Discussion

In recent years, AI and machine learning techniques have been increasingly applied in forensic research as tools capable of integrating complex datasets and identifying patterns that may improve PMI estimation. Although several traditional approaches based on morphological, biochemical, and environmental indicators have been proposed, their accuracy remains limited and strongly influenced by multiple postmortem and environmental variables [3–5, 75].

The present systematic review identified 64 studies investigating AI-based approaches for PMI estimation across multiple forensic domains. Overall, the results demonstrate a rapidly growing interest in the application of machine learning models to decomposition-related data, reflecting the increasing availability of high-throughput biological datasets and advanced computational methods.

Among the included studies, microbiome-based models represented the most frequently investigated approach, accounting for nearly half of the included studies. Microbial succession during decomposition has long been recognized as a potentially informative indicator of postmortem changes, and recent advances in sequencing technologies have enabled detailed characterization of microbial communities in different anatomical sites and environmental contexts.

Several studies applied machine learning algorithms to microbial community datasets obtained from the skin, gut, oral cavity, bone, and surrounding soil environments [13, 28, 29, 62]. Random Forest models were particularly frequently used for PMI prediction due to their robustness in handling complex nonlinear relationships and high-dimensional datasets [18, 43, 51]. In several investigations, microbiome-based models achieved high predictive performance, with coefficients of determination approaching R2 ≈ 0.95 or mean absolute errors of only a few hours to several days depending on the decomposition stage [43, 51].

Despite these promising results, microbiome-based approaches also present important limitations. Microbial community composition is strongly influenced by environmental factors such as temperature, humidity, and soil characteristics, which may limit the generalizability of predictive models across geographic regions. Furthermore, many studies were conducted using controlled animal decomposition models, including rodents or pigs, rather than human forensic cases [23, 56, 62].

Overall, predictive performance appeared to vary according to both the biological matrix analyzed and the investigated PMI interval. Microbiome-based models generally demonstrated the most stable predictive performance across broad decomposition windows, likely because microbial succession continues throughout advanced decomposition stages. In contrast, metabolomics and spectroscopy-based approaches appeared particularly effective during early PMI intervals, when biochemical alterations occur rapidly after death.

Imaging-based artificial intelligence models also demonstrated promising performance for early PMI estimation, especially in controlled experimental settings and postmortem imaging environments. However, their performance may be more sensitive to imaging standardization and acquisition protocols.

Several studies suggested that multimodal approaches integrating multiple biological and environmental datasets may achieve superior predictive performance compared with single-modality models, although direct comparative evidence remains limited.

Another important group of studies explored molecular approaches, including metabolomics and proteomics. These methods aim to identify biochemical signatures associated with postmortem degradation processes, such as protein fragmentation or metabolite accumulation.

Metabolomic profiling of tissues or biological fluids combined with machine learning algorithms has demonstrated promising results for early PMI estimation [25, 38, 47]. In particular, large-scale metabolomic analyses using neural networks or ensemble learning approaches have reported predictive errors of only a few days in human forensic datasets [47].

Proteomic studies have also shown encouraging results, particularly when analyzing skeletal muscle or bone protein degradation patterns [24, 27, 71]. In some cases, classification models based on proteomic features achieved very high accuracy for distinguishing PMI intervals.

Nevertheless, the application of molecular techniques in routine forensic practice remains limited by the need for specialized analytical equipment, such as mass spectrometry platforms, as well as the potential influence of ante-mortem physiological factors or environmental variables on molecular biomarkers.

Recent studies have increasingly investigated the use of imaging-based artificial intelligence models for PMI estimation. These approaches exploit high-dimensional datasets derived from postmortem imaging modalities such as computed tomography, histopathology slides, or hyperspectral imaging. Deep learning architectures including convolutional neural networks and Vision Transformer models have been applied to detect subtle structural or morphological changes associated with decomposition processes [30, 41, 64]. Similarly, machine learning analysis of postmortem CT radiomic features has been proposed as a tool for distinguishing early PMI intervals [33].

Although these approaches remain relatively underrepresented compared with microbiome-based methods, imaging-based AI models may provide valuable tools for early postmortem interval estimation, particularly in hospital or forensic imaging settings where postmortem CT is increasingly used.

Spectroscopy-based approaches represent another emerging research direction in AI-based PMI estimation. Techniques such as Fourier transform infrared spectroscopy (FTIR), attenuated total reflectance FTIR, and near-infrared spectroscopy have been used to detect biochemical changes in tissues or body fluids during decomposition [22, 65, 66].

Machine learning algorithms applied to spectroscopic datasets have demonstrated promising predictive performance, particularly for early PMI intervals where biochemical changes occur rapidly.

Similarly, several studies have applied machine learning models to forensic entomology datasets, including insect developmental stages or chemical signatures in puparia [15, 54, 69]. Because insect colonization often represents the most reliable indicator for longer postmortem intervals, AI-based entomological models may play a key role in extending PMI estimation beyond the early decomposition stages.

Although several studies reported highly promising predictive performance, direct comparisons between AI-based approaches and traditional PMI estimation methods were relatively limited. In many investigations, AI models were evaluated primarily within experimental datasets without systematic comparison against conventional forensic techniques such as body cooling curves, rigor mortis, or entomological staging performed by standard methodologies.

Consequently, while AI-based approaches appear capable of improving prediction accuracy in multidimensional datasets, current evidence should still be interpreted cautiously until larger externally validated comparative forensic studies become available.

Several limitations of this systematic review should be acknowledged. First, the literature search was limited to two major scientific databases (PubMed/MEDLINE and Scopus), and although additional citation tracking was performed, relevant studies indexed in other databases may have been missed. Second, only studies published in English were included, which may have introduced language bias. Another important limitation concerns the substantial heterogeneity across the included studies. Differences in data modalities, biological samples, decomposition conditions, AI model architectures, validation strategies, and reported performance metrics made direct comparison between studies challenging.

An important methodological limitation emerging from this review is the relatively limited use of independent external validation cohorts. Most studies relied on internal validation strategies, such as train–test splits or cross-validation, which may overestimate predictive performance and reduce generalizability in real forensic contexts.Furthermore, many of the included studies were based on experimental animal models rather than human forensic cases, which may limit the generalizability of the findings to real-world forensic investigations. Finally, because of the variability in study design and outcome reporting, it was not possible to perform a quantitative meta-analysis of model performance. Instead, results were synthesized narratively with a focus on methodological trends, data sources, and reported predictive performance. The methodological quality assessment further highlighted that many currently available AI-based PMI studies remain exploratory in nature, with limited external validation and reduced generalizability to real forensic scenarios.

In addition to methodological aspects, the increasing use of AI in forensic and medical contexts also raises relevant medico-legal considerations. AI-based models for PMI estimation may potentially contribute to forensic decision-making, making transparency in data processing and model validation particularly important. A recently proposed medico-legal framework for the evaluation of artificial intelligence in healthcare highlights the importance of systematically assessing dataset quality, identifying potential sources of error, and reconstructing causal pathways when algorithmic systems are involved in clinical or medico-legal contexts [76]. Although developed primarily for healthcare applications, similar principles may also be relevant for the evaluation and responsible use of AI-based tools in forensic investigations.

In addition, many machine learning approaches are inherently probabilistic and stochastic in nature, meaning that predicted PMI estimates are often associated with statistical uncertainty rather than deterministic conclusions. This aspect may have important implications in forensic and judicial contexts, where the interpretation, explainability, and reproducibility of AI-generated estimates become essential for evidentiary reliability and admissibility.

Future research should prioritize the development of large multicenter forensic datasets including diverse environmental conditions and human postmortem cases. Standardized data acquisition protocols and transparent reporting of machine learning pipelines would significantly improve reproducibility and comparability across studies.

In addition, the integration of multimodal datasets, combining microbiome profiles, molecular biomarkers, imaging features, and environmental variables, represents a promising direction for improving PMI prediction accuracy. Multimodal artificial intelligence models may be better suited to capture the complex biological processes involved in decomposition.

Finally, the development of interpretable AI models will be essential for forensic applications, where transparency and explainability are critical due to the potential legal implications of PMI estimation.