RibAssist 3D is a research framework for confidence-aware detection and selective 3D localization of rib fractures from CT-derived biplanar projections. Rather than treating the task as a single end-to-end prediction problem, the study systematically decomposes the pipeline into geometry, localization, and cross-view correspondence to identify the true operational bottleneck. The results demonstrate that projection geometry and conditional 3D localization are reliable when correct cross-view correspondence is established, while confidence-limited correspondence remains the primary factor limiting end-to-end performance. The work introduces a conservative selective localization strategy that abstains from unsupported 3D predictions, preserving uncertain findings for clinician review instead of generating potentially misleading outputs. This approach aligns with the goals of responsible AI in healthcare by emphasizing reliability, transparency, and human-in-the-loop decision support. The presentation will discuss the methodology, key experimental findings, an interactive clinician-review prototype, and future directions for improving confidence-aware multi-view anatomical reasoning in medical imaging.