Medicine and Care

The smart linking of patient data promises to make leaps in progress for medical research, diagnostics and prevention. Robot assistants are helping during surgery and assistance systems can relieve health care professionals of certain tasks and also give patients more autonomy. Acceptance of the technology and data security are important prerequisites for success.

A clinic without a hospital information system is unthinkable nowadays since procedures such as image or laboratory analytics have long become part and parcel of medical practice. Many patients are also tracking their own health through mobile apps and wearables. Methods of Artificial Intelligence can be used to link this data with other research and patient data. Data science can provide support in developing new prevention approaches, doing research on rare hereditary diseases and understanding previously unknown medical concepts. New diagnostic procedures for personalized treatment also require extensive data analysis. Assistance systems are even helping in psychiatric medicine to recognize emotions, thus aiding in the treatment of patients suffering depression.

Human-machine interaction is widespread in the medical field, for example with smart instruments which assist physicians and enable more targeted surgical procedures. In nursing care, digital assistants can help to lift and move patients in hospital beds. In future, assistance robots and AI-based technologies such as robotic exoskeletons will help people to live autonomous lives well into old age. In light of demographic change, this is an area which holds great potential.

At the same time, the health care sector in particular raises some important questions concerning society at large, not least of all the issue of data security. The goal is not to create a “transparent” patient: decision-making about personal health data is a priority issue for all future solutions. Legal liability concerns must also be clarified, for example in cases of misdiagnosis. On the other hand, many patients with chronic illnesses express the wish to have their patient data interlinked to ensure better care based on better information.

The goal of the broad range of AI applications is not to replace health care professionals but instead to relieve the pressure on them and assist in the best possible way. In radiological diagnostics, for example, learning systems are able to interpret X-ray images almost as well as an experienced specialist. Their quick analysis delivers a reliable basis for making decisions, but the human radiologist makes the final diagnostic assessment. Ultimately, the success of many applications will depend on the level of their acceptance. The communication with patients that is so critical for successful treatments cannot be delivered by any computer program, but smart systems can serve as a type of navigation system in the search for the best individual therapy and thus give medical specialists more time for communication and care.

These issues are the focus of working group 6 headed by Mr Klemens Budde (Charité – Universitätsmedizin Berlin) and Mr Karsten Hiltawsky (Drägerwerk AG & Co. KGaA) of the Plattform Lernende Systeme.