AI in medicine — areas of application and future prospects

What is artificial intelligence?

Artificial intelligence describes the ability of machines to human competencies such as logical thinking, learning, planning and creativity to imitate. To do this, AI systems use machine learning algorithms and models. Large amounts of input data (big data) serve as a basis for learning and sorting.

The purpose of AI is to solve complex problems to facilitate human tasks and to enable innovative solutions in various industries — including medicine. The complexity ranges from simple automated data evaluation to the simulation of human-like thinking.

What are machine learning, big data and neural networks?

In connection with AI, the terms machine learning, big data and neural networks or deep learning come up again and again. All areas are intertwined and are also used in medicine, but what do they mean in detail?

• Machine learning: Machine learning enables computers to learn from data and gain experience without explicit programming. There are various types of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. The aim is to develop algorithms that can recognize patterns and make predictions based on the information learned.
• Big data: This term describes large amounts of structured and unstructured data that are difficult to process due to their size and complexity. Big data is often used in conjunction with machine learning to train models based on comprehensive data sets and then enable patterns to be recognized.
• Neural networks: Deep learning is based on neural networks. Inspired by the human brain, many intermediate layers are used between the input layer and output layer of a system. These networks can learn complex patterns and representations by automatically extracting hierarchical features from the data. Deep learning is used, for example, for image and speech recognition.

What are the areas of application for AI in medicine?

The applications of AI in medicine span various areas — some are still being researched, others are already being used successfully. An overview of some of the most promising areas of application:

• Optimizing the patient experience: Through personalized interactions, automated administrative tasks, and more efficient diagnosis and treatment methods, patient satisfaction can be increased and Communication between patients and healthcare professionals be optimized.
• Robotics in medicine: Robotics in medicine uses AI-controlled systems to perform precise surgical procedures and support complex tasks in the operating room. Robot-assisted surgery enables more precise incisions, smaller procedures, and faster recovery for patients.
• Imaging and diagnostics: AI is revolutionizing medical imaging and diagnostics by automating the analysis of radiological images, CT scans, and MRI images. Algorithms can identify deviations and patterns faster and more accurately, leading to earlier diagnoses and more effective treatment plans.
• Drug development: The application of AI in drug research accelerates the development process of new drugs. By analyzing large data sets, potential drugs can be identified, clinical trials optimized and drug development made more efficient.
• Automated resource management: AI can play a key role in automated resource management in healthcare. This includes efficiently allocating staff, beds, and medical resources based on real-time data to optimize patient flow and improve the quality of care.
• Predictive medical device maintenance: AI-based predictive maintenance systems can continuously monitor medical devices to predict potential failures and enable preventive maintenance measures.
• Integration of smart health devices: Wearables and sensors enable continuous monitoring of health parameters and the collection of real-time data. AI algorithms could analyze this data to identify early signs of illness, identify individual health trends, and provide personalized recommendations for a healthier life.

Examples of AI in medicine

There is numerous application examples for AI in medicine. With our selection, we would like to provide an insight into the diversity of areas of application.

AI is being used particularly successfully today in imaging diagnostics. This is how deep learning methods can during colonoscopy up to 10% more colorectal cancer cases discovers become. By evaluating electrocardiograms, AI also helps potential heart disease to predict. Dermatologists are also using AI, for example for automated Detecting skin changes based on photo documentation.

An example of using machine learning to develop drugs provides pfizer. During the COVID-19 pandemic Millions of data points were automatically evaluated to find an active ingredient that best matches the corresponding target molecule in the human body.

What are the challenges of AI?

In addition to many advantages and exciting opportunities, artificial intelligence also brings with it a number of challenges that still need to be overcome. Some of the most important include:

• Data protection and ethical issues: Processing sensitive patient data requires strict security measures to ensure privacy. In addition, ethical dilemmas arise in connection with the use of patient data for research purposes and personalized medicine.
• Standardization and interoperability: There is a lack of uniform standards and protocols to ensure seamless integration of various AI applications and platforms. This makes it difficult to exchange data and information between different applications and medical institutions.‍
• Liability issues in case of errors: It is not always clear who is responsible when an AI algorithm draws incorrect conclusions or provides unexpected results. This requires a clear legal and ethical framework to define responsibilities in the context of AI in medicine.

Current findings and future prospects

Artificial intelligence has developed rapidly in recent years and has become part of everyday life since ChatGPT at the latest. Yet remains a lot of optimization potential. Modern solutions promise significantly more powerful systems in the future.

In 2023, the scientific journal Nature has a study published, which presents a new learning method for AI — Meta-Learning for Compositionality (MLC). Neural networks are specifically trained for compositional generalization — the ability to abstract.

For example, if a toddler understands the instructions “run forward” and “jump backward,” it also understands “jump forward” and “run backward.” Until now, this has not been so easy for machines. With the new method, the researchers were able to achieve results that far surpass previous models. Until now, they have relied on a more casual acquisition of these skills.

In the area of Medical technology is that quantum computing particularly promising. This technology enables significantly more compact data packages and significantly more efficient calculations. This is particularly interesting for diagnostics, which require the evaluation of large amounts of data.

Conclusion: AI is revolutionizing medicine

Just as in many other industries, AI also promises to change a lot in medicine. This is particularly evident in the large number of new applications that now rely on artificial intelligence. With the advance of Digital Health new opportunities have opened up for the integration of artificial intelligence.

A balanced combination of technological progress, ethical guidelines and legal standards is crucial in order to achieve a responsible handling to ensure. Artificial intelligence not only enables an increase in efficiency in diagnosis and therapy, but also provides a Transforming entire patient care in prospect.

The personal names used in this article always refer equally to all persons. Dual naming and alternate names are omitted in order to improve readability.