AI in medicine: Revolutionary progress in the Ostalbkreis!

AI in medicine: Revolutionary progress in the Ostalbkreis!

Artificial intelligence (AI) has found its way into medicine in the Ostalbkreis, with radiology in particular benefiting from the advanced technologies. Dr. Martin Kolb, an expert in the field, explains that AI is being used to more precisely detect small, potentially malignant lung nodules. In a typical radiology practice, 15 to 16 CT scans of the lungs are performed daily. AI pre-sorts hundreds of these CT images and is able to successfully identify four out of five suspicious nodules.

In addition, AI is used not only in radiology, but also in gastroenterology at the Ostalb Clinic to improve diagnoses. Chief physician Dr. Stefan Gölder has implemented an AI system that has been detecting abnormalities in colonoscopies for around two years. As reported, AI not only helps doctors make early diagnoses, but also improves the accuracy of diagnoses in around 20 percent of cases. These technologies also play an important role in training medical staff.

Assistive technology and human responsibility

Despite the impressive advances made possible by AI in medicine, humans remain irreplaceable. AI is seen as a valuable tool that supports, but does not replace, physician decisions. The final assessment and treatment decisions remain the responsibility of the doctors. The “My Doctor, the AI ​​and I” project at the Hannover Medical School has developed clear recommendations for dealing with AI. These recommendations state that patients should understand how AI works and educate themselves about data protection. Doctors, on the other hand, are required to take responsibility for the decisions made by the AI ​​and to explain how it works transparently.

According to that Medical Journal Radiology is an ideal field of application for AI, especially due to its core competencies in image processing, detection and classification. Over 700 AI-based software products are now approved in this area, making radiology a pioneer in the application of medical AI. However, the actual use of these technologies is still low.

Challenges for AI use

Integrating AI into radiology faces several challenges. In addition to financing, the focus is primarily on prospective validation of the benefits for patient care and integration into existing process and IT infrastructures. There are also increasing regulatory requirements that make the introduction of AI in healthcare more complicated. The Swabian Post emphasizes that these challenges raise serious concerns about human-machine interaction and that in-depth research is needed in this area.

The integration of AI into medical training, further education and training is essential to counteract the risk of so-called “automation bias”. The questions regarding reimbursement of investments in AI technologies have not yet been clarified and represent a further hurdle. These aspects are crucial to ensure the safety and effectiveness of AI in a medical context and to promote a more transparent approach to the new technologies.