Data-driven Services and Decision Support
Data is a key enabler for digital transformation. Data-driven automation and decision support are related to statistics, computer science, and linguistics using technologies such as AI.
Artificial Intelligence is a broad concept, covering a range of subfields, including ML, deep learning, computer vision, robotic process automation, image recognition, etc.
The EC proposed a set of actions to boost excellence in AI, and rules to ensure that the technology is trustworthy. The proposal for a Regulation on AI (Commission Proposal of 21 April 2021 for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain union legislative acts – ‘AI Regulation Proposal’; the first-ever legal framework on AI) [1] has been published. For this legal document the Commission defines an ‘artificial intelligence system’ (AI system) as meaning “software that is developed with one or more of the techniques and approaches listed in Annex I* and can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with.”[2].
* ANNEX I, Artificial Intelligence Techniques and Approaches referred to in Article 3, point 1)…of the AI Regulation Proposal:
- Machine learning approaches, including supervised, unsupervised and reinforcement learning, using a wide variety of methods including deep learning;
- Logic-and knowledge-based approaches, including knowledge representation, inductive (logic) programming, knowledge bases, inference and deductive engines, (symbolic) reasoning and expert systems;
- Statistical approaches, Bayesian estimation, search and optimization methods
AI is often used in the medical sector for data mining purposes and for diagnostic purposes. Data collected with digital technologies and recordings can be analysed by AI and ML algorithms [3]. AI can help identify unknown patterns or irregularities or new drug candidates or biomarkers, by processing large quantities of data. These capabilities may improve the accuracy of decision-making (clinical and organisational), risk prediction, resource allocation, drug discovery, or public health in predicting disease spread. However, many specific data sets are required to train AI and achieve these benefits.
References
[1]EC:https://ec.europa.eu/info/strategy/priorities-2019-2024/europe-fit-digital-age/excellence-trust-artificial-intelligence_en
[2] Proposal for a Regulation of The European Parliament And of The Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) And Amending Certain Union Legislative Acts, COM/2021/206 final
https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206
[3] Graili P, Ieraci L, Hosseinkhah N, Argent-Katwala M. Artificial intelligence in outcomes research: a systematic scoping review. https://pubmed.ncbi.nlm.nih.gov/33554681/ doi:10.1080/14737167.2021.1886083