Artificial Intelligence for Integrated Analysis of Non-Blood Biological Fluids: From Biomarker Discovery to Clinical Decision Support Systems

1. Introduction

The analysis of non-blood biological fluids is an essential component of modern laboratory medicine and plays a pivotal role in the diagnosis and monitoring of a broad spectrum of neurological, infectious, inflammatory, and neoplastic diseases. Among these specimens, cerebrospinal fluid (CSF), serous effusions (pleural, peritoneal, and pericardial fluids), and synovial fluid are routinely investigated because they provide biochemical, cytological, microbiological, and molecular information that reflects pathological processes occurring within specific anatomical compartments [1]. In particular, CSF analysis has become indispensable for the diagnostic evaluation of central nervous system disorders and is increasingly recognized as a key source of biomarkers for neurodegenerative diseases, including Alzheimer's disease, where amyloid-β and tau proteins provide valuable insights into the underlying neuropathological processes [2,3].

Despite their considerable clinical value, the diagnostic interpretation of non-blood biological fluids remains challenging throughout the total testing process. Pre-analytical variables—including specimen collection, limited sample volume, and contamination during sampling—may substantially affect analytical quality. Analytical limitations arise because many laboratory assays have been developed and validated for serum or plasma rather than for alternative biological matrices. Consequently, matrix-specific validation, reference intervals, and analytical standardization remain limited for several non-blood biological fluids. The post-analytical phase is equally demanding, requiring the integration of laboratory findings with clinical presentation, imaging studies, and patient history to achieve an accurate differential diagnosis [1,4].

These challenges have stimulated growing interest in Artificial Intelligence (AI) as a tool capable of supporting the interpretation of complex laboratory data. Rather than replacing conventional laboratory testing, AI—particularly Machine Learning (ML) and Deep Learning (DL)—offers the opportunity to integrate heterogeneous sources of information, identify clinically relevant patterns, and assist diagnostic reasoning in situations where traditional rule-based approaches may be insufficient. This paradigm is especially relevant for non-blood biological fluids, where meaningful interpretation frequently depends on the simultaneous evaluation of biochemical, cytological, microbiological, molecular, and clinical information.

Within this evolving landscape, AI-driven Clinical Decision Support Systems (CDSS) represent one of the most promising applications of computational medicine. By integrating laboratory results with demographic characteristics, medical history, imaging findings, and other clinical variables, these systems can generate data-driven diagnostic insights, support patient stratification, and reduce interpretative variability, thereby contributing to more personalized and evidence-based clinical decision-making [4,5].

Against this background, this review provides a comprehensive overview of current and emerging AI applications in the analysis of non-blood biological fluids, with particular emphasis on CDSS, biomarker discovery, multi-omics integration, current evidence, existing limitations, and future perspectives for precision laboratory medicine.

2. Methods

A structured narrative literature review was conducted to identify studies investigating the application of AI, ML, Deep Learning DL, and CDSS in the analysis of non-blood biological fluids. The literature search was performed using PubMed, Scopus, and Web of Science and included articles published between January 2018 and June 2026. The search strategy combined the terms "artificial intelligence", "machine learning", "deep learning", and "clinical decision support systems" with keywords related to “cerebrospinal fluid”, serous effusions, synovial fluid, diagnosis, biomarkers, and laboratory medicine. The strategy was adapted as appropriate for each database. Original articles, systematic reviews, methodological studies, and consensus documents published in English were considered eligible. Following title and abstract screening, potentially relevant publications underwent full-text evaluation. Particular emphasis was placed on studies investigating AI applications in CSF, serous effusions, synovial fluid, and multi-omics integration. The selected studies were subsequently categorized according to biological fluid, AI methodology, and clinical application. Given the narrative nature of this review, no formal meta-analysis was performed. Instead, the discussion focuses on studies reporting diagnostic or prognostic applications of AI, with particular attention to model validation, clinical utility, and translational challenges.

3. Diagnostic Complexity of Non-Blood Biological Fluids

The diagnostic evaluation of non-blood biological fluids is inherently more complex than that of conventional blood samples because these matrices are characterized by greater biological heterogeneity and a lower degree of analytical standardization. Unlike serum and plasma, which benefit from well-established analytical protocols and reference intervals, many non-blood biological fluids lack universally accepted procedures and validated diagnostic thresholds, making both analytical reliability and clinical interpretation more challenging [6].

The complexity begins during the pre-analytical phase. Sample collection varies according to the anatomical compartment, while limited specimen volume, inappropriate handling, delayed processing, or contamination may substantially affect analytical performance. Cerebrospinal fluid is particularly vulnerable to blood contamination during lumbar puncture, whereas prolonged storage may compromise cellular integrity and analyte stability, especially in low-volume or low-protein specimens [7].

Analytical challenges further complicate interpretation. Many laboratory assays currently used for non-blood biological fluids were originally developed and validated for serum or plasma, and their analytical performance in alternative matrices is often insufficiently characterized. The absence of matrix-specific validation procedures and reference intervals may therefore introduce analytical bias and reduce diagnostic confidence [8].

The post-analytical phase is equally demanding because clinically meaningful interpretation rarely depends on a single laboratory parameter. Instead, it requires the integration of biochemical findings, cytological evaluation, microbiological results, and clinical information. For example, the diagnosis of serous effusions relies on the combined interpretation of protein concentration, lactate dehydrogenase, cellular composition, and clinical presentation, whereas synovial fluid analysis requires the simultaneous evaluation of leukocyte count, crystal identification, inflammatory biomarkers, and patient history to distinguish infectious, inflammatory, and degenerative joint disorders [9].

The multidimensional nature of these diagnostic pathways highlights one of the principal limitations of conventional rule-based interpretation. As laboratory datasets become increasingly heterogeneous, computational approaches capable of integrating multiple sources of information may provide substantial support for diagnostic reasoning. In this context, AI offers the opportunity to move beyond isolated parameter evaluation toward a more comprehensive interpretation of biological complexity [10].

5. AI-Driven Clinical Decision Support Systems for Biological Fluid Analysis

Within the expanding landscape of AI applications in laboratory medicine, CDSS represent one of the most clinically relevant developments for the interpretation of non-blood biological fluids. Unlike conventional decision-support approaches based primarily on predefined rules or isolated laboratory parameters, AI-driven CDSS integrate heterogeneous sources of information—including laboratory findings, patient demographics, clinical history, and imaging data—to generate clinically meaningful diagnostic support.

By combining ML algorithms with multimodal clinical information, these systems can identify complex biological patterns, improve disease classification, support patient stratification, and reduce interpretative variability [16]. Their greatest value, however, lies not in replacing expert interpretation but in assisting clinicians when diagnostic reasoning requires the simultaneous evaluation of multiple interconnected variables. This capability is particularly relevant for cerebrospinal fluid, serous effusions, and synovial fluid, where clinically meaningful interpretation rarely depends on a single biomarker. Instead, diagnosis typically emerges from the integration of biochemical measurements, cytological findings, microbiological results, molecular analyses, and the overall clinical context. AI-driven CDSS provide a computational framework capable of integrating these diverse data streams into a unified diagnostic process. Figure 1 illustrates this conceptual workflow. Following appropriate pre-analytical quality assessment and laboratory testing, biochemical, cytological, molecular, omics, imaging, and clinical data are integrated within AI models based on ML and DL methodologies. The resulting computational outputs support biomarker discovery, disease classification, risk stratification, predictive modelling, and ultimately AI-assisted clinical decision-making. The principal AI applications across different biological fluids are summarized in Table 1.

5.1. Cerebrospinal Fluid

Among non-blood biological fluids, CSF currently represents the most mature field for the clinical application of AI. The combination of well-characterized biomarkers, relatively standardized analytical workflows, and extensive neurological datasets has facilitated the development of ML models capable of improving disease classification, prognostic stratification, and biomarker discovery, particularly in neurodegenerative disorders such as Alzheimer's disease. The application of AI to CSF analysis has primarily focused on improving the diagnosis and prognostic stratification of neurological diseases, particularly neurodegenerative disorders such as Alzheimer's disease, while also supporting the differential diagnosis of infectious and inflammatory conditions. To achieve these goals, published studies have investigated a wide range of data sources, including conventional CSF biomarkers such as amyloid-β, total tau, and phosphorylated tau, as well as proteomic and metabolomic datasets integrated with demographic and clinical information. Most AI approaches rely on supervised ML techniques designed to classify disease states or predict disease progression, with Support Vector Machines, Random Forests, and Artificial Neural Networks among the most frequently employed algorithms. Several studies have reported promising results. For example, Bellomo et al. demonstrated that ML models applied to conventional CSF biomarkers improved disease characterization across different neurological disorders (17) . In addition, Hou et al. developed a Support Vector Machine-based framework that integrated multiple CSF proteomic datasets comprising more than 1,200 samples and identified a 12-protein biomarker panel capable of diagnosing Alzheimer's disease with an accuracy exceeding 90% (18) . Likewise, Tiwari et al. reported diagnostic accuracies approaching 84% in distinguishing mild cognitive impairment from Alzheimer's disease using routinely available CSF biomarkers (19). Despite these encouraging findings, important limitations remain. Most studies have been conducted retrospectively in single-center settings and have relied predominantly on internal validation procedures. Furthermore, substantial heterogeneity exists in cohort characteristics, biomarker selection, analytical platforms, and model development strategies, making direct comparisons challenging and limiting the generalizability of the results. Therefore, while current evidence suggests that AI may represent a valuable support tool for CSF interpretation, its role should presently be considered complementary to clinical judgment rather than a substitute for established diagnostic pathways.

5.2. Serous Effusions

Serous effusions represent one of the most promising areas for AI-assisted digital pathology because diagnostic interpretation frequently depends on the recognition of subtle cytological features together with biochemical and clinical information. Recent advances in digital cytology and DL have substantially improved the automated detection and classification of malignant cells, opening new opportunities for AI-assisted cytopathology. The application of AI to the analysis of serous effusions has primarily focused on improving the etiological classification of pleural, peritoneal, and pericardial fluids, particularly by distinguishing benign from malignant effusions and supporting the diagnosis of infectious and inflammatory diseases. This represents a challenging diagnostic scenario because the interpretation of serous fluids often requires the integration of biochemical, cytological, and clinical information. To address this complexity, published studies have explored a variety of data sources, including biochemical parameters such as protein concentration, lactate dehydrogenase, pH, and glucose levels, cytological findings and cellular composition, digital whole-slide cytology images, as well as demographic and clinical variables. Most AI applications in this field rely on supervised classification algorithms or automated image analysis approaches based on deep learning techniques. Additional methodologies include object detection for cellular identification, image segmentation, and predictive models designed to improve diagnostic accuracy and workflow efficiency. Recent evidence has been particularly encouraging in the area of digital cytopathology (20). Park et al. developed a deep convolutional neural network for pleural fluid cytology that achieved an overall diagnostic accuracy of 98.6% in detecting malignant cells, providing valuable support to conventional cytological evaluation (21). Similarly, Zhang et al. proposed a whole-slide image-based deep learning model capable of discriminating malignant from benign pleural effusions, reporting an area under the curve (AUC) of 0.97 and a diagnostic accuracy approaching 97% (22) . More recently, Giarnieri et al. introduced user-friendly AI-assisted image analysis systems based on object-detection algorithms, including YOLO architectures, demonstrating the potential of these tools to simplify cytological interpretation while maintaining high diagnostic performance (23) . Despite these promising results, several limitations should be acknowledged. Most available studies are retrospective and rely on highly selected image datasets rather than consecutive routine clinical samples. In addition, algorithm development still depends heavily on manual image annotation by expert cytopathologists, and external multicenter validation remains limited. Consequently, although AI-assisted cytology shows considerable promise for the evaluation of serous effusions, further prospective studies based on standardized digital pathology workflows are needed before widespread implementation in routine laboratory practice can be recommended.

5.3. Synovial Fluid

Compared with cerebrospinal fluid and serous effusions, AI applications in synovial fluid analysis remain at an earlier stage of development. Nevertheless, the combination of spectroscopic technologies, digital microscopy, and ML has demonstrated promising potential for improving the differential diagnosis of inflammatory, crystal-induced, infectious, and degenerative joint diseases. The application of AI to synovial fluid analysis is primarily aimed at improving the differential diagnosis of septic arthritis, inflammatory arthropathies, crystal-induced arthritis, and degenerative joint diseases. The interpretation of synovial fluid is often complex, requiring the integration of laboratory findings, microscopic examination, spectroscopic data, and clinical information. In this context, AI-based approaches may facilitate the extraction of clinically relevant information from multidimensional datasets and support more accurate diagnostic decision-making. Current studies have investigated a variety of data sources, including leukocyte count and differential, crystal identification, Raman spectroscopy data, inflammatory biomarkers, and demographic and clinical variables. Most published AI models rely on supervised ML techniques for classification and predictive modelling, with particular emphasis on spectroscopic analyses combined with ML algorithms to identify crystal types and distinguish between different arthropathies. Compared with cerebrospinal fluid and serous effusions, however, the application of AI to synovial fluid remains relatively underexplored. Among the available studies, Niessink et al. demonstrated that ML algorithms applied to Raman spectroscopy were able to correctly identify monosodium urate and calcium pyrophosphate crystals with an overall diagnostic accuracy of approximately 88% (24). Furthermore, AI-assisted spectroscopic platforms have reported diagnostic accuracies exceeding 94% in differentiating rheumatoid arthritis from osteoarthritis, suggesting that advanced computational methods may capture subtle molecular signatures that are not readily detectable through conventional microscopic evaluation alone (24). Although these findings highlight the potential of AI to enhance synovial fluid diagnostics, the current evidence base remains limited. Most studies involve relatively small patient populations and rely on highly specialized analytical technologies that are not routinely available in clinical laboratories. In addition, the majority of investigations are proof-of-concept studies with internal validation only, while independent external validation across diverse patient cohorts is still lacking. Therefore, further multicenter prospective studies are needed to confirm the clinical utility, reproducibility, and cost-effectiveness of AI-assisted synovial fluid analysis before its widespread adoption in routine laboratory practice can be justified.

The maturity and translational readiness of AI applications differ considerably across non-blood biological fluids. Currently, the most robust evidence supports AI-assisted interpretation of cerebrospinal fluid biomarkers and digital cytology of serous effusions, both of which have demonstrated promising diagnostic performance and a relatively advanced stage of development. By contrast, applications involving synovial fluid and multi-omics datasets remain largely exploratory and are predominantly confined to proof-of-concept studies. Despite the growing body of evidence, several challenges continue to limit clinical translation, including the predominance of retrospective study designs, substantial heterogeneity in patient populations and analytical platforms, limited external validation, and the lack of standardized workflows for model development and implementation. As a result, most AI systems described to date should be regarded as tools that support, rather than replace, expert laboratory and clinical interpretation. Moving forward, the successful integration of AI into routine laboratory medicine will require multicenter prospective studies, harmonized validation strategies, and rigorous assessments of real-world clinical utility and cost-effectiveness. A comparative overview of the current evidence level, methodological robustness, and clinical readiness of AI applications across different non-blood biological fluids is presented in Table 2.

The table summarizes representative studies investigating the integration of proteomics, metabolomics, metagenomics, and multimodal multi-omics data with AI techniques for the analysis of non-blood biological fluids. For each application, the biological fluid, clinical objective, AI approach, representative references, and principal methodological limitations are reported. Current evidence indicates that proteomics represents the most mature application, particularly in CSF analysis for neurodegenerative diseases, whereas metabolomics and metagenomics remain promising but still require larger multicenter studies, standardized analytical workflows, and external validation before routine clinical implementation.

6. Integration of Multi-Omics and AI in Biological Fluid Diagnostics

The convergence of multi-omics technologies and AI represents one of the most significant advances in contemporary laboratory medicine, considerably expanding the diagnostic potential of non-blood biological fluids. High-throughput analytical platforms, including proteomics, metabolomics, and metagenomics, now enable comprehensive characterization of molecular signatures associated with neurological, infectious, inflammatory, and neoplastic diseases. Rather than focusing on individual biomarkers, these complementary technologies provide a multidimensional view of disease biology by capturing molecular alterations across different biological layers.

The increasing complexity of these datasets has rapidly exceeded the capabilities of conventional statistical approaches. Consequently, ML and DL have become essential tools for integrating heterogeneous biological information, identifying hidden molecular relationships, prioritizing candidate biomarkers, and generating clinically meaningful diagnostic models (25–27).

Among the different omics disciplines, proteomics currently represents the most mature application in CRF diagnostics. The availability of relatively standardized proteomic datasets and well-characterized neurological phenotypes has facilitated the development of robust ML models capable of improving disease classification and molecular stratification in neurodegenerative disorders. Hou et al. developed a Support Vector Machine (SVM)-based framework integrating proteomic data from more than 1,200 CSF samples and identified a 12-protein biomarker panel capable of diagnosing Alzheimer's disease with an overall accuracy exceeding 90% [18]. Similarly, Bader et al. applied multiple ML algorithms to quantitative CSF proteomic profiles and identified reproducible protein signatures that discriminated Alzheimer's disease from cognitively healthy individuals across independent cohorts [28,33]. More recently, Scalia et al. combined data-independent acquisition proteomics with machine-learning techniques to identify molecular signatures associated with distinct Alzheimer's disease pathological subtypes, further supporting the role of AI-assisted proteomics in biomarker discovery and patient stratification [29,34].

Metabolomics provides complementary information by capturing the dynamic biochemical alterations associated with disease onset and progression. Because metabolomic datasets comprise hundreds of highly correlated variables, ML algorithms are particularly well suited to identifying subtle metabolic signatures that would be difficult to recognize using conventional statistical approaches. Recent studies have demonstrated that AI-assisted metabolomic profiling improves the discrimination between Alzheimer's disease, mild cognitive impairment, Parkinson's disease, and healthy controls. For example, Berezhnoy et al. showed that NMR-based metabolomic profiling combined with multivariate computational analysis successfully differentiated patients with Alzheimer's disease and mild cognitive impairment from cognitively healthy individuals [30]. Preliminary evidence also suggests that metabolomic profiling may contribute to the characterization of inflammatory and malignant conditions in pleural and synovial fluids, although these applications remain largely exploratory.

Metagenomics has further expanded the diagnostic potential of biological fluid analysis, particularly in the evaluation of central nervous system infections. The integration of metagenomic next-generation sequencing (mNGS) with AI-assisted bioinformatic pipelines has substantially improved pathogen detection, especially in patients with meningitis and encephalitis for whom conventional microbiological methods remain inconclusive [31]. Unlike targeted microbiological assays, mNGS enables the unbiased detection of bacterial, viral, fungal, and parasitic nucleic acids directly from clinical specimens. AI-based analytical tools further enhance this process by supporting sequence classification, pathogen prioritization, contamination filtering, and the integration of molecular findings with laboratory and clinical data, thereby facilitating the interpretation of highly complex sequencing datasets [32]. Despite these encouraging advances, most metagenomic applications remain confined to research settings because of computational complexity, limited analytical standardization, and the lack of prospective multicenter validation. Recent reviews consistently emphasize the need for standardized bioinformatic pipelines, robust quality-control procedures, and harmonized interpretation criteria before AI-assisted metagenomics can be routinely implemented in clinical laboratory practice [33].

Collectively, these developments indicate that laboratory medicine is progressively moving beyond isolated omics technologies toward integrated multi-omics frameworks capable of simultaneously incorporating proteomic, metabolomic, genomic, microbiological, imaging, and clinical information (Figure 2). By capturing complex biological interactions rather than relying on individual biomarkers, these multimodal approaches have the potential to improve disease stratification, identify novel molecular endotypes, and support more precise diagnostic and prognostic assessment. As summarized in Table 3, proteomics currently provides the strongest evidence for AI-assisted biomarker discovery in CRF, whereas metabolomics and metagenomics remain promising but require broader prospective validation before widespread clinical implementation.

Table 3. Current maturity level and clinical readiness of Artificial Intelligence applications in the analysis of non-blood biological fluids. Evidence level refers to the methodological robustness of available studies, including cohort size, study design, and validation strategy. This table summarizes the current level of evidence, degree of external validation, and clinical implementation status of AI-based approaches applied to different biological fluids. Evidence level reflects the methodological robustness of available studies, considering study design, cohort characteristics, validation strategy, and reproducibility.

Biological fluid	Main AI application	Evidence level	External validation	Current clinical readiness
Cerebrospinal fluid (CSF)	Interpretation of neurodegenerative biomarkers; disease classification; prognostic stratification; integration of biochemical and omics data	Moderate	Limited (mostly internal or single-center external)	Emerging; mainly applied in research settings and specialized centers
Serous effusions (pleural, peritoneal, pericardial fluids)	Digital cytology; malignant cell detection; whole-slide image analysis; image-based classification	Moderate–High	Limited (few external datasets)	Pilot implementation; requires prospective multicenter validation before routine clinical adoption
Synovial fluid	Crystal identification; Raman spectroscopy-based classification; prediction of inflammatory and degenerative joint diseases	Low–Moderate	Very limited	Experimental; currently restricted to proof-of-concept studies
Multi-omics approaches (CSF, serous effusions, synovial fluid)	Biomarker discovery; molecular classification; integration of proteomics, metabolomics, metagenomics, and clinical data for precision diagnostics	Low–Moderate	Rare Research setting only	Research setting only

* Evidence level definitions:.

Table 3. Current maturity level and clinical readiness of Artificial Intelligence applications in the analysis of non-blood biological fluids. Evidence level refers to the methodological robustness of available studies, including cohort size, study design, and validation strategy. This table summarizes the current level of evidence, degree of external validation, and clinical implementation status of AI-based approaches applied to different biological fluids. Evidence level reflects the methodological robustness of available studies, considering study design, cohort characteristics, validation strategy, and reproducibility.

Biological fluid	Main AI application	Evidence level	External validation	Current clinical readiness
Cerebrospinal fluid (CSF)	Interpretation of neurodegenerative biomarkers; disease classification; prognostic stratification; integration of biochemical and omics data	Moderate	Limited (mostly internal or single-center external)	Emerging; mainly applied in research settings and specialized centers
Serous effusions (pleural, peritoneal, pericardial fluids)	Digital cytology; malignant cell detection; whole-slide image analysis; image-based classification	Moderate–High	Limited (few external datasets)	Pilot implementation; requires prospective multicenter validation before routine clinical adoption
Synovial fluid	Crystal identification; Raman spectroscopy-based classification; prediction of inflammatory and degenerative joint diseases	Low–Moderate	Very limited	Experimental; currently restricted to proof-of-concept studies
Multi-omics approaches (CSF, serous effusions, synovial fluid)	Biomarker discovery; molecular classification; integration of proteomics, metabolomics, metagenomics, and clinical data for precision diagnostics	Low–Moderate	Rare Research setting only	Research setting only

* Evidence level definitions:.

• Low: proof-of-concept studies, small cohorts, retrospective design, no external validation
• Moderate: retrospective multicenter or internally validated studies with reasonable sample size
• High: external validation and/or prospective studies with clinical implementation components

Figure 2. Conceptual framework of AI-assisted multi-omics integration in non-blood biological fluid analysis. Data generated from proteomics, metabolomics, metagenomics, clinical assessment, and medical imaging are combined within AI platforms to identify molecular signatures, improve disease stratification, and support CDSS. The integration of these complementary data layers enables a transition from isolated biomarker interpretation toward precision laboratory medicine and personalized healthcare.

Figure 2. Conceptual framework of AI-assisted multi-omics integration in non-blood biological fluid analysis. Data generated from proteomics, metabolomics, metagenomics, clinical assessment, and medical imaging are combined within AI platforms to identify molecular signatures, improve disease stratification, and support CDSS. The integration of these complementary data layers enables a transition from isolated biomarker interpretation toward precision laboratory medicine and personalized healthcare.

7. Challenges and Limitations

Despite the growing interest in AI-assisted analysis of non-blood biological fluids, several scientific, technical, regulatory, and ethical challenges continue to limit its widespread clinical implementation. One of the principal obstacles concerns the availability, quality, and representativeness of the datasets used for model development. Most published studies remain retrospective, include relatively small and highly selected patient cohorts, and rely predominantly on internal validation, raising concerns about reproducibility, external validity, and generalizability across different clinical settings [34,35].

These limitations are further compounded by substantial variability throughout the analytical workflow, including sample collection, pre-analytical handling, analytical platforms, calibration procedures, reagent lots, feature extraction strategies, and computational pipelines. Such heterogeneity reduces data comparability across institutions and limits the transferability of AI models developed using single-center datasets.

Another major challenge is the limited analytical standardization of many non-blood biological fluids. Unlike conventional blood-based testing, these specimens often present matrix-specific characteristics and less harmonized laboratory procedures, making the development of robust, reproducible, and clinically transferable AI models particularly demanding. Model interpretability represents an additional concern. The limited transparency of complex deep learning architectures may hinder clinical acceptance and reduce confidence among laboratory professionals and clinicians, who ultimately remain responsible for diagnostic decision-making [9].

Regulatory and ethical considerations constitute further barriers to implementation. AI-based diagnostic tools require rigorous clinical validation together with continuous post-deployment monitoring to ensure analytical validity, clinical reliability, safety, and reproducibility throughout their lifecycle. At the same time, the increasing integration of large-scale clinical and multi-omics datasets raises important issues related to data governance, patient privacy, cybersecurity, and secure data management within healthcare systems.

Recent recommendations from the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) emphasize that successful implementation of AI in laboratory medicine requires harmonized analytical procedures, comprehensive metadata documentation, robust external validation, transparent model development, and continuous performance monitoring after clinical deployment (32,36,37). Ultimately, the routine adoption of AI-driven CDSS will depend not only on technological innovation but also on close collaboration among laboratory professionals, clinicians, data scientists, industry partners, and regulatory authorities to ensure that computational advances translate into clinically reliable, safe, and patient-centered diagnostic practice.

8. Future Perspectives

The future adoption of AI-driven tools in biological fluid analysis will likely depend less on further advances in algorithm development than on the establishment of a robust analytical and digital infrastructure capable of supporting their safe and effective implementation in routine clinical practice. Progress in computational resources, data integration frameworks, and CDSS is already enabling the development of increasingly sophisticated diagnostic models that combine laboratory, clinical, imaging, and genomic information within unified decision-making frameworks (36–39).

Achieving this transition will require high-quality, well-curated datasets, standardized analytical workflows, robust external validation, and seamless interoperability with Laboratory Information Systems (LIS) and Electronic Health Records (EHR). At the same time, the convergence of digital pathology, automated cytology, advanced molecular diagnostics, and multi-omics technologies is expected to further expand the diagnostic value of non-blood biological fluids by enabling more comprehensive and biologically informed disease characterization.

Equally important will be the development of explainable AI models capable of providing transparent and clinically interpretable outputs. Together with large multicenter collaborations and data-sharing initiatives, these advances will facilitate regulatory approval, strengthen clinician confidence, and improve the generalizability of AI models across diverse healthcare settings [38,39].

Ultimately, the success of AI in biological fluid diagnostics will not be determined solely by algorithmic performance. Its clinical value must be demonstrated through prospective implementation studies evaluating diagnostic accuracy, patient outcomes, workflow efficiency, and cost-effectiveness. Only through the integration of technological innovation, analytical standardization, and clinical validation will AI-driven CDSS become an integral component of precision laboratory medicine [32].

9. Conclusions

The integration of AI into the analysis of non-blood biological fluids represents more than a technological advance; it reflects a fundamental evolution in the role of laboratory medicine. As biological data become increasingly multidimensional, conventional diagnostic approaches based on the interpretation of isolated laboratory parameters are progressively giving way to integrative models capable of combining biochemical, cytological, microbiological, molecular, imaging, and clinical information within a unified diagnostic framework.

Current evidence indicates that the maturity of AI applications varies substantially across different biological fluids. AI-assisted interpretation of cerebrospinal fluid biomarkers and digital cytology of serous effusions currently provide the strongest clinical evidence, whereas applications involving synovial fluid and integrated multi-omics remain largely exploratory. Nevertheless, the rapid convergence of AI with high-throughput omics technologies is accelerating biomarker discovery, improving disease stratification, and creating new opportunities for precision laboratory medicine.

Despite these advances, important methodological, analytical, regulatory, and ethical challenges must still be addressed before AI-driven CDSS can be routinely implemented in clinical laboratories. Robust multicenter validation, standardized analytical workflows, transparent and interpretable algorithms, and harmonized regulatory frameworks will be essential to ensure that AI systems are reliable, reproducible, and clinically trustworthy.

Looking ahead, the greatest contribution of AI is unlikely to be the replacement of laboratory professionals, but rather its ability to support biological interpretation by integrating heterogeneous sources of information into clinically meaningful knowledge. In this perspective, laboratory medicine is evolving from the measurement of individual biomarkers toward the interpretation of complex biological systems. The successful integration of AI, multi-omics technologies, and expert laboratory interpretation has the potential to transform laboratory medicine from a discipline primarily focused on measuring biomarkers into one centered on the interpretation of complex biological systems, ultimately strengthening its role within precision healthcare.