Intuitive Human–Artificial Intelligence Theranostic Complementarity

Cancer Biotherapy and Radiopharmaceuticals, Год журнала: 2025, Номер unknown

Опубликована: Фев. 20, 2025

Deep learning artificial intelligence (AI) algorithms are poised to subsume diagnostic imaging specialists in radiology and nuclear medicine, where radiomics can consistently outperform human analysis reporting capability, do it faster, with greater accuracy reliability. However, claims made for generative AI respect of decision-making the clinical practice theranostic medicine highly contentious. Statistical computer cannot emulate emotion, reason, instinct, intuition, or empathy. simulates without possessing it. has no understanding meaning its outputs. The unique statistical probability attributes large language models must be complemented by innate intuitive capabilities physicians who accept responsibility accountability direct care each individual patient referred management specified cancers. Complementarity envisions synergistic engagement radiomics, genomics, radiobiology, dosimetry, data collation from multidimensional sources, including electronic medical record, enable physician spend informed face time their patient. Together discernment, application technical insights will facilitate optimal formulation a personalized precision strategy empathic, efficacious, targeted treatment cancer accordance wishes.

Язык: Английский

Precision Forecasting in Colorectal Oncology: Predicting Six-Month Survival to Optimize Clinical Decisions

Electronics, Год журнала: 2025, Номер 14(5), С. 880 - 880

Опубликована: Фев. 23, 2025

Colorectal cancer (CRC) has a relatively high five-year survival rate compared to other cancers; however, this drops significantly in patients with malignant CRC. One critical factor palliative care decision-making is the ability accurately predict patient survival, six-month period commonly used as threshold. In study, we evaluated performance of five machine learning models—logistic regression, decision tree, random forest, multilayer perceptron, and extreme gradient boosting (XGBoost)—in predicting for CRC using publicly available synthetic dataset containing 11,774 samples 51 features. The models were trained validated five-fold cross-validation, minority oversampling technique (SMOTE) was applied address class imbalance. Among models, XGBoost demonstrated highest performance, achieving 95% accuracy, precision, recall, F1-score, along 90% specificity. Feature importance analysis identified smoking status surgical history key factors influencing model predictions. These findings highlight potential tree-based supporting timely informed decisions, while also providing insights into handling data imbalance optimizing parameters prediction tasks.

Язык: Английский

Generating Synthetic T2*-Weighted Gradient Echo Images of the Knee with an Open-source Deep Learning Model

Academic Radiology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Язык: Английский