Pancreatic ductal adenocarcinoma (PDAC) presents as a cancer with an especially poor prognosis, largely due to the challenges surrounding its early diagnosis. Liquid biopsy has emerged as a promising, noninvasive method for screening across a variety of cancers. This approach is limited, however, by the extensive heterogeneity of biological samples, a challenge that teams are looking to address using artificial intelligence (AI) and machine learning (ML) strategies. By harnessing the ability of ML algorithms to extract the most salient features from complex datasets, researchers can identify biomarkers with high predictive value for PDAC. This review explores the current landscape of AI-powered liquid biopsy for early PDAC diagnosis, focusing on specific techniques and their respective degrees of success. Following PRISMA-ScR guidelines, 85 studies were extracted from PubMed and Scopus with a final 18 studies included. The majority of papers utilized blood ( n = 15) as the source of liquid biopsy, with the remainder analyzing urine, bile, or cyst fluid. Random forests ( n = 9) and support vector machines ( n = 7) were the most frequently implemented ML models, while two papers focused on deep learning methods. Limitations include the lack of standardized reporting for model performance metrics and small cohort sizes with non-granular labels.

Background: Freezing of gait (FoG) is a common symptom of Parkinson’s disease, especially in its later stages of progression. Characterized by involuntary stopping during normal gait patterns, FoG greatly increases fall risk, reducing quality of life. Given the complex presentation and etiology of FoG, current treatments have proven ineffective in managing episodes. In recent years, machine learning algorithms have been leveraged to derive actionable clinical insights from biomedical datasets. As a manifestation of neuromechanical dysfunction, impending FoG episodes may be characterized through data collected by wearable devices and sensors. Objective: This scoping review evaluates the current landscape of machine and deep learning-derived biomarkers to enhance the personalized management of FoG. Methods: This scoping review was conducted using established methodological frameworks for scoping reviews and is reported in accordance using the PRISMA-ScR checklist. Three databases were queried, with screening yielding 60 studies. Results: Thirty-nine papers reported on deep learning techniques, with the most common architectures being convolutional neural networks and long short-term memory models. Conclusions: Inertial measurement units, which can be worn on various locations, may be a promising modality for practical implementation. To generate closed-loop FoG therapies, algorithms can be integrated into real-time systems like robotic exoskeletons or adaptive deep brain stimulation. Future work in generating datasets from ambulatory devices, as well as distributed computing strategies, may lead to real-time FoG management.

The Social Determinants of Health significantly impact patient care, and understanding their role in the disease process and patient management is essential to high-quality healthcare. Several barriers exist to integrating social determinants of health into medical curricula, including the lack of consensus on evidence-based teaching methods. In this context, our study investigated the impact of integrating social determinants of health into pre-clerkship problem-based learning courses using concept mapping on the clinical problem-solving process. Fourteen pre-clerkship medical students voluntarily participated and were randomly assigned to control and experimental groups. The experimental group education intervention included two problem-based cases with social determinants of health-informed probes to promote linking those determinants to the disease process and develop a treatment plan using concept mapping. The control group participated in the same cases without the education intervention. Students in both groups completed post-session assignments that included two new cases, individual concept maps, and recorded reflections. Concept maps were scored using a scoring rubric. The scores of both groups were compared using the Mann-Whitney U test. Recorded reflections and concept maps were analyzed using reflexive thematic analysis coding method. Quantitative data analysis showed that the experimental group received significantly higher scores than the control group. Qualitative data analysis highlighted substantial differences in clinical problem-solving approaches. The control group’s clinical reasoning approach focused mainly on the clinical aspects of the case. The experimental group followed a social determinants of health-informed clinical reasoning approach to patient-centered treatment plans with balanced therapeutic/nontherapeutic elements.