## 2 Literature Review
### Advanced Technologies
#### 2.2.1 Software  
The performance of humans and software (predominantly AI) has been compared in various industries and different applications including speech and face recognition [54, 65, 66, 67], object recognition and classification [48, 55, 57], translation [54], gaming [54], music classification and prediction [49, 68], teaching [69, 70], learning ability [61], communication [52] and autonomous driving [71, 72]. The healthcare industry in particular has had an increasing interest in developing advanced technologies in recent years [58, 73, 74, 75, 76]. The motivation lies in the necessity to assist the clinician and avoid incorrect diagnosis that could have severe, or even fatal consequences for the patient. Medical diagnosis is quite similar to quality inspections in the sense that professionals (clinicians or inspectors) perform an assessment of the human body or aircraft parts and search for any indications or alarming conditions that might hint at a disease of defect, respectively [6, 9, 10]. The consequences of a missed adverse anomaly can be critical in both cases. Moreover, no defect or condition is identical to another, although the human body can be seen as being more complex than manufactured engine blades [9]. In both cases the assessor needs to be versatile to be able to detect these various anomalies. The diagnostics performance of clinicians and AI software for medical imaging has been analysed and compared for a variety of medical assessments, including detection and characterisation of acute ischemic stroke [50], age-related macular degeneration [60], breast cancer [76] and lesions [59]. [PERSON] et al. provided a comprehensive literature review on deep learning and performed a meta-analysis using contingency tables to derive and compare the software performance with healthcare professionals [58]. Those authors showed that the results of deep learning models are comparable to the human performance. They also noted that the performance of AI is often poorly reported in the literature, which makes human-software comparisons difficult. Only a few studies were found that used the same sample to measure both the performance of AI and human [50, 59, 60, 76]. This limits the reliable interpretation of the reported performances in [58] and the results should be viewed with caution. For quality inspections, [PERSON] et al. [77] reviewed the literature on manual, hybrid and automated inspection systems and provided a comprehensive summary of those studies, including factors influencing human inspection performance and an overview of advanced digital technologies used for automated visual inspection systems (AVIS). The authors concluded that some inspection tasks cannot be fully automated due to the nature and complexity of the inspection, thus a human is unlikely to be replaced by a machine. Likewise, there are inspections that cannot be performed by human operator due to the inspection environment, e.g., inspection of hot steel slabs [77]. Different image processing approaches for defect detection in textiles were compared in the work of [PERSON] and [PERSON] [63]. This study suggests that the performance highly depends on the selected approach and defect type. Thus, in the present study we analysed the inspection performance of a variety of defects. Only one study by [PERSON] and [PERSON] [47] was found that compared human and machine performances. The study analysed an inspection task of small steel cylinders for any defects, including nicks and dents, scratches, pits and toolmarks. Neither the human nor the automated inspection system showed an outstanding performance, specifically for nicks and dents. The inspection machine was able to detect most faults but was inconsistent in the decision, i.e., whether the finding was acceptable or had to be rejected. Overall, the inspectors outperformed the inspection machine due to their more sophisticated decision-making capabilities. The field of automated inspection and artificial intelligence has since evolved significantly [78; 79].