Key challenges and future research directions.
| Challenge category | Specific challenge | Current limitation | Future direction | Key references |
|---|---|---|---|---|
| Data characteristics | High dimensionality | Overfitting; unstable feature selection; curse of dimensionality | Self-supervised learning; foundation models; synthetic data generation | [6, 8] |
| Data heterogeneity | Integration difficulty; modality-specific characteristics lost | Advanced fusion strategies; modality-specific encoders with shared representations | [9, 40] | |
| Batch effects | Confounded models; poor generalization | Standardized preprocessing; robust normalization; batch-invariant representations | [14, 75] | |
| Missing modalities | Incomplete data; biased imputation | Masked modeling; generative imputation; modality-agnostic architectures | [15, 53] | |
| Methodological | Interpretability vs. performance trade-off | Black-box models outperform interpretable ones | Hybrid models; intrinsically interpretable architectures; attention mechanisms | [19, 22] |
| Explanation faithfulness | Post-hoc explanations may not reflect model behavior | Faithfulness benchmarks; causal explanation methods; model-specific XAI | [21, 45] | |
| Scalability | Computational cost of training and explaining large models | Efficient attention; sparse architectures; federated learning | [108, 109] | |
| Evaluation | Lack of standardized benchmarks | Inconsistent evaluation; incomparable results | Community benchmarks (e.g., MOB); standardized metrics for XAI | [42, 50] |
| Reproducibility | Variability in preprocessing, splits, seeds | Open code; shared pipelines; reproducible workflows | [14, 73] | |
| Clinical validation | Few prospective studies; limited external validation | Multi-center trials; real-world evidence; regulatory pathways | [34, 60] | |
| Translational | Workflow integration | Models not integrated with EHR/clinical systems | Interoperability standards (HL7 FHIR); user-centered design | [29, 35, 97] |
| Regulatory approval | Unclear pathways for AI as medical device | Engage regulators early; validation frameworks; post-market surveillance | [18] | |
| Ethical and fairness concerns | Bias in training data; privacy risks | Federated learning; fairness audits; inclusive cohort design | [32, 60] | |
| Emerging frontiers | Causal inference | Correlation-based predictions limit mechanistic insight | Causal AI; Mendelian randomization; interventional predictions | [14, 110] |
| Foundation models | Limited pre-trained models for multi-omics | Multi-modal foundation models; transfer learning; open-source models | [108, 111] | |
| Digital twins | No integrated patient simulations | Multi-modal digital twins; generative models; personalized simulations | [112, 113] | |
| Human-in-the-loop | AI decisions without clinician oversight | Interactive XAI; clinician feedback loops; collaborative AI | [32, 33] |
XAI: explainable artificial intelligence.
During the preparation of this work, the authors used Google AI tools to improve language and readability. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.
MH: Conceptualization, Writing—original draft, Visualization. SH: Writing—review & editing, Supervision. KA: Methodology, Writing—review & editing, Formal analysis. MW: Methodology, Formal analysis, Writing—review & editing. All authors have read and approved the final version of the manuscript.
The authors declare that they have no conflicts of interest.
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No new data were generated or analyzed in this review. All referenced datasets are publicly available from the sources cited.
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