Evidence-Based Analysis of AI Chatbots in Oncology Patient Education: Implications for Trust, Perceived Realness, and Misinformation Management

Journal of Cancer Education, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 18, 2025

Abstract The rapid integration of AI-driven chatbots into oncology education represents both a transformative opportunity and critical challenge. These systems, powered by advanced language models, can deliver personalized, real-time cancer information to patients, caregivers, clinicians, bridging gaps in access availability. However, their ability convincingly mimic human-like conversation raises pressing concerns regarding misinformation, trust, overall effectiveness digital health communication. This review examines the dual-edged role AI chatbots, exploring capacity support patient alleviate clinical burdens, while highlighting risks lack or inadequate algorithmic opacity (i.e., inability see data reasoning used make decision, which hinders appropriate future action), false information, ethical dilemmas posed human-seeming entities. Strategies mitigate these include robust oversight, transparent development, alignment with evidence-based protocols. Ultimately, responsible deployment requires commitment safeguarding core values practice, human-centered care.

Language: Английский

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Intuitive Human–Artificial Intelligence Theranostic Complementarity

Cancer Biotherapy and Radiopharmaceuticals, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 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.

Language: Английский

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All Your Base Are Belong to Us: The Urgent Reality of Unproctored Testing in the Age of LLMs

International Journal of Selection and Assessment, Journal Year: 2025, Volume and Issue: 33(2)

Published: March 4, 2025

ABSTRACT The release of new generative artificial intelligence (AI) tools, including large language models (LLMs), continues at a rapid pace. Upon the OpenAI's o1 models, I reconducted Hickman et al.'s (2024) analyses examining how well LLMs perform on quantitative ability (number series) test. GPT‐4 scored below 20th percentile (compared to thousands human test takers), but 95th percentile. In response these updated findings and Lievens Dunlop's (2025) article about effects validity pre‐employment assessments, make an urgent call action for selection assessment researchers practitioners. A recent survey suggests that proportion applicants are already using AI tools complete high‐stakes it seems no current assessments will be safe long. Thus, offer possibilities future testing, detail their benefits drawbacks, provide recommendations. These are: increased use proctoring, adding strict time limits, LLM detection software, think‐aloud (or similar) protocols, collecting analyzing trace data, emphasizing samples over signs, redesigning allow during completion. Several inspire research modernize assessment. Future should seek improve our understanding design valid use, effectively test‐taker whether protocols can help differentiate experts novices.

Language: Английский

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The role of large language models in medical genetics

Molecular Genetics and Metabolism, Journal Year: 2025, Volume and Issue: unknown, P. 109098 - 109098

Published: March 1, 2025

Language: Английский

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Large Language Models Take on the AAMC Situational Judgment Test: Evaluating Dilemma-Based Scenarios

International Journal of Artificial Intelligence in Education, Journal Year: 2025, Volume and Issue: unknown

Published: April 4, 2025

Language: Английский

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The Dark Addiction Patterns of Current AI Chatbot Interfaces

Published: April 23, 2025

Language: Английский

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