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From Agents to Governance: Essential AI Skills for Clinicians in the Large Language Model Era

Large language models are rapidly transitioning from pilot schemes to routine clinical practice. This creates an urgent need for clinicians to develop the necessary skills to strike the right balance between seizing opportunities and taking accountability. We propose a 3-tier competency framework to support clinicians’ evolution from cautious users to responsible stewards of artificial intelligence (AI). Tier 1 (foundational skills) defines the minimum competencies for safe use, including prompt engineering, human–AI agent interaction, security and privacy awareness, and the clinician-patient interface (transparency and consent). Tier 2 (intermediate skills) emphasizes evaluative expertise, including bias detection and mitigation, interpretation of explainability outputs, and the effective clinical integration of AI-generated workflows. Tier 3 (advanced skills) establishes leadership capabilities, mandating competencies in ethical governance (delineating accountability and liability boundaries), regulatory strategy, and model life cycle management—specifically, the ability to govern algorithmic adaptation and change protocols. Integrating this framework into continuing medical education programs and role-specific job descriptions could enhance clinicians’ ability to use AI safely and responsibly. This could standardize deployment and support safer clinical practice, with the potential to improve patient outcomes.

Tumor microenvironment and macroenvironment: A new perspective on holistic oncology

Cancer Lett. 2025 Oct 11;634:218076. doi: 10.1016/j.canlet.2025.218076. Online ahead of print.

ABSTRACT

The tumor microenvironment (TME) and tumor macroenvironment (TMaE) jointly shape cancer biology by linking local cellular niches with systemic host physiology. The TME provides the immediate soil for tumor initiation, progression, and therapy resistance, whereas the TMaE integrates metabolic, immune, neuroendocrine, microbial, and inflammatory signals that remodel local ecosystems. Recent advances highlight how systemic factors, including aging, energy imbalance, chronic inflammation, cachexia, and psychosocial stress, interact with extracellular matrix remodeling, vascular dynamics, and immune surveillance to influence tumor dormancy, metastatic reactivation, and therapeutic outcomes. However, the conceptual boundaries between TME and TMaE remain unclear, mechanistic insights are limited, and current models insufficiently capture local-systemic crosstalk. Future strategies integrating multi-omics, advanced imaging, and humanized models are essential to map this multidimensional interplay. A deeper understanding of TME-TMaE will be critical to refine precision oncology, advance preventive strategies, and design combinatorial therapies targeting both local and systemic cancer ecosystems. This review highlights the roles of the TME and TMaE in tumor initiation, progression, and heterogeneity, their interactions, and the clinical implications for classification, therapy, and prognosis.

PMID:41083101 | DOI:10.1016/j.canlet.2025.218076

Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation

An analytical resource of post-translational modifications from over 1,000 patients across 11 cancer types reveals pan-cancer changes involved in hallmark cancer processes and reveals potential new therapeutic avenues.

Cancer-associated fibroblasts promote the stemness and progression of renal cell carcinoma via exosomal miR-181d-5p

Cell Death Discovery, Published online: 01 November 2022; doi:10.1038/s41420-022-01219-7

Cancer-associated fibroblasts promote the stemness and progression of renal cell carcinoma via exosomal miR-181d-5p
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