AI describes the process of a machine or system performing its own actions and learning through interactions and experience with its environment. AI is trained by large amounts of relevant data sets, this is called machine learning. If you want to know more about how ki:elements uses AI, check out one of our articles: here is the article about AI, here about language and here is an article about the company pipeline and how we work.
The goal of AI is primarily to support people in their work processes, as an accompanying technology – at least for now. This is also shown in a study by PCW (2019), which surveyed 500 decision-makers in german companies and if they are AI affine. The study reveals that of all the questioned companies 71% use AI to support human tasks and only 20% use AI to perform autonomous processes.
Of the companies which use AI, 70% use it in the area of data analysis of decision-making processes and 63% use AI for automation of the business processes, to support and to relieve the strain of time-consuming, highly repetitive assembly line work.
AI is also used in other areas of medical work. For example, there is a current project at the University of Bamberg to integrate AI into medical diagnosis. This is accomplished with the help of an interdisciplinary research team consisting of experts from various institutes and professions, like software developers, psychologists and physicians.
Since 2018, the team of experts around Dr. Ute Schmid, Professor of Applied Computer Science at the University of Bamberg, has been in the process of creating the so-called “Transparent Companion for Medical Applications”. The system aims towards recognizing symptoms and being able to give a well-founded diagnosis.
This process relies on two prototypes – one recognizes the pain of patients by looking at a video of patients in pain who cannot communicate their discomfort so the prototype must analyse the facial expression and body language. The other prototype determines the diagnosis using image material from a microscopy. However, not only the diagnosis is important but also a valid justification of the system, to prove the diagnosis, so that transparency and traceability is guaranteed. After the system’s diagnosis and the respective explanation the expert team decides whether they came to the same conclusions or if the system needs correction or improvement. In this way, the system keeps learning to better apply the expert knowledge. For this process to work, the team has to consist of medical experts working together with data scientists and developers, so that the “companions” achieve valid and reliable diagnosis while meeting important criteria of transparency, because a tracking and overview of the system’s findings that ultimately lead to the diagnosis is essential.
ki:elements relies on the expertise of an interdisciplinary team. Our expert team of psychologists, data scientists and developers is always actively involved in the creation and evaluation process to ensure the accurate implementation of the analysis algorithm. The study from PWC in 2019 also clarifies that AI not only offers enormous potential for innovation and stress relief by supporting employees during routine or analysis work but AI is also enabling new ways of analyzing data and allows deeper insights based on the collected information. So in contrast to the prevailing opinion, a more innovative way of working is emerging and new and more efficient jobs can be created through the use of AI. For example, conclusions about cognitive deficits or impairments can be drawn from speech and voice recordings by using AI – read more about this here.