Artificial intelligence in urology training: practical benefits and real‐world limits

Abstract

Artificial intelligence (AI) is rapidly entering the everyday workflow of clinicians. For urology trainees, large language models and other AI-assisted tools offer instant access to guideline-based information, support with academic writing and presentations, and even feedback during simulated surgical training. Yet, alongside the enthusiasm, there remain clear limitations that must be recognised if AI is to genuinely support, rather than mislead, the next generation of urologists. Many trainees already use AI chatbots to revise before ward rounds or theatre lists, asking for summaries of bladder cancer follow-up, management pathways for obstructing stones, or side-effects of intravesical BCG. These tools can feel like a digital study partner. However, evidence suggests that accuracy remains inconsistent in urology-specific knowledge [1]. In an evaluation using 100 questions from the 2022 European Board of Urology In-Service Assessment, even the most advanced language model tested achieved only around 63–77% accuracy, with substantial variation across sub-topics [1]. The authors concluded that large language models may be helpful as a rapid reference for trainees, but error rates are too high for them to replace conventional learning or well-validated educational resources. This reinforces what most trainees recognise intuitively: AI can assist with revision, but responsibility for confirming clinical facts remains firmly with the user. Early attempts to optimise this performance for clinical urology are already emerging. A recent study comparing a general language model with a version custom-trained on the 2023 European Association of Urology (EAU) guidelines showed that the fine-tuned model provided shorter and more specific answers aligned with recognised recommendations, e.g., in questions involving recurrent UTI prevention and prostate biopsy prophylaxis [2]. The authors suggested that such customised systems may eventually support real-time decision-making in clinical urology. For trainees, this points to a future in which AI tools may not only summarise information but do so in a manner that reflects the guidelines they are examined on and expected to apply in daily practice. However, even these models still require verification and clinical judgement, and cannot replace reading the source guideline or seeking senior input. Where AI may have even greater promise is in surgical skills development. Duty-hour restrictions and uneven operative exposure can make it difficult to achieve early confidence with endoscopic or robotic techniques. Intelligent tutoring systems integrated into virtual surgical simulators have already shown that they can accelerate learning curves in operative tasks, with trainees in one study learning a procedure more than twice as fast and with substantially improved technical performance when coached by an AI tutor rather than a human instructor [3]. Although similar evidence in urology is still emerging, it is not difficult to imagine such systems supporting a resident learning the basics of ureteroscopic navigation or intracorporeal suturing. Artificial intelligence is increasingly present in academic life too. Literature synthesis, manuscript structure, cover letter drafting and even patient information materials can be generated far more efficiently with AI-assisted writing support. Recent work within urology has shown that while AI systems are capable of producing well-structured academic text, they may also generate fabricated or inaccurate references if the user is not vigilant [4]. For trainees, this is not a small concern but an academic integrity risk. Used properly, for refining language, generating alternative phrasing or improving clarity, AI can enhance productivity. Used uncritically, it may undermine trust in scientific work and damage a developing academic reputation. Outside research, AI may also help with daily communication demands. Many trainees already use it to translate complex cancer discussions into discharge letters or patient-friendly summaries. Such use can enhance shared decision-making and free time for clinical care, but only if every output is checked carefully. No AI system is yet ready to communicate independently with patients. Although AI-driven chatbots are being explored in patient-facing urology care, their role for trainees should remain supportive and supervised [5]. Attitudes among surgical trainees are understandably mixed. In a recent survey, residents saw clear value in AI for repetitive tasks, documentation assistance, and knowledge support [6]. However, more than three-quarters expressed concern about AI making life-impacting decisions or replacing human clinical judgement. Their caution resonates within urology, where patients value not only correct decisions but also the empathy and accountability behind them. AI can process information, but it cannot build trust with a family facing a new cancer diagnosis. For urology training, the question is no longer whether AI will be used, but how and where it should be used responsibly. AI tools should be formally integrated into training pathways as supportive technologies, particularly for guideline-oriented knowledge revision, academic writing support, and simulation-based surgical education. Their use may be appropriate within competency-based progression frameworks, including examination preparation, reflective learning, portfolio development, and supervised skills training. However, AI should not be used as a substitute for clinical judgement, operative experience, or certification assessment, nor should it generate unsupervised clinical recommendations or patient-facing communication. Clear institutional guidance is therefore required to define acceptable use, reinforce verification of outputs, and ensure that AI augments, rather than erodes, competency-based urology training. Artificial intelligence will not create competent urologists on its own, nor will it replace experiential learning, mentorship, or accountability at the bedside and in the operating theatre. Its value lies instead in enhancing efficiency, access to information, and opportunities for deliberate practice, particularly in an era of increasing clinical and academic demands on trainees. When used critically and transparently, AI may help trainees learn faster, write better, and prepare more effectively, while preserving the primacy of human judgement and responsibility. The future of urology training should therefore not be AI led, but AI aware, AI guided, and firmly human centred, with trainees trained not only to use these tools, but to understand their limits. Authors have no conflicts of interest to declare. This research received no external funding.

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