Artificial Intelligence in Medicine

Healthcare is undergoing a digital transformation. In clinics digital patient files and other intelligent systems are on the rise, assisting doctors in providing a diagnosis or as robotic assistants in the operating room. The digital networking of patient data makes it possible to individualize and optimize treatment processes. In the future, digital medicine will even accompany us at home for our follow-up care and treatment. Health apps and wearables that, for instance, measure your heart rate and count the daily steps we make already form part of everyday life for many people. Here, they can be much more than a lifestyle product, as they enable patients to track their own health data and implement therapies.

The digital patient journey

In the future, we will be accompanied by digital medicine as patients: Ranging from the prevention, screening, diagnosis and therapy to the aftercare.

Graphic about the digital patient journey
© Fraunhofer IKS

Digitalization opportunities in medicine

Digitalization generates huge amounts of data. Industry 4.0 demonstrates how this data can be used effectively. Data is networked to monitor processes, to identify trends at an early stage and to be able to respond to them using new business models. These advantages can also be applied to medicine. While taking into account all the medical and non-medical data, it becomes possible to make efficient and rational decisions, individualize therapies or detect diseases at an early stage.

Big Data and artificial intelligence (AI) are important keywords in the medicine of the future. AI can combine and analyze large amounts of data in a very short time, faster than humans ever could. This paves the way for intelligent applications in the fields of:

  •  Clinical decision-making
  •  Robot-assisted surgery
  •  Medical imaging and diagnostics
  •  Chronic disease monitoring
  •  Hospital Data Management

At Fraunhofer IKS, we conduct research on the validation of digital applications in these safety-critical areas.

Medical imaging and diagnostics

CT-Bilder auf Laptop und Bildschirm
© iStock.com/Orientfootage

By analyzing image data, artificial intelligence can assist in medical diagnostics. Based on existing image data and associated diagnoses, patterns in the image are recognized by the AI, which are assigned to disease patterns. The analysis and availability of large amounts of data makes it possible to detect pathological changes in the image quickly and reliably, to individually adapt therapies to the patient and to provide prognoses concerning the further course of the disease.

We are also working on practical applications of quantum technology in medicine. You can find more information about this on our blog (in German).

Robot-assisted surgery

Robotics in the operating theater is anything but science fiction. Today, robotic assistants are already being used in a wide variety of operations or in hospital logistics for the distribution of medications. Robotics enables higher precision, better visualization and a minimally invasive approach to surgery.

Medizinischer Roboter im OP
© iStock.com/ClaudioVentrella

Safeguarding of safety-critical AI applications in medicine

Holistic safety concepts are required to ensure the safety of users of digital health services. Intelligent systems collect and combine data and make decisions based on it. 

This decision-making process is complicated by two factors:

  • The availability of the data: Artificial intelligence is trained to recognize patterns using processed data sets. In the healthcare sector, these data are sometimes scarcely available, e.g. in the diagnosis of rare diseases.
  • Intransparency and traceability of AI decisions: Complex AI algorithms and their decisions, for example in the area of diagnostics, can only be checked by specialists with difficulty because it is not clear which data are of fundamental importance for the decision.

False diagnoses and prognoses constitute a major risk in the application of cognitive systems in the field of medical technology. At the Fraunhofer Institute for Cognitive Systems (IKS), we conduct research to make intelligent cognitive systems reliable and secure.

Our research encompasses several focal points:

  • Safeguarding perception of cognitive systems in medicine.
  • Dynamic security proofs of digital health services
  • Uncertainty estimations of the AI outputs
  • Explainability and comprehensibility of cognitive systems
  • Application of quantum computing in medicine

Research focus at Fraunhofer IKS

Safeguarding the perception of cognitive systems in medicine

Artificial intelligence recognizes patterns in images and sensor data that have been previously learned from training data. By means of this perception, i.e. interpretation of this data, objects are recognized and the environment perceived. Reliable and robust perception is a critical safety factor not only for the diagnostics, but also for the perception of surgical robots. In order to be able to act safely and avoid administration of the incorrect treatment and injuries to patients, the exact positioning of the procedure in the body is essential.

Dynamic proofs of security for digital health services

The regulatory demands upon the safety and performance of medical devices are high. Intelligent systems constantly further develop and build on their capabilities. In doing so, they continue to be a black box, i.e. it is not clear to the outside world how decisions are made. A dynamic and continuous proof of safety is necessary in order to be able to certify artificial intelligence. At Fraunhofer IKS, we are researching solutions to improve the explainability, transparency, and robustness of neural networks and to provide quality and proofs of safety of AI.

Determination of the uncertainty of the AI output

We use estimations of uncertainty to teach AI to doubt things in order to avoid making incorrect decisions. In the medicine field, there is often only a small amount of low quality data available for the training of cognitive systems. This may result in a situation whereby no clear decision can be made on the basis of existing data. In such situations, it is important that the cognitive system is aware of this uncertainty and draws attention to it, for example, to avoid misdiagnoses.

Explainability and comprehensibility of AI

Under the keyword of Explainable AI, Fraunhofer IKS is researching solutions to make the basis for decision-making in diagnostics comprehensible and verifiable for users. In doing so, we combine AI technology with classical software development.

Quantum computing in medicine

In a joint project with the LMU Munich Hospital, we are researching into solutions on how quantum computing and artificial intelligence can be used for intelligent diagnostics and healthcare.