# A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research
## 3 Results
### Discussion  
While our systematic search initially identified 234 publications, the final set included only 22 papers (approximately 9%). This relatively small number is primarily due to the niche nature of the research field, as our inclusion criteria were intentionally focused on papers that directly addressed frameworks that proposed concrete solutions based on AI-driven experimental design. Filtering for AI-based and experimental design-related publications is necessary to ensure appropriate results. Future surveys could consider broader search terms or inclusion criteria to capture a wider range of contributions. For example, the term \"artificial intelligence\" could be replaced or extended with the terms \"deep learning\", \"machine learning\", and \"automated experimentation\". However, this could also increase the number of irrelevant publications found during the experimental derivation of the search query, especially by only considering \"automated experimentation\", \" which leads, among others, to non-AI-based experimentation frameworks. Similarly, a replacement of the included experiment-related terms with the term \"experimental design\" also led to poor results. The observations made during the systematic review of the literature lead to the question of what properties an ideal system or framework for AI-driven experimental design should incorporate. Depending on the application's objective, one has to decide whether a system needs to operate partially or fully autonomously. Both frameworks are capable of using AI to plan and select experiments and optimize future experiments based on the obtained experimental results, e.g., [PERSON] et al. [226] (partially autonomous) and [PERSON] et al. [220] (fully autonomous). While partially autonomous frameworks can play a supporting role for scientists during the experimental design process to overcome expensive trial-and-error experiments, fully autonomous frameworks perform the selected \begin{table}
\begin{tabular}{l c} \hline \hline
**Generalizability** & **Number** \\ \hline Yes & 16 \\ Limited & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The number of papers with regard to generalizability. experiments without the need for a scientist in the loop. In contrast, supportive frameworks are not desirable due to the missing feedback loop. Thus, such systems are not able to learn from the new observations obtained during experiments. Compared to partially autonomous frameworks, fully autonomous frameworks are significantly less represented in the reviewed literature, which shows the need for further implementations in this field. However, both partially and fully autonomous frameworks share similar desired properties. Both need to be generalizable in order to be used for different applications. A majority of the reviewed frameworks exhibit generalizability. This is particularly important for fully autonomous systems, which are usually applied in the context of SDLs and, thus, should perform different kinds of experiments without being limited to one specific kind. An example of a limited framework was proposed by [PERSON] et al. [220] that focused on flow chemistry. Therefore, the experimentation stage needs to be more flexible and adaptable. Furthermore, online capabilities are necessary to adapt experiments based on newly collected data. All partially and fully autonomous reviewed systems meet this criterion. In terms of AI techniques, AL (including optimization) and RL should be applied. Both methodologies incorporate a feedback loop and an adaptation based on newly received data. A key limitation of existing frameworks is the sparse availability of real, domain-specific data, which is both time-consuming and costly to obtain. Particularly in the early stage, sufficient data are mandatory for an efficient exploration. While optimization techniques and AL are generally efficient in handling sparse data, RL- and supervised learning-based methodologies require large amounts of data to achieve effective learning and generalization.