e.g. mhealth
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Skip search results from other journals and go to results- 14 Journal of Medical Internet Research
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Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach
In conclusion, our study established a predictive framework using EHR data to assess the association between risk factors and cancer outcomes using explainable ML models across major cancer types. We reported critical nontraditional chronic condition risk factors in addition to common demographic risk factors and outlined distinct patterns for each of the 4 cancer types studied. Additionally, we explored the similarities and differences in risk factor patterns across these cancers.
JMIR Cancer 2025;11:e62833
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While AI plays a crucial role, particularly through the use of LLMs and machine learning (ML), it is used selectively within the broader software framework to enhance specific tasks.
LLMs are used in generating related search terms, expanding upon human-generated queries to enhance the comprehensiveness of literature searches. Any LLM can be adapted to TU software, up to date we have used Chat GPT 4 [18].
JMIR Res Protoc 2025;14:e67248
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Large Language Models in Biochemistry Education: Comparative Evaluation of Performance
ml
JMIR Med Educ 2025;11:e67244
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Applications of AI in Predicting Drug Responses for Type 2 Diabetes
AI includes a range of methods, among which ML and deep learning (DL) stand out as 2 prominent subsets [8]. ML is involved in building systems that are capable of learning from data, identifying patterns, and making decisions. On the other hand, DL, is a special form of ML inspired by the structure and function of the brain, especially neural networks. These models learn from data autonomously and are adaptable to various features.
JMIR Diabetes 2025;10:e66831
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A prior study by Beverin et al [7] examined the prediction of total lung capacity from spirometry using three tree-based machine learning (ML) models, achieving a mean squared error of 560.1 m L. They further developed models to classify restrictive ventilatory impairment, achieving a sensitivity and specificity of 83% and 92%, respectively. However, they did not explore prediction of the complete lung volume assessments.
JMIR AI 2025;4:e65456
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