Artificial intelligence (AI) has influenced and changed many areas of our lives in recent years. The availability of powerful language models with easy-to-use chat interfaces - such as ChatGPT - or image generation tools - such as Midjourney or Stable Diffusion - has finally brought the topic of artificial intelligence to the attention of the general public in recent months and triggered a wide range of discussions about the possibilities, benefits and risks.
The fields of application of AI-based software are far more diverse than the current public discussion, which mainly revolves around generative AI tools, would suggest. As we at sepp.med deal intensively with the topic of "Future Healthcare", the possible applications of AI in medicine and medical technology are of particular interest to us.
Whether in research, diagnostics or in practical applications as support for medical professionals - the possibilities and areas of application for artificial intelligence in the medical environment are diverse. In this series of articles, we will take a closer look at various exciting aspects of the use of artificial intelligence, particularly in the form of machine learning, as part of medical systems.
Diagnostic imaging is a fundamental part of modern medicine and includes various techniques such as X-ray, magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound. These techniques provide detailed images of the internal structures of the body, which are essential for the diagnosis and treatment of diseases. AI-based software for analyzing the data has the potential to further improve these techniques and significantly increase the accuracy and speed of diagnoses.
One concrete example is the detection of lung cancer in CT images. Lung cancer is one of the most common types of cancer worldwide and often leads to a high mortality rate, especially if it is detected late. AI systems can help in the early detection of lung cancer by recognizing patterns and anomalies in CT images that are difficult for the human eye to see. Early diagnosis gives patients a better chance of successful treatment and recovery.
The automatic detection of strokes in MRI images is another example. Strokes are one of the main causes of disability and death worldwide. Rapid diagnosis and treatment of strokes is crucial to minimize permanent damage. AI-based diagnostic software can contribute to faster and more accurate detection of strokes by recognizing patterns in MRI images that indicate a stroke. This helps doctors to quickly initiate the right treatment and improve the prognosis for those affected.
Personalized medicine, also known as precision medicine, is an innovative approach to healthcare that aims to develop a tailored therapy for each patient. Artificial intelligence can be used in the personalization of drugs and therapeutic approaches to identify and analyze complex patterns in genomic and health data. Such an analysis forms the basis for individual adaptation to individual patients.
The development of new drugs is a lengthy and cost-intensive process that often takes many years. The use of artificial intelligence can significantly accelerate research and at the same time increase the success rate in identifying promising new active ingredients. To this end, AI tools analyze large amounts of data from different sources to identify patterns and correlations that are crucial for the identification of new active ingredients and their development.
The identification of the active ingredient Halicin as a potential broad-spectrum antibiotic is a vivid example of the use of AI in drug development. Halicin was actually developed for the treatment of diabetes, but never came onto the market in this function. In 2019, however, researchers at the Massachusetts Institute of Technology (MIT) identified Halicin as a promising candidate for the treatment of infections caused by antibiotic-resistant bacteria. It is believed to be the first antibiotic discovered through the use of artificial intelligence - deep learning, to be precise - without premises. The AI system searched through thousands of compounds and identified Halicin within a few days, whereas conventional methods of research would have taken months or years.
AI-assisted drug discovery can be done in different ways. One method is so-called "in-silico drug discovery", in which machine learning algorithms search virtual libraries of chemical compounds to identify promising candidates for the development of new drugs. The AI software is able to recognize patterns and correlations that indicate the compounds' potential efficacy in certain diseases. This process was also used in the halicin example described above.
Another area of application is the use of artificial intelligence in research to optimize active ingredients, for example to improve their solubility, absorption or metabolism in the body. This can help to bring new drugs through clinical trials and ultimately to approval more quickly.
Virtual health assistants can help with self-assessment of symptoms, for example, by first asking a patient a series of questions based on current medical knowledge. AI algorithms are then used to determine possible causes for the symptoms that could be derived from the user's answers. The app then makes a recommendation as to whether medical advice should be sought or whether self-help measures are sufficient. In this context, it is important to emphasize that such virtual health assistants do not replace a medical diagnosis, but merely serve as a source of information and a decision-making aid.
Virtual health assistants can also help to improve the management of chronic diseases. For example, a virtual health assistant can help diabetics to monitor their blood glucose levels and insulin dose, remind them to take their medication and give them tips on healthy eating and physical activity. In addition, virtual health assistants can collect and analyze data about the patient's health status to provide individualized recommendations to improve quality of life and disease management.
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