While the amount of data ideally increases knowledge, it also increases complexity
The life sciences, and thus modern medicine, have undergone rapid development in recent years, accelerated in part by the genomic revolution in single-cell genomics. With this, deep insights into the structure and interaction of single cells can be gained. This allows a better understanding of the biological processes involved in the development of diseases, and thus a better development of new drugs.
However, as the level of detail gained increases, so does the complexity and volume of data collected, which can no longer be managed using classical approaches, so new approaches are needed.
Increased insight through AI support
Artificial intelligence methods offer a way out: compared to conventional approaches, in which experts sift through data manually and try to recognize known patterns, AI systems can extract important information in a data-driven way:
They reduce complexity, can incorporate expert knowledge, and later use this knowledge in a reproducible and deterministic way to automatically detect significant patterns and anomalies.
The reward for the effort involved is reflected not only in improved diagnostics, but also in the shortened time required to gain knowledge. In the time saved, the experts themselves can concentrate on the essentials: researching complex disease patterns, identifying new biomarkers and developing new active substances.
Thus, the use of AI support can make an important contribution to pharmaceutical research towards personalized and precision medicine.
The resulting benefits are:
- Faster development cycles
- Complexity reduction
- Reproducible analytics
- Reduced manual effort
- Higher knowledge gain through AI support
- Overall more stable processes
- Competitive advantage over competitors