Artificial intelligence plays a major role across the segments prevention, diagnosis, treatment, recovery.
The key services of Syte include:
- Data mining
With an international team of leading AI specialists, SYTE can supports with setting up a data mining process for your company to capture the most essential and valuable good in AI: Raw data.
- AI Piloting
Having strong domain expertise, SYTE enables fast track AI piloting studies to evaluate most promising solutions within your option space. This process can be seen as a first attempt to refine valuable raw data for business purposes.
- AI Roll-Out
After having conducted promising AI pilot studies and evaluated most valuable market opportunities, SYTE supports in transferring the implemented AI process to promising business models.
AI in Healthcare as one big application domain of AI is categorized by SYTE into four main categories: MedTech, Pharma, Insurance and Government. By applying AI in these four industries, huge potentials through the leverage of technological advantages can be used.
For each category promising examples are:
- AI in MedTech may enable intelligent patient monitoring.
- AI in Pharma may support companies in clinical trial management.
- AI in Insurance might be applied to foster preventive behavior.
- AI in Government can help to optimize general population health.
The concrete benefits by application of AI are threefold:
- Accuracy of AI
AI is able to consider more data for its tasks without showing fatigue or making faults once it is appropriately prepared (i.e. “trained” in jargon). In contrast, humans become tired making (routine) tasks such as manual data handling.
- Cost and Efficiency of AI
Once the AI algorithm is applied to a certain application area, it can solve problems again and again almost without causing additional costs.
- Replication of AI
AI is capable of making numerous copies of the task. By slight variations in the tuning parameters of the algorithm one can get additional insights into the problem.
Nevertheless, AI is still far from perfection and universal application. There are three major challenges yet to be solved:
- Data handling
Ease of use of AI requires efficient data capturing and processing. As data often comes from many different data sources there are still manual and time consuming tasks involved in the process.
- Reliable mechanism
In many AI applications, the user does not know exactly how the algorithm came up with the solution. Especially in sensitive areas, such as proposing treatment options for to patients, reliable mechanisms have to be established in order to create trust.
AI plays its big advantages when it is applied in complex application domains, i.e. huge amounts of data from many different sources have to be analyzed. There is still an unmet need for better representation techniques of the problems to be solved.
Syte Insight – Interview:
We often get asked by our clients, where the concrete advantage of AI in contrast to conventional statistics is manifested.
Our answer: One major factor, especially within the domain of deep learning (a subdomain of AI and machine learning), is the necessity applied apriori knowledge or assumptions:
By applying conventional statistical methods, e.g. linear regression, heavy feature engineering (=processing of data) often is required and apriori assumption are made. In the case of linear regression: You assume a linear relationship between two investigated factors and you eventually want to find out how strong this relationship is.
In contrast to that approach, deep learning almost requires no feature engineering or apriori assumptions and knowledge. It learns it by itself – even the most complex relationship can be learned, which are more likely to occur in complex systems such as the human body.
However, there is one big disadvantage: The amount of required data.
To learn such complex relationships and to make valid predictions, huge amounts of data are required for successful application of AI, especially in healthcare. – Christoph Kocher, consultant at SYTE