed by using it to analyze and improve styles. Tools like ChatGPT, DeepSeek and Claude are capable of not only generating code but also diagnosing CSS errors and suggesting why a particular style might not be working as expected.

Simple example: Why is the div not centered?

Query: "Why is this div not centered?"

AI analyzes the code and provides the following response:

Problem: The parent container does not have a defined width or display: flex.
Solution: Add the appropriate styles to the parent container.

.parent {
  display: flex;
  justify-content: center;
  align-items: center;
  width: 100vw;
  height: 100vh;
}

AI for Automatic Style Conversion and Code Migration in UI Components

AI can assist with style conversion between different technologies, such as transferring code from traditional CSS to Styled Components or Tailwind CSS.

Let's assume we have a style written in traditional CSS:

.button {
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;
}

.button:hover {
  background-color: darkblue;
}

We can use AI for automatic conversion to Styled Components:

import styled from "styled-components";

const Button = styled.button`
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;

  &:hover {
    background-color: darkblue;
  }
`;

export default Button;

AI can also assist in migrating code between frameworks, such as from React to Vue or from CSS to Tailwind.

This makes style migration easier and faster.

How AI Enhances UI Animation Creation

Animations are crucial for enhancing user experience in interfaces, but they are not always provided in the project specification. In such cases, developers have to come up with how the animations should look, which can be time-consuming and require significant creativity. AI, in this context, becomes helpful because it can automatically generate CSS animations or animations using libraries like Framer Motion, saving both time and effort.

Example: Automatically Generated Button Animation

Suppose we need to add a subtle scaling animation to a button but don't have a ready-made animation design. Instead of creating it from scratch, AI can generate the code that meets our needs.

Code generated by AI:

import { motion } from "framer-motion";

const AnimatedButton = () => (
  <motion.button
    whileHover={{ scale: 1.1 }}
    whileTap={{ scale: 0.9 }}
    className="bg-blue-500 text-white px-4 py-2 rounded-lg"
  >
    Press me
  </motion.button>
);

In this way, AI accelerates the animation creation process, providing developers with a simple and quick option to achieve the desired effect without the need to manually design animations from scratch.

Summary

AI significantly accelerates the creation of UI components. We can generate ready-made components from images, optimize styles, transform code between technologies, and create animations in just a few seconds. Tools like ChatGPT, DeepSeek, Claude and Lovable are a huge help for frontend developers, enabling faster and more efficient work.

In the next part of the series, we will take a look at:

If you want to learn more about how AI is impacting the entire automation of frontend processes and changing the role of developers, check out our blog article: AI in Frontend Automation – How It's Changing the Developer's Job?

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AI in Real Estate: How Does It Support the Housing Market?

Miłosz Mach

18 Mar 2025
AI in Real Estate: How Does It Support the Housing Market?

The digital transformation is reshaping numerous sectors of the economy, and real estate is no exception. By 2025, AI will no longer be a mere gadget but a powerful tool that facilitates customer interactions, streamlines decision-making processes, and optimizes sales operations. Simultaneously, blockchain technology ensures security, transparency, and scalability in transactions. With this article, we launch a series of publications exploring AI in business, focusing today on the application of artificial intelligence within the real estate industry.

AI vs. Tradition: Key Implementations of AI in Real Estate

Designing, selling, and managing properties—traditional methods are increasingly giving way to data-driven decision-making.

Breakthroughs in Customer Service

AI-powered chatbots and virtual assistants are revolutionizing how companies interact with their customers. These tools handle hundreds of inquiries simultaneously, personalize offers, and guide clients through the purchasing process. Implementing AI agents can lead to higher-quality leads for developers and automate responses to most standard customer queries. However, technical challenges in deploying such systems include:

  • Integration with existing real estate databases: Chatbots must have access to up-to-date listings, prices, and availability.
  • Personalization of communication: Systems must adapt their interactions to individual customer needs.
  • Management of industry-specific knowledge: Chatbots require specialized expertise about local real estate markets.

Advanced Data Analysis

Cognitive AI systems utilize deep learning to analyze complex relationships within the real estate market, such as macroeconomic trends, local zoning plans, and user behavior on social media platforms. Deploying such solutions necessitates:

  • Collecting high-quality historical data.
  • Building infrastructure for real-time data processing.
  • Developing appropriate machine learning models.
  • Continuously monitoring and updating models based on new data.

Intelligent Design

Generative artificial intelligence is revolutionizing architectural design. These advanced algorithms can produce dozens of building design variants that account for site constraints, legal requirements, energy efficiency considerations, and aesthetic preferences.

Optimizing Building Energy Efficiency

Smart building management systems (BMS) leverage AI to optimize energy consumption while maintaining resident comfort. Reinforcement learning algorithms analyze data from temperature, humidity, and air quality sensors to adjust heating, cooling, and ventilation parameters effectively.

Integration of AI with Blockchain in Real Estate

The convergence of AI with blockchain technology opens up new possibilities for the real estate sector. Blockchain is a distributed database where information is stored in immutable "blocks." It ensures transaction security and data transparency while AI analyzes these data points to derive actionable insights. In practice, this means that ownership histories, all transactions, and property modifications are recorded in an unalterable format, with AI aiding in interpreting these records and informing decision-making processes.

AI has the potential to bring significant value to the real estate sector—estimated between $110 billion and $180 billion by experts at McKinsey & Company.

Key development directions over the coming years include:

  • Autonomous negotiation systems: AI agents equipped with game theory strategies capable of conducting complex negotiations.
  • AI in urban planning: Algorithms designed to plan city development and optimize spatial allocation.
  • Property tokenization: Leveraging blockchain technology to divide properties into digital tokens that enable fractional investment opportunities.

Conclusion

For companies today, the question is no longer "if" but "how" to implement AI to maximize benefits and enhance competitiveness. A strategic approach begins with identifying specific business challenges followed by selecting appropriate technologies.

What values could AI potentially bring to your organization?
  • Reduction of operational costs through automation
  • Enhanced customer experience and shorter transaction times
  • Increased accuracy in forecasts and valuations, minimizing business risks
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Want to implement AI in your real estate business?

Nextrope specializes in implementing AI and blockchain solutions tailored to specific business needs. Our expertise allows us to:

  • Create intelligent chatbots that serve customers 24/7
  • Implement analytical systems for property valuation
  • Build secure blockchain solutions for real estate transactions
Schedule a free consultation

Or check out other articles from the "AI in Business" series