AI in medicine – areas of application and future prospects

What is artificial intelligence?

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

The purpose of AI is to solve complex problems, make human tasks easier and enable innovative solutions in various sectors - 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?

The terms machine learning, big data, neural networks and deep learning keep cropping up in connection with AI. All areas are interlinked 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 different 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 volumes 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 using large 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 the 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 image and speech recognition, for example.

What areas of application are there for AI in medicine?

The applications of AI in medicine extend across 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: Personalized interactions, automated administrative tasks and more efficient diagnosis and treatment methods can increase patient satisfaction and optimize communication between patients and medical staff .
• 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 interventions and a faster recovery for patients.
• Imaging and diagnostics: AI is revolutionizing medical imaging and diagnostics through the automated analysis of radiological images, CT scans and MRI scans. Algorithms can detect abnormalities and patterns faster and more accurately, leading to earlier diagnoses and more effective treatment plans.
• Drug development: The use of AI in drug research accelerates the development process of new drugs. By analyzing large data sets, potential active ingredients can be identified, clinical trials can be optimized and drug development can be made more efficient.
• Automated resource management: AI can play a key role in automated resource management in the healthcare sector. This includes the efficient allocation of staff, beds and medical resources based on real-time data to optimize patient flow and increase the quality of care.
• Predictive maintenance of medical devices: 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 the continuous monitoring of health parameters and the collection of real-time data. AI algorithms could analyse this data to detect early signs of illness, identify individual health trends and provide personalized recommendations for a healthier life.

Examples of AI in medicine

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

AI is currently being used particularly successfully in diagnostic imaging. For example, deep learning methods can be used to detect up to 10 % more cases of bowel cancer can be detected. AI also helps to predict possible heart disease at an early stage by evaluating electrocardiograms. Dermatologists also use AI, for example for the automated detection of skin changes based on photo documentation.

Pfizer provides an example of the use of machine learning for drug development. During the COVID-19 pandemic, millions of data points were automatically analyzed to find a drug that best matches the corresponding target molecule in the human body.

What challenges does AI pose?

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 are:

• Data protection and ethical issues: The processing of sensitive patient data requires strict security precautions to ensure the protection of 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 the seamless integration of different AI applications and platforms. This makes it difficult to exchange data and information between different applications and medical facilities.‍
• Liability issues in the event of errors: It is not always clear who bears responsibility if an AI algorithm draws incorrect conclusions or delivers 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. Nevertheless, there is still a lot of potential for optimization. Modern solutions promise significantly more powerful systems in the future.

The scientific journal Nature published a study in 2023 that 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 small child understands the instructions "walk forwards" and "hop backwards", it will also understand "hop forwards" and "walk backwards". Until now, this was not so easy for machines. With the new method, the researchers were able to achieve results that far surpass previous models. These previously relied on a more casual acquisition of these skills.

In the field of medical technology quantum computing is particularly promising. This technology enables significantly more compact data packages and much more efficient calculations. This is particularly interesting for diagnostics, where large amounts of data need to be analyzed.

Conclusion: AI is revolutionizing medicine

Just like in many other industries, AI promises to change things in medicine too. This is particularly evident in the large number of new applications that now rely on artificial intelligence. The advance of digital health has opened up new opportunities for the integration of artificial intelligence.

A balanced interplay of technological progress, ethical guidelines and legal standards is crucial to ensure responsible treatment. Artificial intelligence not only makes it possible to increase efficiency in diagnosis and therapy, but also holds out the prospect of transforming patient care as a whole.

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The personal designations used in this article always refer equally to all persons. For the sake of better readability, we have refrained from using double or opposite names.