## 4 Discussion  
This study shows that the AI model achieved high sensitivity (92.8%) for detecting DFO on plain radiographs, surpassing the performance of an experienced clinician (90.24%). The model also demonstrated a high positive predictive value (PPV) of 0.97. In comparison to the previous studies, which used histopathology as the gold standard, our AI model showed superior sensitivity to those of [PERSON] et al. (89%), [PERSON] et al. (82%), and [PERSON] et al. (86%) [36; 37; 42]. These outcomes highlight the potential of AI-assisted radiographic interpretation in specialized diabetic foot units. Despite its strong sensitivity, the AI model demonstrated low-level specificity. This phenomenon is in alignment with previous findings, as indicated in a recent meta-analysis [1], which scrutinized seven validation studies that used histopathology as the gold standard. In these studies, the specificity value was consistently lower than the sensitivity value. This trend can be ascribed to the high prevalence of DFO in specialized diabetic foot centers, where validation studies are typically undertaken. For example, [PERSON] et al. [37] reported a prevalence of 79.5%, [PERSON] et al. [36] reported 72.5%, and [PERSON] et al. [42] reported 66.7%. In our study, the prevalence of DFO was 73.2%, which reinforces that our cohort represents a high-prevalence setting. In addition to internal validation parameters, assessing reproducibility is crucial for evaluating a diagnostic tool. Our study found a very low level of agreement between the AI model and the experienced clinician, with a [PERSON]'s kappa coefficient of K = \(-\)0.105 (\(p\) = 0.117). Similar findings have been reported in previous studies assessing the reproducibility of radiographic interpretation for DFO among clinicians with varying levels of expertise. [PERSON] et al. (2013) [8] found low-level inter-clinician agreement (K = 0.35, \(p\) < 0.01) among experts evaluating radiographs for DFO. A subsequent validation study in  
Figure 4: An annotated Venn diagram showing the overlap in positive diabetic foot osteomyelitis (DFO) classifications between the AI model and the experienced clinician. The left circle represents the cases classified as positive by the AI model (\(n\) = 157), and the right circle those classified as positive by the clinician (\(n\) = 139). The overlapping region (\(n\) = 128) corresponds to cases classified as positive by both these methods. The “Only AI” segment includes cases identified as positive exclusively by the AI model (\(n\) = 29), while the “Only Clinician” segment includes those identified only by the clinician (\(n\) = 11). 2018 showed similar results (K = 0.40, \(p\) < 0.001) [42]. Likewise, [PERSON] et al. [37] reported a low level of inter-clinician agreement (K = 0.136, \(p\) = 0.104). One potential explanation for the low-level agreement observed in our study is that both the AI model and the experienced clinician interpreted radiographs solely based on the ulcer location, without access to additional clinical information. Prior studies have demonstrated that incorporating clinical assessments such as the PTB test improves diagnostic consistency. [PERSON] et al. [9] reported that when radiographic interpretation was combined with PTB, the degree of inter-clinician agreement significantly increased (K = 0.77, \(p\) < 0.001). Similarly, [PERSON] et al. [36] demonstrated that combining radiography with PTB increased the level of sensitivity from 82% (radiography alone) to 97% (radiography + PTB). A significant observation is that the AI model (\(n\) = 157) and the proficient clinician (\(n\) = 138) both tended to categorize a considerable number of patients as positive for DFO. They concurred on 128 positive cases, equating to an 81.5% agreement rate for positive diagnoses, as shown in Table 1. However, there was a lack of concordance for the negative instances.