AI-Based Disease Detection Framework

• AI-based disease detection frameworks are integrated platforms that use machine and deep learning to diagnose diseases from heterogeneous data sources.
• They employ multi-stage pipelines including closed-loop, open clinical, multimodal, and edge-to-cloud architectures to enhance accuracy and adaptability.
• These systems provide real-time feedback and personalized diagnostic recommendations, optimizing clinical workflows and cost-effective healthcare delivery.

AI-based disease detection frameworks are integrated systems that employ machine learning and deep learning models to identify, classify, or predict diseases from a variety of clinical, sensory, or imaging data sources. These frameworks have evolved into sophisticated platforms that support continuous data collection, individualized diagnosis, explainable predictions, and real-time feedback, enabling their use in diverse domains, including clinical medicine, telemedicine, agriculture, veterinary surveillance, and smart healthcare environments.

1. System Architectures and Data Workflows

AI-based disease detection frameworks are architected as multi-stage pipelines that coordinate the collection, filtering, preprocessing, modeling, and interpretation of heterogeneous medical data. Key architectural patterns include:

• Closed-loop frameworks: Systems such as AI-Skin (Chen et al., 2019) leverage a closed information loop in which user devices, edge nodes, and cloud servers iteratively exchange data and model updates. User terminals (e.g., smartphones, wearables) capture data; edge nodes perform preliminary filtering using information-theoretic measures (e.g., entropy-based sample selection); remote clouds orchestrate deep learning model training and parameter updates; and experts can provide feedback, closing the loop.
• Open, dynamic clinical architectures: Frameworks such as OpenClinicalAI (Huang et al., 2021) are built to handle real-world clinical settings characterized by incomplete data, unknown subject categories, and resource heterogeneity. The architecture comprises cooperative modules for diagnosis, data reconstruction, and dynamic exam recommendation, enabling iterative personalization and communication with clinicians.
• Multimodal and ensemble-based systems: Many frameworks integrate multiple data modalities—imaging (MRI, X-ray, CT, ultrasound), quantitative biomarkers, cognitive assessments, or sensor data—using modality-specific models (CNNs, LSTMs, GCNs) and robust aggregation (weighted averaging, majority voting, stacked generalization, probabilistic fusion) to yield resilient predictions, e.g., as in multimodal AD detection (Nagarhalli et al., 5 Aug 2025).
• Edge-to-cloud and IoT-integrated systems: In agriculture and digital healthcare, frameworks combine local edge inference (for low-latency tasks) with cloud-based heavy computation, leveraging IoT for sensor-rich environments, as exemplified by plant disease detection in aeroponic greenhouses (Narimani et al., 14 Sep 2025).

2. Data Processing, Filtering, and Augmentation

Robust data management is central to effective AI-based disease detection. Salient processes include:

• Entropy-based sample selection: In AI-Skin, information entropy of prediction probabilities,

$$E\left(p_{x_i^u}\right) = -\sum_{j=1}^{M} p_i^{j}\log\left(p_i^{j}\right),$$

is calculated for unlabeled candidate samples. Samples with entropy below a tunable threshold, $E_T$, are prioritized for cloud-level learning and expert evaluation to optimize labeling efficiency and reduce communication overhead.

• Data balancing and augmentation: For tabular clinical or sensor data (e.g., coronary artery disease (Nasarian et al., 2023)), class imbalance is compensated using methods like Borderline SMOTE for synthetic minority oversampling, and data augmentation is further performed with autoencoders to expand limited-size datasets. In imaging, GAN-based data generation and harmonization (Hwang et al., 16 Jul 2024) produce anatomically plausible synthetic samples to increase diversity and improve model generalization.
• Preprocessing for medical imaging: Intensity normalization, resizing, contrast enhancement (e.g., CLAHE (Kumar et al., 2023)), region-of-interest extraction (using Histogram Oriented Gradient or unsupervised segmentation), and affine transformations for alignment (e.g., PCA-based anatomical registration in aquaculture lesion detection) serve to standardize inputs and facilitate robust model training.
• Temporal and spectral transforms: In sensor-rich frameworks for neurological disease screening (Li et al., 2022), time-series data are converted to spectrograms using the short-time Fourier transform to allow CNN-based analysis.

3. Model Architectures and Algorithmic Strategies

A wide spectrum of algorithmic backbones is employed, selected according to the clinical context and data modality:

• Convolutional Neural Networks (CNNs): Used extensively for 2D and 3D image analysis (MRI, CT, X-ray, dermoscopy), with architectural variants including LeNet-5, AlexNet, VGG16 (Chen et al., 2019), RepVGG (Mayats-Alpay, 2022), and Inception-based U-Net hybrids (e.g., InceptNet (Sajedi et al., 2023)). Multi-model load modules allow flexible deployment and benchmarking.
• Transformer-based architectures: Vision Transformers (ViT), Swin Transformers, and DinoV2 leverage global attention and hierarchical structures to achieve advanced feature extraction for dermatological image classification, with transfer learning from large-scale datasets (e.g., ImageNet1k) resulting in high test accuracies (e.g., DinoV2 achieving 96.48% and F1=0.9727 (Mohan et al., 20 Jul 2024)).
• Graph-based neural networks: For interpretable neuroimaging (e.g., AD diagnosis), patch-based U-Net grading followed by GCN-based integration of regional grading, volume, and demographic variables captures inter-regional and intra-subject variability (Nguyen et al., 2022).
• Long Short-Term Memory (LSTM) networks: Deployed for sequential data analysis, notably longitudinal cognitive assessment and biomarker trajectory modeling in multimodal AD detection frameworks (Nagarhalli et al., 5 Aug 2025).
• Ensemble and decision fusion: Majority or confidence-weighted voting (across VGG, ResNet, Inception, DenseNet (Amin et al., 7 Mar 2024)), and multi-agent aggregation (confidence-weighted summation of hypotheses in swine surveillance (Mairittha et al., 19 Mar 2025)) underpin robust consensus and resistance to outlier effects.
• Replay-based continual learning: For wearable sensor-based multi-disease detection (Li et al., 2023), replay and synthetic data generation strategies ensure resilience to catastrophic forgetting and enable lifelong learning on edge devices.

4. Explainability, Personalization, and Clinical Integration

Explainability and adaptivity are critical for broad clinical acceptance and effective personalized care:

• Model interpretability: Grad-CAM (class activation mapping), SHAP (Shapley Additive Explanations), and LIME (Local Interpretable Model-agnostic Explanations) provide heatmaps, feature attributions, and linear approximations, individually or in combination, enabling clinicians to visualize and understand predictions (e.g., in skin lesion localization (Mohan et al., 20 Jul 2024), brain tumor detection (Amin et al., 7 Mar 2024)).
• Personalized examination and dynamic test recommendation: Systems such as OpenClinicalAI (Huang et al., 2021) analyze incoming incomplete data, assess confidence (OpenMax), personalize further diagnostic recommendations based on estimated utility and institutional resources, and consult clinicians when prediction confidence is low.
• Robust learning under noise: The use of dynamic truncated loss functions combined with mutual entropy weighting and gradient scaling suppresses the influence of likely mislabeled or unstable samples, as in deep learning models for stroke risk prediction amid label noise (Lin et al., 23 Jun 2024).
• Integration with IoT and edge/cloud: Real-time data streams from sensors are synchronized with predictive models to enable real-time feedback and remote intervention in plant pathology and environmental stress management (Narimani et al., 14 Sep 2025).
• User interface and workflow integration: Interactive platforms (e.g., Gradio for VLM-powered medical imaging (Al-Hamadani, 16 Sep 2025)) expose layered visualization, structured report generation, and compliance with clinical data management protocols (PACS, EHRs).

5. Experimental Performance, Applications, and Clinical Implications

Robust experimental validation underpins the clinical relevance of these frameworks. Key reported results and applications include:

Framework	Application Domain	Best Accuracy/F1/Metric
AI-Skin (Chen et al., 2019)	Skin disease recognition	AlexNet: 0.91 (blackheads), 0.95 (clean face) accuracy
RepVGG (Mayats-Alpay, 2022)	Lung disease X-ray diagnosis	95.79% accuracy, high F1
DinoV2 (Mohan et al., 20 Jul 2024)	Dermatology (31 classes)	96.48% accuracy, F1 = 0.9727
InceptNet (Sajedi et al., 2023)	Medical image segmentation	0.9555 DRIVE, 0.9945 Breast Cancer
DOCTOR (Li et al., 2023)	Wearable sensor multi-disease	~0.99 accuracy in domain-incremental learning
VLM-based platform (Al-Hamadani, 16 Sep 2025)	Radiology (multi-modality)	80 px avg. location error, high anomaly detection rate
Multimodal AD (Nagarhalli et al., 5 Aug 2025)	Early AD detection	Multi-modality improves robustness and reliability

These platforms support real-time, individualized, and extensible diagnosis in both clinical (human/animal health) and agricultural contexts, reduce latency (e.g., <1.2s edge computation in AI-Skin), and enable workflow automation. Advanced frameworks demonstrate adaptability (e.g., OpenClinicalAI’s dynamic test routing) and strong generalization (e.g., extensive external benchmarking in AD detection (Nguyen et al., 2022), cross-species lesion detection (Hwang et al., 16 Jul 2024)).

Clinical implications include earlier intervention, reduced unnecessary testing, cost and resource optimization, and global scalability. The inclusion of explainability and clinician collaboration features is essential for trust, regulatory compliance, and informed decision-making. Challenges remain in robustness to data variability, label noise, and clinical workflow integration, but the reported frameworks demonstrate the trajectory toward more transparent, resilient, and personalized disease detection solutions.