Table 3.
Categorization of artificial intelligence techniques in reviewed studies: a comprehensive list of artificial intelligence (AI) categories identified in the 18 reviewed studies, detailing specific algorithms and associated study references. Categories include supervised learning models, clustering techniques, deep learning, reinforcement learning, natural language processing (NLP), simulation models, and conceptual frameworks.
| AI category | Specific algorithms/methods | Studies referenced |
| Supervised learning (MLa) | Logistic regression, LightGBMb, SVCc, random forest, ensemble methods | Zhu et al (2024) [20], Quddos et al (2023) [27], Foer et al (2023) [25], Shao et al (2022) [36], Giorda et al (2023) [24], Yamada et al (2020) [5] |
| Unsupervised learning (clustering) | K-means, UMAPd | Quddos et al (2023) [27] |
| Deep learning | CNNe, DNNf, 3D U-Net, Mask-RCNNg | Nelson et al (2024) [13], Yamada et al (2020) [5] |
| Statistical modeling/traditional analytics | Hierarchical Bayesian modeling, standard regression models | Beavers et al (2025) [28], Quddos et al (2023) [27], Warren et al (2024) [32], Mozaffarian (2024) [9] |
| Risk prediction engines/simulation models | BRAVOh diabetes model, microsimulation, cost-effectiveness simulations | Shao et al (2022) [36], Giorda et al (2023) [24], Warren et al (2024) [32] |
| NLP | Clinical note parsing, COPDi phenotyping algorithms | Foer et al (2023) [25] |
| Adaptive/reinforcement learning | AI-driven insulin titration (d-Nav System) | Warren et al (2024) [32] |
| Explainable AI/rule-based systems | LLMj | Giorda et al (2023) [24] |
| Conceptual/editorial AI integration | Narrative discussion, strategic integration frameworks | Crea (2024) [22], Cheng et al (2024) [31], Mozaffarian (2024) [9], Ansari et al (2023) [35], Armandi and Schattenberg (2022) [37], Kovatchev (2019) [3], Vitorino (2025) [14] |
| Omics and systems biology integration | Proteomics/metabolomics (no direct AI implementation; discussed future AI integration) | Vitorino (2025) [14] |
aML: machine learning.
bLightGBM: light gradient boosting machine.
cSVC: support vector classifier.
dUMAP: Uniform Manifold Approximation and Projection.
eCNN: convolutional neural network.
fDNN: deep neural network.
gRCNN: region-based convolutional neural network.
hBRAVO: Building, Relating, Assessing, and Validating Outcomes.
iCOPD: chronic obstructive pulmonary disease.
jLLM: large language model.