人工智慧正逐步取代各类劳动者，但放射科医师的处境相对稳固。全球放射科医师面临短缺，他们每日需判读数千张医学影像，其判断对早期癌症侦测至关重要。Mayo Clinic的一项研究显示，其AI模型能在患者确诊前平均约475天便侦测出胰脏癌。然而AI同样可能误判：柏林Charité大学医院放射科主任Jens Vogel-Claussen举出两例——AI曾将结核疤痕误判为潜在癌性结节，也曾遗漏一处后经证实为肺癌的可疑病灶。他指出，软体与放射科医师皆非完美，但两者结合后的表现优于医师独立判读。

专家认为AI革新医疗的潜力巨大，目前已在肿瘤学与病理学等专科中部署应用，但其承诺在短期内仍难以完全兑现。关键问题之一在于AI缺乏自我怀疑机制——Coalition for Health AI执行长Brian Anderson警告，模型可能表现得极度自信却仍然不准确，而在医疗领域这绝非抽象风险，而是攸关患者的真实诊断。此外，AI还可能强化医师自身的确认偏误，且不同训练方式导致各模型解读品质参差不齐。

责任归属亦是重大隐忧。根据世界卫生组织的数据，在已部署AI诊断的国家中，仅有8%制定了明确的责任标准，界定AI系统失误时的究责对象。Bernstein分析师Susannah Ludwig认为，AI的角色是增强而非取代人力资本。她强调，由于最终涉及的是患者的生命，AI必须正常运作，对错误的容忍度因此远低于其他领域。

Artificial intelligence is increasingly displacing various workers, yet radiologists remain comparatively secure. Facing a global shortage, radiologists read thousands of medical images daily, and their judgment is critical for early cancer detection. A Mayo Clinic study demonstrated that its AI model can detect pancreatic cancer an average of approximately 475 days before patient diagnosis. However, AI can also err: Jens Vogel-Claussen, director of radiology at Charité University Hospital in Berlin, cited two cases—AI once misidentified a tuberculosis scar as a potentially cancerous nodule and in another instance overlooked a suspicious feature later confirmed as lung cancer. He noted that neither the software nor the radiologist is perfect, but together they outperform the radiologist alone.

Experts say AI's potential to revolutionize healthcare is enormous, with deployment already spanning oncology and pathology, yet its promise will remain largely unfulfilled in the near term. A critical issue is AI's lack of self-doubt—Brian Anderson, CEO of the Coalition for Health AI, warned that a model can appear completely confident while still being inaccurate, and in healthcare this is not an abstract risk but someone's actual diagnosis. Furthermore, AI can reinforce physicians' own confirmation bias, and varying training methodologies produce inconsistent interpretation quality across models.

Liability remains a major concern. According to the World Health Organization, only 8% of countries deploying AI in diagnostics have established liability standards defining accountability when an AI system fails. Bernstein analyst Susannah Ludwig argues that AI augments rather than replaces human capital. She emphasizes that because patients' lives are ultimately at stake, AI must function properly, making the tolerance for errors far lower than in other domains.