Dermoscopic skin lesion classification is a task of major clinical importance but is computationally expensive, making it inaccessible in resource-constrained healthcare settings. In this paper, we introduce a computationally efficient skin lesion classification framework for seven classes using EfficientNet-B0, complemented by Monte Carlo (MC) Dropout for uncertainty quantification. Our approach was trained and tested on the HAM10000 dataset containing 10,015 dermoscopic images across seven classes. To address the severe 67:1 class imbalance, we employ WeightedRandomSamplerand class-weighted cross-entropy loss as complementary corrections acting at the batch-composition level and the gradient-magnitude level respectively. By performing $T=50$ stochastic forward passes during inference, we decompose predictive uncertainty into aleatoric and epistemic components and apply an entropy-based referral threshold that flags uncertain predictions for specialist review. To validate spatial interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) is applied and quantitatively evaluated via Intersection over Union (IoU) against ISIC segmentation masks, yielding a mean IoU of 0.61 across all accepted predictions. Our experiments achieve a test macro AUROC of 0.9404and macro F1-score of 0.7308, with six of seven classes exceeding 70% per-class accuracy (melanocytic nevi: 69.8%). Referring the 30% most uncertain predictions to a clinician raises accepted-subset AUROC from 0.9404 to 0.9568 ($+1.64\%$). The framework is competitive with ResNet-50 and DenseNet-121 at one-fifth the parameter count, and the only lightweight method in the comparison providing calibrated uncertainty estimates. Inference latency benchmarks on an NVIDIA Jetson Nano (edge CPU mode) are reported to contextualize deployment feasibility.

Artificial intelligence can help people interpret complex data, anticipate change, and choose among competing actions. Its value nevertheless depends on the quality of the information entering a model, the meaning of its output, the authority to act on that output, and the ability to assess consequences. This conceptual narrative review synthesizes 26 publications identified in a supplied collection of works. The works address healthcare, public health, business analytics, sustainability, management information systems, software quality, and cybersecurity. Their diversity makes them useful for examining a common decision process, although they cannot be treated as a comparable set of experimental results. The paper develops a five-layer framework linking governed data, task-appropriate analytics, interpretable evidence, accountable action, and outcome monitoring. It then examines how the framework applies to surveillance and care, organizational performance and sustainability, and protection of digital infrastructure. Across these settings, model accuracy is necessary for many tasks but insufficient to establish practical benefit. The proposed research agenda calls for prospective evaluations that define the decision being supported, document existing practice, test performance across groups and settings, measure the consequences of intervention, and specify when a system should be revised or withdrawn. This framework provides a way to design and examine AI as part of a human and institutional process rather than as an isolated predictive tool.

Objective: Clinical guidelines for Premenstrual Dysphoric Disorder (PMDD) prioritize Selective Serotonin Reuptake Inhibitors (SSRIs) as first-line treatment, yet real-world adherence is frequently compromised by adverse effects that are underreported in randomized controlled trials. This discrepancy suggests a “Tolerability Tax”: a cumulative burden of side effects that degrades patient quality of life despite symptom efficacy. Methods: Here, we utilize natural language processing to quantify this discordance by analyzing 1,491 unique patient narratives from a large online biobank. Data was stratified into pharmacologic and holistic classes to evaluate sentiment, and a mechanistic density analysis alongside sensitivity analyses excluding “hype” keywords were performed. Results: We identified a significant treatment-satisfaction hierarchy where patients reported higher subjective satisfaction with nutritional interventions compared to standard clinical care (d=0.27, P<0.001). The mechanistic density analysis revealed that 51.7% of SSRI narratives contained side-effect complaints compared to 38.4% of supplement narratives, suggesting a relationship where lower satisfaction is associated with tolerability burden rather than a lack of efficacy. Sensitivity analyses excluding “hype” keywords validated the robustness of this signal. Conclusions: These findings demonstrate that digital phenotyping can capture the “Tolerability Tax” missed by traditional instruments, suggesting that long-term adherence in PMDD requires a clinical reprioritization of side-effect mitigation alongside raw potency.