Artificial intelligence has developed significantly over the past 70 years.

Early work in the 1950s and 1960s focused on whether human intelligence could be described in a way that would allow machines to reproduce aspects of it. Initial experiments included programs capable of playing chess, solving logical problems and proving mathematical theorems.

Progress was not continuous. During the 1970s, limitations in computing power and algorithms contributed to a period often described as an “AI winter”. Interest increased again in the 1980s with the development of expert systems designed to imitate decision-making in areas such as medicine, finance and engineering.

In the 1990s, the focus increasingly shifted from rule-based systems to machine learning, allowing computers to identify patterns and learn from data. A widely recognised milestone came in 1997, when IBM’s Deep Blue defeated world chess champion Garry Kasparov.

During the 2000s and 2010s, AI became increasingly integrated into everyday technologies, including search engines, recommendation systems, language translation, voice assistants, facial recognition and medical image analysis.

Since 2022, generative AI has brought artificial intelligence to a much wider audience, with tools supporting activities such as searching for information, writing, image creation, planning and decision-making.

Today, AI is also an important area of research in healthcare.

Within AI-PROGNOSIS, artificial intelligence is being investigated in the context of Parkinson’s disease. The project combines AI with digital biomarkers and health data to support research on Parkinson’s disease risk, progression and medication response.

AI-PROGNOSIS brings together expertise from artificial intelligence, clinical research, digital biomarkers and health technologies, with work spanning research, development and clinical validation.