AI's pivotal impact on redefining stakeholder roles and their interactions in medical education and health care.

Artificial Intelligence (AI) has the potential to revolutionize medical training, diagnostics, treatment planning, and healthcare delivery while also bringing challenges such as data privacy, the risk of technological overreliance, and the preservation of critical thinking. This manuscript explores the impact of AI and Machine Learning (ML) on healthcare interactions, focusing on faculty, students, clinicians, and patients. AI and ML's early inclusion in the medical curriculum will support student-centered learning; however, all stakeholders will require specialized training to bridge the gap between medical practice and technological innovation.

Circulating cancer-associated macrophage-like cells and macrophage-related cytokines in obese patients with advanced breast cancer who undergo neoadjuvant chemotherapy.

Cancer-associated macrophage-like cells (CAMLs) are rare, gigantic, and atypical circulating cells found exclusively in the peripheral blood of patients with solid cancers. Obesity-induced hypoxia attracts macrophages to the tumor microenvironment, where they contribute to establishing chronic inflammation, leading to cancer progression. We hypothesized that obese patients with advanced breast cancer may have CAML profiles different from those of nonobese patients, and these profiles may correlate with proinflammatory markers or other macrophage-related markers.

Predictive Models for Long Term Survival of AML Patients Treated with Venetoclax and Azacitidine or 7+3 Based on Post Treatment Events and Responses: Retrospective Cohort Study.

Background: The treatment of acute myeloid leukemia (AML) in older or unfit patients typically involves a regimen of venetoclax plus azacitidine (ven/aza). Toxicity and treatment responses are highly variable following treatment initiation and clinical decision-making continually evolves in response to these as treatment progresses. To improve clinical decision support (CDS) following treatment initiation, predictive models based on evolving and dynamic toxicities, disease responses, and other features should be developed.