| Sumario: | Bloodstain Pattern Analysis (BPA) is transitioning from an experiential, expert-dependent craft to a quantitative, multidisciplinary forensic science. This review critically evaluates the evolutionary trajectory of BPA, contrasting traditional macroscopic examination with modern computational advancements, including 3D laser scanning, terrestrial LiDAR, and artificial intelligence (AI)-driven computer vision models. While automated software platforms (e.g., HemoSpat, BackTrack) and deep learning networks significantly improve area-of-origin reproducibility and pattern classification speed, their operational integration remains constrained by severe methodological fragmentation, dataset bias, and a historical lack of multi-variable substrate validation. Furthermore, a critical translational gap persists regarding legal admissibility under Daubert-style frameworks due to insufficient error propagation modeling. To overcome these epistemological boundaries, this paper proposes a structured transition toward a hybrid forensic paradigm that synthesizes human expert reasoning with physicodynamically constrained algorithms. Future directions must prioritize the construction of validated ground-truth datasets, the implementation of Likelihood Ratio (LR) Bayesian inference models for objective uncertainty quantification, and universal compliance with international standardization initiatives (OSAC/ASB). Ultimately, establishing these quantitative frameworks is essential for safeguarding the evidentiary value and courtroom reliability of BPA in modern judicial decision-making. • Critical evaluation of BPA's paradigm shift from expert intuition to quantitative fluid dynamics. • Comparative framework contrasting classical, AI-based, and hybrid BPA digital methodologies. • Identification of critical gaps in substrate heterogeneity modeling and error propagation. • Analysis of computational and machine learning limitations under Daubert admissibility criteria. • A roadmap advocating for Likelihood Ratio (LR) frameworks and global OSAC/ASB standardization.
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