Schlemmer Heinz-Peter
Department of Radiology, German Cancer Research Center, Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
Jpn J Radiol. 2025 Jul 31. doi: 10.1007/s11604-025-01810-9.
The rapid acceleration of digital transformation and artificial intelligence (AI) is fundamentally reshaping medicine. Much like previous technological revolutions, AI-driven by advances in computer technology and software including machine learning, computer vision, and generative models-is redefining cognitive work in healthcare. Radiology, as one of the first fully digitized medical specialties, is at the forefront of this transformation. AI is automating workflows, enhancing image acquisition and interpretation, and improving diagnostic precision, which collectively boost efficiency, reduce costs, and elevate patient care. Global data networks and AI-powered platforms are enabling borderless collaboration, empowering radiologists to focus on complex decision-making and patient interaction. Despite these profound opportunities, widespread AI adoption in radiology remains limited, often confined to specific use cases, such as chest, neuro, and musculoskeletal imaging. Concerns persist regarding transparency, explainability, and the ethical use of AI systems, while unresolved questions about workload, liability, and reimbursement present additional hurdles. Psychological and cultural barriers, including fears of job displacement and diminished professional autonomy, also slow acceptance. However, history shows that disruptive innovations often encounter initial resistance. Just as the discovery of X-rays over a century ago ushered in a new era, today, digitalization and artificial intelligence will drive another paradigm shift-this time through cognitive automation. To realize AI's full potential, radiologists must maintain clinical oversight and safeguard their professional identity, viewing AI as a supportive tool rather than a threat. Embracing AI will allow radiologists to elevate their profession, enhance interdisciplinary collaboration, and help shape the future of medicine. Achieving this vision requires not only technological readiness but also early integration of AI education into medical training. Ultimately, radiology will not be replaced by AI, but by radiologists who effectively harness its capabilities.
数字转型和人工智能（AI）的快速加速正在从根本上重塑医学。与以往的技术革命非常相似，由包括机器学习、计算机视觉和生成模型在内的计算机技术和软件进步驱动的人工智能正在重新定义医疗保健中的认知工作。放射学作为最早完全数字化的医学专业之一，处于这一转型的前沿。人工智能正在使工作流程自动化，增强图像采集和解读，并提高诊断精度，这些共同提高了效率、降低了成本并提升了患者护理水平。全球数据网络和人工智能驱动的平台正在实现无边界协作，使放射科医生能够专注于复杂的决策制定和患者互动。尽管有这些巨大的机遇，但人工智能在放射学中的广泛应用仍然有限，通常局限于特定的用例，如胸部、神经和肌肉骨骼成像。对于人工智能系统的透明度、可解释性和道德使用的担忧依然存在，而关于工作量、责任和报销的未解决问题又带来了额外的障碍。心理和文化障碍，包括对工作岗位被取代和职业自主权降低的担忧，也减缓了接受速度。然而，历史表明，颠覆性创新往往会遇到最初的阻力。就像一个多世纪前X射线的发现开创了一个新时代一样，如今，数字化和人工智能将推动另一场范式转变——这一次是通过认知自动化。为了实现人工智能的全部潜力，放射科医生必须保持临床监督并维护他们的专业身份，将人工智能视为一种支持工具而非威胁。拥抱人工智能将使放射科医生提升他们的职业，加强跨学科合作，并帮助塑造医学的未来。实现这一愿景不仅需要技术准备，还需要将人工智能教育尽早纳入医学培训。最终，放射学不会被人工智能取代，而是会被有效利用其能力的放射科医生所取代。
