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The integration of artificial intelligence into medical diagnostics has evolved from controlled research demonstrations to real-world clinical deployment, creating both unprecedented opportunities and substantial challenges [...]

Predictive modeling is a complex methodology that involves leveraging advanced mathematical and computational techniques to forecast future occurrences or outcomes. This tool has numerous applications in medicine, yet its full potential remains untapped within this field. Therefore, it is imperative to delve deeper into the benefits and drawbacks associated with utilizing predictive modeling in medicine for a more comprehensive understanding of how this approach may be effectively leveraged for improved patient care. When implemented successfully, predictive modeling has yielded impressive results across various medical specialities. From predicting disease progression to identifying high-risk patients who require early intervention, there are countless examples of successful implementations of this approach within healthcare settings worldwide. However, despite these successes, significant challenges remain for practitioners when applying predictive models to real-world scenarios. These issues include concerns about data quality and availability as well as navigating regulatory requirements surrounding the use of sensitive patient information—all factors that can impede progress toward realizing the true potential impact of predictive modeling on improving health outcomes.

Class imbalance is a prevalent challenge in machine learning that arises from skewed data distributions in one class over another, causing models to prioritize the majority class and underperform on the minority classes. This bias can significantly undermine accurate predictions in real-world scenarios, highlighting the importance of the robust handling of imbalanced data for dependable results. This study examines one such scenario of real-time monitoring systems for fall risk assessment in bedridden patients where class imbalance may compromise the effectiveness of machine learning. It compares the effectiveness of two resampling techniques, the Synthetic Minority Oversampling Technique (SMOTE) and SMOTE combined with Edited Nearest Neighbors (SMOTEENN), in mitigating class imbalance and improving predictive performance. Using a controlled sampling strategy across various instance levels, the performance of both methods in conjunction with decision tree regression, gradient boosting regression, and Bayesian regression models was evaluated. The results indicate that SMOTEENN consistently outperforms SMOTE in terms of accuracy and mean squared error across all sample sizes and models. SMOTEENN also demonstrates healthier learning curves, suggesting improved generalization capabilities, particularly for a sampling strategy with a given number of instances. Furthermore, cross-validation analysis reveals that SMOTEENN achieves higher mean accuracy and lower standard deviation compared to SMOTE, indicating more stable and reliable performance. These findings suggest that SMOTEENN is a more effective technique for handling class imbalance, potentially contributing to the development of more accurate and generalizable predictive models in various applications.

Using fluid-structure interaction algorithms to simulate the human circulatory system is an innovative approach that can provide valuable insights into cardiovascular dynamics. Fluid-structure interaction algorithms enable us to couple simulations of blood flow and mechanical responses of the blood vessels while taking into account interactions between fluid dynamics and structural behaviors of vessel walls, heart walls, or valves. In the context of the human circulatory system, these algorithms offer a more comprehensive representation by considering the complex interplay between blood flow and the elasticity of blood vessels. Algorithms that simulate fluid flow dynamics and the resulting forces exerted on vessel walls can capture phenomena such as wall deformation, arterial compliance, and the propagation of pressure waves throughout the cardiovascular system. These models enhance the understanding of vasculature properties in human anatomy. The utilization of fluid-structure interaction methods in combination with medical imaging can generate patient-specific models for individual patients to facilitate the process of devising treatment plans. This review evaluates current applications and implications of fluid-structure interaction algorithms with respect to the vasculature, while considering their potential role as a guidance tool for intervention procedures.

Background: High in-domain accuracy in healthcare machine learning (ML) models does not guarantee reliable clinical performance, especially when training and validation protocols are insufficiently robust. This paper presents a standardized framework for training and validating ML models intended for classifying medical conditions, emphasizing the need for clinically relevant evaluation metrics and external validation. Methods: We apply this framework to a case study in knee osteoarthritis grading, demonstrating how overfitting, data leakage, and inadequate validation can lead to deceptively high accuracy that fails to translate into clinical reliability. In addition to conventional metrics, we introduce composite clinical measures that better capture real-world utility. Results: Our findings show that models with strong in-domain performance may underperform on external datasets, and that composite metrics provide a more nuanced assessment of clinical applicability. Conclusions: Standardized training and validation protocols, together with clinically oriented evaluation, are essential for developing ML models that are both statistically robust and clinically reliable across a range of medical classification tasks.

Obesity represents a multifactorial health condition influenced by complex interactions among behavioral, environmental, and physiological factors, yet the relative predictive importance of lifestyle behaviors versus medical history indicators remains incompletely characterized. This investigation employed a three-phase machine learning approach to systematically compare the predictive power of behavioral lifestyle factors, medical history variables, and their integration for obesity classification. Phase A utilized a dedicated obesity dataset containing demographic, dietary, and lifestyle predictors to perform seven-category obesity classification, achieving 81.65% test accuracy with an optimized Random Forest ensemble and macro-averaged F1-score of 0.82. Phase B addressed binary obesity classification using health indicators from diabetes screening data, where a Gradient Boosting model with optimized decision threshold achieved 67.84% accuracy and AUC of 0.735, demonstrating substantially lower performance than behavioral predictors. Phase C integrated both feature sets into a unified model, where Gradient Boosting achieved 68.31% accuracy and AUC of 0.747, representing marginal improvement over medical history alone. Cross-validated performance comparisons revealed that behavioral lifestyle factors provided superior discriminative power compared to medical history indicators, with dedicated lifestyle predictors achieving 13.81 percentage points higher accuracy than medical indicators. Feature importance analysis confirmed that transportation mode, physical activity patterns, and dietary behaviors ranked among the most influential predictors in the combined model. These findings demonstrate that behavioral lifestyle factors constitute stronger obesity predictors than medical history variables, with implications for clinical screening strategies and public health intervention targeting that prioritize lifestyle assessment and modification programs.

Recent studies investigating the diagnostic capabilities of large language models (LLMs) have attracted significant media attention, often resulting in headlines claiming that AI systems can match or even outperform physicians. As LLMs have rapidly proliferated, this has fueled a widespread misconception that they represent the cutting edge of artificial intelligence in all contexts. This narrative tends to overshadow the continued importance of task-specific machine learning models, which were developed and validated for particular diagnostic applications well before the rise of LLMs. This single-case study evaluated the reliability of five leading multimodal LLMs (GPT-5, Gemini 3 Pro, Llama 4 Maverick, Grok 4, and Claude Opus 4.5 Extended) for radiological image interpretation by presenting each model with an identical non-contrast head CT demonstrating intracranial pathology, complemented by a novel cross-evaluation protocol wherein each model graded all responses. The deliberate use of a straightforward case (rather than diagnostically challenging pathology) aimed to establish minimum competency thresholds; if LLMs cannot reliably interpret obvious pathology, their deployment on ambiguous cases becomes indefensible. The study intentionally excluded human radiologist ground truth to avoid generating comparative accuracy metrics that could be selectively cited for commercial purposes, focusing instead on demonstrating class-wide limitations rather than ranking individual products. Results revealed a 20% rate of fundamental diagnostic error, with one model misidentifying ischemic stroke as intracerebral hemorrhage with incorrect lateralization. Even among concordant models, clinically meaningful variability persisted in acuity characterization, anatomical localization, and differential diagnoses. Cross-evaluation exposed ground truth disagreement between models, self-evaluation bias, inconsistent grading stringency, and divergent evaluation philosophies. Only one model included appropriate safety disclaimers. These findings demonstrate that current multimodal LLMs exhibit unacceptable diagnostic variability and evaluative inconsistency for autonomous clinical deployment. The appropriate clinical role for LLMs should be distinguished by deployment context: autonomous diagnosis requires validated task-specific models; decision support applications demand rigorous radiologist oversight protocols; and educational summarization represents the most appropriate current use case, with mandatory disclaimers. Healthcare applications requiring reliable image interpretation should prioritize validated, task-specific machine learning systems over general-purpose language models.

Piriformis syndrome, a neuromuscular disorder caused by sciatic nerve compression by the piriformis muscle, remains understudied in athletic populations despite anecdotal reports of elevated prevalence in hockey players. This study investigated the prevalence of piriformis syndrome symptoms and potential risk factors in actively competing (current) and retired (former) high-level hockey players. A cross-sectional survey was conducted among 67 actively competing and retired professional, collegiate, and junior hockey players (58 males, 9 females; mean age 25.6 ± 4.0 years; mean playing experience 17.8 ± 3.7 years). Active playing status was defined as currently participating in organized competitive hockey at any level, while retired status was defined as having ceased competitive participation for at least one season. The survey instrument was based on a validated clinical assessment scoring system, consisting of 12 questions assessing piriformis syndrome-related symptoms. Participants were classified as “high score” (≥6 affirmative responses) or “low score” (<6 responses). Multiple linear regression analysis was used to evaluate associations between demographic variables (age, playing status, years played, competitive level) and total symptom scores. Overall, 25.4% (n = 17) of participants met criteria for high symptom burden, with sitting-induced buttock pain being the most prevalent specific symptom (40.3%). Mean total score was 4.8 ± 1.8 (range 2–10). Multiple regression analysis revealed no statistically significant associations between piriformis syndrome scores and any demographic variable (overall model: R2 = 0.065, p = 0.374). Retired players showed a non-significant trend toward higher scores compared to actively competing players (β = −1.388, 95% CI: −2.793 to 0.018, p = 0.053). No correlations were observed with age (r = −0.045, p = 0.719), years played (r = −0.054, p = 0.666), or competitive level (p = 0.666). In conclusion, this study revealed substantial piriformis syndrome symptom burden (25.4%) in high-level hockey players without significant demographic associations.

If we do not urgently educate current and future medical professionals to critically evaluate and distinguish credible AI-assisted diagnostic tools from those whose performance is artificially inflated by data leakage or improper validation, we risk undermining clinician trust in all AI diagnostics and jeopardizing future advances in patient care. For instance, machine learning models have shown high accuracy in diagnosing Parkinson’s Disease when trained on clinical features that are themselves diagnostic, such as tremor and rigidity. This study systematically investigates the impact of data leakage and feature selection on the true clinical utility of machine learning models for early Parkinson’s Disease detection. We constructed two experimental pipelines: one excluding all overt motor symptoms to simulate a subclinical scenario and a control including these features. Nine machine learning algorithms were evaluated using a robust three-way data split and comprehensive metric analysis. Results reveal that, without overt features, all models exhibited superficially acceptable F1 scores but failed catastrophically in specificity, misclassifying most healthy controls as Parkinson’s Disease. The inclusion of overt features dramatically improved performance, confirming that high accuracy was due to data leakage rather than genuine predictive power. These findings underscore the necessity of rigorous experimental design, transparent reporting, and critical evaluation of machine learning models in clinically realistic settings. Our work highlights the risks of overestimating model utility due to data leakage and provides guidance for developing robust, clinically meaningful machine learning tools for early disease detection.

Background: Mechanical trauma during pregnancy from motor vehicle accidents, falls, and maternal seizures poses significant risks to fetal development. The fetus is protected by multiple hierarchical layers including the uterine wall, amniotic fluid, and cerebrospinal fluid surrounding the brain. Despite the clinical significance of maternal trauma occurring in approximately six to eight percent of pregnancies, previous computational studies have focused primarily on amniotic fluid protection while treating the fetus as a homogeneous structure, without examining the nested protective architecture comprising both amniotic fluid and cerebrospinal fluid as an integrated system. Methods: This investigation implements a nested fluid–structure interaction framework simultaneously capturing three hierarchically organized systems: the uterine wall interacting with amniotic fluid, amniotic fluid interacting with the fetal body, and the cranial system comprising skull, cerebrospinal fluid, and brain tissue. The computational architecture employs smoothed particle hydrodynamics for fluid domains coupled with finite element methods for solid structures. Boundary conditions representing traumatic forces were obtained through experimental protocols using an instrumented medical simulation mannequin performing seizure movements. Results: Computational simulations predicted that amniotic fluid absorbed the majority of impact forces through hydraulic cushioning, while cerebrospinal fluid provided additional stress reduction through pressure redistribution, with model predictions suggesting total stress reduction exceeding ninety percent. Peak fetal brain stress values predicted by the model were below injury thresholds reported in adult neural tissue literature, though direct applicability of these thresholds to fetal tissue remains uncertain. The fetal brain exhibited minimal movement relative to the skull despite complex force cascades. Stress distributions showed elevated values in the frontal lobe and brainstem, though magnitudes remained within ranges that the model suggests may be tolerable. Conclusions: Computational modeling suggests that the nested fluid protection architecture operates as an integrated hierarchical system providing potential mechanical protection through sequential energy dissipation. These findings represent model predictions requiring experimental and clinical validation before translation to clinical practice.