Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.

While it is relatively easy to imitate and evolve natural swarm behavior in simulations, less is known about the social characteristics of simulated, evolved swarms, such as the optimal (evolutionary) group size, why individuals in a swarm perform certain actions, and how behavior would change in swarms of different sizes. To address these questions, we used a genetic algorithm to evolve animats equipped with Markov Brains in a spatial navigation task that facilitates swarm behavior. The animats' goal was to frequently cross between two rooms without colliding with other animats. Animats were evolved in swarms of various sizes. We then evaluated the task performance and social behavior of the final generation from each evolution when placed with swarms of different sizes in order to evaluate their generalizability across conditions. According to our experiments, we find that swarm size during evolution matters: animats evolved in a balanced swarm developed more flexible behavior, higher fitness across conditions, and, in addition, higher brain complexity.

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16Evolving in groups can either enhance or reduce an individual's task performance. Still, 17 we know little about the factors underlying group performance, which may be reduced to 18 three major dimensions: (a) the individual's ability to perform a task, (b) the dependency on 19 environmental conditions, and (c) the perception of, and the reaction to, other group 20 members. In our research, we investigated how these dimensions interrelate in simulated 21 evolution experiments using adaptive agents equipped with Markov brains ("animats"). We 22 evolved the animats to perform a spatial-navigation task under various evolutionary setups.23 The last generation of each evolution simulation was tested across modified conditions to 24 evaluate and compare the animats' reliability when faced with change. Moreover, the 25 complexity of the evolved Markov brains was assessed based on measures of information 26 integration. We found that, under the right conditions, specialized animats were as reliable 27 as animats already evolved for the modified tasks, that interaction between animats was 28 dependent on the environment and on the design of the animats, and that the task difficulty 29 influenced the correlation between the performance of the animat and its brain complexity.30 Generally, our results suggest that the interrelation between the aforementioned dimensions 31 is complex and their contribution to the group's task performance, reliability, and brain 32 complexity varies, which points to further dependencies. Still, our study reveals that 33 balancing the group size and individual cognitive abilities prevents over-specialization and 34 can help to evolve better reliability under unknown environmental situations. 35 Keywords: Collective behavior, evolutionary algorithms, cognitive science, Markov brains. PLOS Computational Biology --FOR REVIEW 3 of 35 36Author Summary 37The ability to adapt to environmental changes is an essential attribute of organisms which 38 have had evolutionary success. We designed a simulated evolution experiment to better 39 understand the relevant features of such organisms and the conditions under which they 40 evolve: First, we created diverse groups of cognitive systems by evolving simulated 41 organisms ("animats") acting in groups on a spatial-navigation task. Second, we post-42 evolutionary tested the final evolved animats in new environments-not encountered before-43 in order to test their reliability when faced with change. Our results imply that the ability to 44 generalize to environments with changing task demands can have complex dependencies on 45 the cognitive design and sensor configuration of the organism itself, as well as its social or 46 environmental conditions. 47Introduction 48Intelligence is the ability to adapt to changes. According to this prevalent perspective, 49 possessing general intelligence [1,2] not only enables one to perform a task correctly under 50 already known conditions, but also to perform well under unexpected conditions. Further, in 51 natural ...