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November 28, 2025, 10:52

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Commentary on "Large Language Models and Large Concept Models in Radiology: Present Challenges, Future Directions, and Critical Perspectives" The transition from LLMs to LCMs, aiming for enhanced semantic reasoning, is fundamentally challenged by the necessity of building these sophisticated models upon historical data streams polluted by human cognitive biases [1]. Diagnostic interpretation errors are often not perceptual misses but interpretive errors driven by faulty reasoning [2,3]. These biases include Anchoring Bias, where a radiologist becomes fixated on an initial impression despite contradictory evidence, often coupled with Confirmation Bias, the inclination to seek information only to affirm that initial theory [2,4,5]. Similarly, Availability Bias, or availability heuristics, predisposes the interpreter to recall recently seen or memorable diagnoses regardless of the actual prevalence [3,4,6]. When AI learns its "concepts" or "relationships" from millions of reports generated under the influence of these specific biases, it may normalize or amplify flawed reasoning patterns, potentially leading to widespread, systemic diagnostic vulnerabilities that mirror rather than correct human limitations [3]. For instance, an AI trained primarily on reports that exhibit Zebra Retreat—the avoidance of accurate but rare diagnoses due to lack of confidence—will systematically underreport uncommon but critical findings, reducing the diagnostic sensitivity for edge cases [2,6]. The core strength of future AI systems must therefore lie not just in conceptual depth but in active debiasing, mitigating the human errors that underpin the training corpus [4,5]. If AI recommendations are opaque, clinicians may fall prey to Blind Obedience or Premature Closure by accepting the machine's initial diagnosis without critical Type 2 analysis [2,6]. To counter this, AI must incorporate the same cognitive forcing strategies used by human interpreters, demanding metacognition ("thinking about thinking") to identify susceptibility to bias [3,4]. Furthermore, AI must specifically address the Hindsight Bias that plagues retrospective quality review [2,6], by ensuring its decision pathways are fully auditable and transparent, allowing for objective assessment of whether an error resulted from inherent data contamination or algorithmic failure. As AI integrates deeper into clinical workflows, its ability to enhance safety hinges on proactively resisting the transfer and propagation of predictable human cognitive limitations [6]. References 1. Merchant SA, Merchant N, Varghese SL, Shaikh MJS. Large language models and large concept models in radiology: Present challenges, future directions, and critical perspectives. World J Radiol. 2025;17(11):114754. [DOI: 10.4329/wjr.v17.i11.114754] 2. Onder O, Yarasir Y, Azizova A, Durhan G, Onur MR, Ariyurek OM. Errors, discrepancies and underlying bias in radiology with case examples: a pictorial review. Insights Imaging. 2021;12:51. [PMID: 33877458. DOI: 10.1186/s13244-021-00986-8] 3. Chen J, Gandomkar Z, Reed WM. Investigating the impact of cognitive biases in radiologists' image interpretation: A scoping review. Eur J Radiol. 2023;166:111013. [PMID: 37541180. DOI: 10.1016/j.ejrad.2023.111013] 4. Busby LP, Courtier JL, Glastonbury CM. Bias in Radiology: The How and Why of Misses and Misinterpretations. Radiographics. 2018;38:236–247. [PMID: 29194009. DOI: 10.1148/rg.2018170107] 5. Gunderman RB. Biases in radiologic reasoning. AJR Am J Roentgenol. 2009;192:561–564. [PMID: 19234247. DOI: 10.2214/AJR.08.1220] 6. Yoon SY, Lee KS, Bezuidenhout AF, Kruskal JB. Spectrum of Cognitive Biases in Diagnostic Radiology. Radiographics. 2024;44:e230059. [PMID: 38843094. DOI: 10.1148/rg.230059]

Replied on November 29, 2025, 06:35

Thank you very much for your thoughtful and detailed commentary on our article. Your points about the way human cognitive biases permeate historical radiology reporting and may consequently shape concept learning in future AI systems are both insightful and highly pertinent to the core concerns of the paper. Your emphasis on anchoring, confirmation, availability bias, zebra retreat, premature closure, blind obedience, and hindsight bias as dominant drivers of interpretive error is firmly grounded in the contemporary radiology literature, and you are correct that any LCM trained naïvely on such data risks inheriting and amplifying these limitations rather than correcting them. This aligns closely with our argument that the real strength of next generation AI should lie not only in richer conceptual representations but also in active mechanisms for debiasing, transparency, and metacognitive support. In light of your comments, we fully agree that cognitive bias aware design needs to be made more explicit in the future directions and safety sections of the article, including discussion of bias focused validation, auditable decision pathways, and incorporation of cognitive forcing strategies into LCM based tools. We are also grateful for the high quality references you have provided, which will help us strengthen the discussion with up to date, evidence based perspectives on cognitive bias in radiology. Once again, sincere thanks for taking the time to provide such nuanced, well referenced feedback on this vital topic, and for sharing a carefully curated set of references that will enrich subsequent iterations of this work. Your insights are greatly appreciated and will help refine the way LCMs are framed and developed as safe, bias aware systems for radiology practice.