## 6 Challenges of XAI Approaches
### Technical Limitations of Explainability  
While there is a common trend in favour of employing XAI among researchers, the limitations of these approaches are often underestimated, which can be especially high risk in the field of medicine [131]. These obstacles might not fully invalidate the use of XAI methods, but researchers and developers should be aware of them, as they can negate the application of these techniques in certain cases, and urgently require study [28] (RQ2).  
Importantly, it should be noted that explanations and transparency might not translate into understanding underlying biomedical causal relationships [132], so we must be very cautious when using explainability as a tool to attempt knowledge discovery. For instance, using a surrogate model to explain a black-box system is only an approximation of the original model, and the produced explanations are unlikely to be faithful to the true inner workings of it [133]. Beyond this, it cannot be assumed that behind the explanations or decisions of an AI system there is always an equivalent or at least a truly comparable reason that human experts could infer [134]. For that reason, we should be careful not to build systems that are persuasive rather than transparent [135; 136], by properly analysing, validating, and interpreting the results obtained.  
Another point to be taken into account is that, usually, models and explanations that are more simple or compact are more interpretable, but it might not always be that way. Explanations depend on the specific context of the task and expectations of the user [44], and, if they are not meaningful, detailed enough or in the needed form, they might not be useful and may only complicate explanations of decision pathways [136].  
It should also be considered that there can also be serious cognitive limits to explainability, as was acknowledged about knowledge acquisition in the development of expert systems in the first decades of AI development. When human experts, like clinicians, become proficient in an area, they perform their tasks in a kind of automated, or at least compiled, way, effortlessly and efficiently. At this point, the knowledge needed for performing these tasks has become tacit, meaning that it is compiled in their mind and not available for conscious access and sharing [14]. For that reason, sometimes they cannot completely explain what the exact reasons are behind their decisions.  
This issue of after-the-fact justificatory explanations by experts makes it really hard to accurately or veridically model expert knowledge when the problem to solve is complex or vaguely defined, which is typical for the medical domain [17]. Unlike scientific disciplinessuch as biology, in clinical practice where reproducible experimentation is approximately feasible at least, there is a considerable uniquely patient-and-expert-encounter-specific practical expertise, and a lack of generalizable knowledge for many practical problem-solving situations [137]. In consequence, it is really difficult to elicit reproducible and useful models of physician reasoning in the form of concrete rules [17].  
This is one of the reasons that have contributed to making it impossible to fully capture the knowledge needed for building expert systems [14], and, ironically, it presents parallels to the problems with black-box AI heuristic or statistical models [137], so maybe it is unrealistic to expect comprehensive explanations from these systems as we might desire, in the same way that we do not entirely understand much of human neural processes within their complex biochemical, genetic, and developmental living contexts. Moreover, one must take into account that post hoc explanations frequently suffer from the same problems of interpretability as human-expert explanations [27].