AI-Based Biomarkers for Precision Medicine

Updated 14 November 2025

AI-based biomarkers are algorithmically derived features extracted via machine learning from high-dimensional clinical and biological data for precise diagnosis and prognosis.
They integrate diverse modalities such as imaging, omics, and physiological signals to uncover subtle disease patterns beyond traditional measurement methods.
Robust AI approaches, including deep learning and ensemble models, validate these biomarkers through rigorous cross-validation, interpretability techniques, and clinical benchmarking.

AI-based biomarkers are algorithmically derived features or composite measures, often from complex, high-dimensional biological or clinical data, that enable diagnosis, prognosis, risk stratification, or therapy response monitoring, with performance improved or made feasible by machine learning or other AI methodologies. Unlike traditional biomarkers, which are typically defined by a priori hypotheses or straightforward measurement of a single entity (e.g., protein, metabolite), AI-based biomarkers are discovered or quantified through models that integrate multi-modal data, learn data-driven representations, or identify subtle interactions—often beyond human discernibility—in signal, imaging, genomics, transcriptomics, clinical, or behavioral datasets.

1. Definitions and Conceptual Scope

AI-based biomarkers encompass any measurable characteristic derived using AI, typically employing ML, deep learning (DL), or more advanced generative or reinforcement learning approaches, that is objectively associated with clinical, physiological, or pathological states. These biomarkers are commonly discovered through supervised/unsupervised learning on large datasets, enabling complex feature extraction and multi-factorial integration. Categories include:

Imaging-based biomarkers (e.g., radiological, histopathological, retinal, cardiac)
Omics-derived biomarkers (e.g., genomic, transcriptomic, proteomic, metabolomic)
Physiological time-series biomarkers (e.g., EEG, fNIRS, speech, ECG)
Composite multimodal indices (combining clinical, laboratory, imaging, behavioral, and environmental data)
Latent digital signatures (extracted features or embeddings predictive of disease status, progression, or outcome)

Key properties include reproducibility, generalizability, interpretability (sometimes achieved post hoc), and robust correlation with clinically meaningful endpoints.

2. Methodological Principles for Discovery and Validation

Data Acquisition and Preprocessing

AI-based biomarker pipelines typically require standardized, high-quality acquisition, often from multi-center studies to guarantee generalizability. Preprocessing steps are domain-specific, including intensity normalization, denoising, artifact correction, multimodal registration, and harmonization across platforms (e.g., MNI-space alignment for neuroimaging (Barbero et al., 2023), ComBat batch correction for gene expression (Punzo et al., 26 Sep 2025)). Careful handling of missing data, signal normalization, and outlier removal (or imputation) are integral, especially for real-world or EHR-derived datasets (Ahmed et al., 9 Mar 2025).

Feature Extraction and Representation

AI approaches extract candidate biomarker features using ML models attuned to complex data characteristics. Examples include:

Riemannian geometry of EEG covariance: Covariance matrices from multichannel EEG are treated as points on the symmetric positive-definite (SPD) manifold, with features consisting of their distances, means, and tangent-space projections (Rutkowski et al., 2018).
Regional PET/MRI loadings: Normalized uptake or volumetric measures across brain parcellations serve as input for supervised classifiers (Barbero et al., 2023).
Omics embedding: Latent encodings via autoencoders, variational autoencoders, or generative transformers compress high-dimensional molecular data for downstream optimization (Ying et al., 23 Sep 2024).
Saliency/attribution maps: Grad-CAM, SHAP, or LIME techniques enable identification of key features or anatomical regions driving model predictions (Jimenez-Mesa et al., 7 Jul 2025, Coluzzi et al., 2023, Islam et al., 26 Sep 2025).

Model Training, Selection, and Validation

Typical approaches include:

Classical supervised learning: SVMs (with linear, polynomial, or RBF kernels), random forests, regularized regression, and ensemble tree methods (Rudroff et al., 25 Jun 2024, Bavikadi et al., 16 May 2024, Barbero et al., 2023).
Deep learning & self-supervised methods: CNNs (e.g., ResNet, DenseNet, U-Net), GNNs, and transformer-based architectures (e.g., Swin-UNETR, Vision Transformer) (Coluzzi et al., 2023, Guo et al., 28 Oct 2025, Kunhoth et al., 1 Dec 2024).
Generative/fusion approaches: Multi-agent reinforcement learning for subset selection, encoder–evaluator–decoder transformers for combinatorial biomarker optimization (Ying et al., 23 Sep 2024).
Multimodal integration: Early or late fusion, multi-kernel learning, stacking, or meta-learners integrate signals from heterogeneous sources (Rudroff et al., 25 Jun 2024, Kushner et al., 27 Aug 2025, Ahmed et al., 9 Mar 2025).

Validation rigorously employs cross-validation (k-fold, leave-one-out), external test sets, and, where possible, prospective or longitudinal studies. Metrics include accuracy, precision, recall/sensitivity, specificity, F1-score, area under the ROC curve (AUC), mean absolute error (MAE), and Cox proportional hazards for time-to-event analyses.

3. Exemplary Applications Across Domains

Neurodegenerative Disease

EEG-based digital biomarkers for dementia: Using information geometry, tangent-space mapping, and SVM/LDA, trial-to-trial EEG covariance features in event-related potentials (esp. P300 amplitude and latency) differentiate high- vs. low-task-load states, mimicking Alzheimer's and MCI pathophysiology, with cross-validated accuracy >70% (Rutkowski et al., 2018).
PET amyloid imaging: Cubic-kernel SVMs operating on regionally normalized Aβ loadings from [18F]-florbetaben PET scans, combined with LIME for explainability, achieve 92% accuracy, identifying key anatomical predictors paralleling clinical radiologist interpretation (Barbero et al., 2023).
Multimodal AD classification: Structural and diffusion MRI features, parsed via 3D CNNs and graph convolutional networks, capture both regional atrophy (medial temporal lobe) and connectivity disruptions (default mode network), with performance improved by feature-level complementarity and cross-modality interpretability (Coluzzi et al., 2023).

Oncology and Pathology

Digital pathology for molecular stratification: CNNs, MIL, and transformer-based models operate on gigapixel H&E WSIs to predict gene mutations (e.g., BRCA1/2, TP53), RNA/protein expression (ESR1, HER2, MKI67), and molecular subtypes, achieving AUCs up to 0.83 for BRCA1/2 and 0.92 for ER status, with advanced stain normalization and attention mechanisms addressing dataset and site variability (Kunhoth et al., 1 Dec 2024).
Radiomics-derived markers: Deep learning segmentation of airways in fibrotic lung disease enables robust extraction of total airway volume (TAV), branch count, and other geometric-radiomic biomarkers, with TAV independently predictive of survival (HR=2.58 univariate, p<0.0001) (Nan et al., 2023).
Peritumoral microenvironment markers: Infiltration heterogeneity around glioblastomas is quantified using DTI-derived free water fraction, deep CNN-extracted peritumoral microenvironment index (PMI), and composite spatial/shape/directional heterogeneity metrics, which stratify survival and IDH1 mutation status (Samani et al., 2022).

Systemic and Multimodal Applications

Aging and frailty clocks: Ensembles of ML, DNN, and time-series models integrate biochemical (CRP, IGF-1, IL-6, GDF-15), wearable sensor, and clinical features, yielding MAE for biological age as low as 2.7 years and C-index improvements for mortality from 0.68 (baseline) to 0.74 (Kushner et al., 27 Aug 2025).
Cancer cachexia detection: Early fusion of tabular, lab, radiological (CT), and clinical note embeddings in an ensemble of MLPs allows dynamic, context-adaptive biomarker decision thresholds and internal confidence estimation, enabling real-world, scalable adoption (Ahmed et al., 9 Mar 2025).
Speech biomarkers for dementia: The OVBM framework integrates 16 complementary audio and cognitive biomarkers (e.g., cough, sentiment, word usage) through a modular GNN, achieving 93.8% accuracy on raw speech for Alzheimer's detection, with personalized saliency mapping for longitudinal tracking (Soler et al., 2021).

4. Model Interpretability and Biomarker Transparency

Interpretability is approached via three main strategies:

Intrinsic model explainability (e.g., feature importance in tree-based classifiers, tangent-space representations)
Post hoc explainability: LIME, SHAP, Grad-CAM, and global explanation optimizers are used to attribute model decisions to specific features, anatomical regions, or subsets, often enabling visualization as saliency or relevance maps (Barbero et al., 2023, Jimenez-Mesa et al., 7 Jul 2025, Islam et al., 26 Sep 2025).
Biological/pathological alignment: For instance, AI models for Alzheimer's must highlight hippocampal atrophy or default mode disruptions in agreement with neuropathological understanding, which fosters clinician trust and adoption (Coluzzi et al., 2023).

5. Evaluation Metrics, Performance, and Benchmarking

Performance metrics are rigorously defined and directly tied to clinical or biological questions. Common metrics include:

Accuracy, sensitivity/recall, specificity, precision, F1-score
AUC for classification tasks (values ≥0.85–0.95 in top imaging and -omics tasks)
MAE, RMSE for regression (e.g., age estimation, continuous biomarker values)
Hazard ratios (HR) and Cox proportional hazards for prognostic markers
Faithfulness, sparseness, and structural similarity (for explainable AI methods) (Jimenez-Mesa et al., 7 Jul 2025)

Repeated cross-validation, external cohort testing, and, where possible, longitudinal consistency (e.g., scan-rescan Bland–Altman analysis, reproducibility metrics) are emphasized (Wickremasinghe et al., 21 Aug 2024).

6. Limitations, Challenges, and Future Directions

Key challenges include:

Generalizability and Data Diversity: Current AI biomarkers are often trained on single-center or demographically homogeneous datasets, limiting translatability. Cross-center harmonization, federated learning, and large-scale prospective validation are priorities (Kushner et al., 27 Aug 2025, Rudroff et al., 25 Jun 2024).
Interpretability and Clinical Adoption: Despite advanced XAI, many deep models remain black boxes or lack biological justification for identified features. Practical adoption requires user-friendly tools for visualization, integrated workflows, and clinician-in-the-loop validation (Barbero et al., 2023, Kunhoth et al., 1 Dec 2024).
Bias, Drift, and Dataset Shift: Variable acquisition protocols, demographic effects, and technical noise can introduce artifacts and spurious correlations. Harmonization, uncertainty quantification, and robust model calibration are necessary (Ahmed et al., 9 Mar 2025).
Lack of Multidimensional, Longitudinal Baselines: For complex diseases (esp. neuropsychiatric), reliance on cohort comparisons ignores degeneracy and redundancy inherent in biological systems. Future directions propose subject-centric, multimodal, and longitudinal data collection, supported by subgroup-specific or dynamic biomarker modeling (Helson et al., 8 Sep 2025).
Ethical, Legal, and Regulatory Considerations: Privacy-preserving analytics, regulatory compliance (SaMD), and transparent performance reporting are seen as critical bottlenecks to clinical deployment (Rudroff et al., 25 Jun 2024, Kushner et al., 27 Aug 2025).

7. Theoretical, Computational, and Practical Implications

The advent of AI-based biomarkers has redefined what constitutes a measurable disease signature in modern medicine. Shifting from monofactorial, hand-engineered metrics to multivariate, AI-discovered representations, the field can now:

Detect subclinical or pre-symptomatic disease (e.g., changes in P300 ERP 5–10 years before clinical dementia (Rutkowski et al., 2018))
Resolve ambiguity and observer bias in imaging interpretation (e.g., PET Aβ status with explainable accuracy matching expert readers (Barbero et al., 2023))
Quantify complex microenvironments (e.g., peritumoral DTI-based PMI hubs (Samani et al., 2022))
Continuously update and recalibrate individual risk estimation (e.g., adaptive thresholds in cachexia (Ahmed et al., 9 Mar 2025), streaming aging clocks (Kushner et al., 27 Aug 2025))
Systematically identify high-utility marker subsets via generative optimization (bypassing combinatorial explosion in subset search (Ying et al., 23 Sep 2024))

A plausible implication is that continued innovation in AI-based biomarker discovery and validation—driven by richer data, robust multimodal fusion, and ever more transparent algorithms—will form the backbone of future precision medicine and preventive healthcare. However, meaningful clinical impact will depend on rigorous benchmarking, validation across diverse populations, and tight integration into actionable medical workflows.