AI in medicine

Artificial intelligence and LLMs in particular are seen by many as a beacon of hope for the overburdened healthcare system. Above all, AI-based automation could quickly provide relief for knowledge management routine tasks. Until that happens, problems with security and safety must be solved and legal requirements fulfilled. Fraunhofer IKS research is addressing both of these issues.

Babies born prematurely, i.e. before their organs have fully developed, often suffer from various health problems, known as morbidities. These rarely manifest alone, but often occur simultaneously. Researching connections or even patterns in their co-occurrence helps to develop more effective and more personalized care for premature babies. A project report.

Regulations and standards for trustworthy AI are in place, and high-risk medical AI systems will be up for audits soon. But how exactly can we translate those high-level rules into technical measures for validating actual code and algorithms? Fraunhofer IKS’s AI verification framework provides a solution.

AI has paved the way for better and faster diagnostics in the realm of medical imaging. However, challenges specific to the field of medicine, notably the scarcity of data, can impede its performance. This article looks into the enhancement of AI algorithms using quantum technology for the improvement of medical diagnostics, showcasing how quantum computing can tackle existing challenges in AI and offer unique opportunities for healthcare.

Artificial intelligence (AI) is gaining attention and importance within the healthcare system. AI can help to make tedious routine tasks easier - including forecasting staffing requirements in hospitals? Fraunhofer IKS is addressing this question in a current project – and will soon present its findings at the Healthcare Hackathon in Mainz, Germany.

In everyday life, algorithms already support us in many activities, and in healthcare, too, artificial intelligence (AI) is considered a key technology and driver for progress and development. Classic treatment paths but also diagnostics will change and the use of AI will play a crucial role in this.

Where humans get stuck, artificial intelligence can help: for example, in the treatment of patients suffering from coronary artery disease (CAD).

Striving for excellence in scientific research on trustworthy Artificial Intelligence (AI), Dr. Narges Ahmidi also aims to encourage young female scientists to enter science and become leaders.

Hospital staff usually suffer from enormous workloads and staff shortages, especially in intensive care units. This has consequences for patients, particularly when facing the prospect of multi-organ failure. In the future, Artificial Intelligence (AI) aims to enhance the application of a clinical multi-organ support system to further optimize patient treatment.

While many current CDS expert systems (Clinical Decision Support) in laboratories are being criticized due to difficulty in scaling, constant review from the medical perspective and limited data cases, artificial intelligence (AI) has become a highly plausible alternative. »Safe Intelligence for Laboratories«, a webinar hosted by Fraunhofer IKS, focused on the cases and challenges of AI in laboratories from the perspectives of both industry and patients.

In the near future, medical engineering will be shaped by artificial intelligence and machine learning. However, the complex processes involved in machine learning create a problem for researchers — it is costly and challenging to eliminate errors before autonomous systems are put into use, and to reliably detect errors during operation.

In modern perception applications, such as in medical engineering, models based on artificial intelligence (AI) are increasingly being used due to their strong performance. However, this increasing performance often comes at the cost of the transparency of results. An online tool developed by the Fraunhofer Institute for Cognitive Systems IKS can help here.

Quantum computing has the potential to train artificial intelligence in medical diagnostics more efficiently. This could make diagnoses more accurate even when there is little data available. In the future, medical professionals expect to see improvements in the screening, diagnosis and progress monitoring of brain tumors, for example.