The TRIPOD-LLM Statement: A Targeted Guideline For Reporting Large Language Models Use.

Large Language Models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present TRIPOD-LLM, an extension of the TRIPOD+AI statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion. The guidelines introduce a modular format accommodating various LLM research designs and tasks, with 14 main items and 32 subitems applicable across all categories. Developed through an expedited Delphi process and expert consensus, TRIPOD-LLM emphasizes transparency, human oversight, and task-specific performance reporting. We also introduce an interactive website ( https://tripod-llm.vercel.app/ ) facilitating easy guideline completion and PDF generation for submission. As a living document, TRIPOD-LLM will evolve with the field, aiming to enhance the quality, reproducibility, and clinical applicability of LLM research in healthcare through comprehensive reporting. COI: DSB: Editorial, unrelated to this work: Associate Editor of Radiation Oncology, HemOnc.org (no financial compensation); Research funding, unrelated to this work: American Association for Cancer Research; Advisory and consulting, unrelated to this work: MercurialAI. DDF: Editorial, unrelated to this work: Associate Editor of JAMIA, Editorial Board of Scientific Data, Nature; Funding, unrelated to this work: the intramural research program at the U.S. National Library of Medicine, National Institutes of Health. JWG: Editorial, unrelated to this work: Editorial Board of Radiology: Artificial Intelligence, British Journal of Radiology AI journal and NEJM AI. All other authors declare no conflicts of interest.

Improving colorectal cancer screening - consumer-centred technological interventions to enhance engagement and participation amongst diverse cohorts.

The current "Gold Standard" colorectal cancer (CRC) screening approach of faecal occult blood test (FOBT) with follow-up colonoscopy has been shown to significantly improve morbidity and mortality, by enabling the early detection of disease. However, its efficacy is predicated on high levels of population participation in screening. Several international studies have shown continued low rates of screening participation, especially amongst highly vulnerable lower socio-economic cohorts, with minimal improvement using current recruitment strategies. Research suggests that a complex of dynamic factors (patient, clinician, and the broader health system) contribute to low citizen engagement. This paper argues that the challenges of screening participation can be better addressed by (1) developing dynamic multifaceted technological interventions collaboratively across stakeholders using human-centered design; (2) integrating consumer-centred artificial intelligence (AI) technologies to maximise ease of use for CRC screening; and (3) tailored strategies that maximise population screening engagement, especially amongst the most vulnerable.

The Inverse Data Law: Market Imperatives, Data, and Quality in AI Supported Care.

Over the last decade, the explosion of "Big Data" and its fusion with AI has led many to believe that the development and integration of AI systems in healthcare will usher in a transformative revolution that democratises access to high quality healthcare and collectively improve patient outcomes. However, the nature of market forces in the evolving data economy, has started to show evidence that the opposite is more likely to be true. This paper argues that there is a poorly understood "Inverse Data Law" that will exacerbate the widening health divide between affluent and marginalised communities because: (1) data used to train AI systems favour individuals that are already engaged with healthcare, who have the lowest burden of disease, but the highest purchasing power; and (2) data used to drive market decisions around investment in AI health technology favours tools that increase the commodification of healthcare through over-testing, over-diagnosis, and the acute and episodic management of disease, over tools that support the patient to prevent disease. This dangerous combination is more likely to cripple efforts towards preventative medicine, as data collection and utilisation tends to be inversely proportional to the needs of the patients served - the inverse data law. The paper concludes by introducing important methodological considerations in the design and evaluation of AI systems to promote systems improvement for marginalised users.

AI Diagnostic Technologies and the Gap in Colorectal Cancer Screening Participation.

AI augmented clinical diagnostic tools are the latest research focus in colorectal cancer (CRC) detection. While the opportunity presented by AI-enhanced CRC diagnosis is sound, this paper highlights how its effectiveness with respect to reducing CRC-related mortality and enhancing patient outcomes may be limited by the fact that patient participation remains extremely low globally. This paper builds a foundation to consider how human factors tend to contribute to low participation rates and suggests that a more nuanced socio-technical approach to the development, implementation and evaluation of AI systems that is sensitive to the psycho-social and cultural dimension of CRC may lead to tools that increase screening uptake.