Faculty of Health, Medicine and Life Sciences, School of Health Professions Education, Maastricht University, Maastricht, the Netherlands.
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.
Med Educ. 2021 Jan;55(1):30-36. doi: 10.1111/medu.14131. Epub 2020 Mar 30.
'COLD' TECHNOLOGIES AND 'WARM' HANDS-ON MEDICINE NEED TO WALK HAND-IN-HAND: Technologies, such as deep learning artificial intelligence (AI), promise benign solutions to thorny, complex problems; but this view is misguided. Though AI has revolutionised aspects of technical medicine, it has brought in its wake practical, conceptual, pedagogical and ethical conundrums. For example, widespread adoption of technologies threatens to shift emphasis from 'hands-on' embodied clinical work to disembodied 'technology enhanced' fuzzy scenarios muddying ethical responsibilities. Where AI can offer a powerful sharpening of diagnostic accuracy and treatment options, 'cold' technologies and 'warm' hands-on medicine need to walk hand-in-hand. This presents a pedagogical challenge grounded in historical precedent: in the wake of Vesalian anatomy introducing the dominant metaphor of 'body as machine,' a medicine of qualities was devalued through the rise of instrumental scientific medicine. The AI age in medicine promises to redouble the machine metaphor, reducing complex patient experiences to linear problem-solving interventions promising 'solutionism.' As an instrumental intervention, AI can objectify patients, frustrating the benefits of dialogue, as patients' complex and often unpredictable fleshly experiences of illness are recalculated in solution-focused computational terms. SUSPICIONS ABOUT SOLUTIONS: The rate of change in numbers and sophistication of new technologies is daunting; they include surgical robotics, implants, computer programming and genetic interventions such as clustered regularly interspaced short palindromic repeats (CRISPR). Contributing to the focus of this issue on 'solutionism,' we explore how AI is often promoted as an all-encompassing answer to complex problems, including the pedagogical, where learning 'hands-on' bedside medicine has proven benefits beyond the technical. Where AI and embodied medicine have differing epistemological, ontological and axiological roots, we must not imagine that they will readily walk hand-in-hand down the aisle towards a happy marriage. Their union will be fractious, requiring lifelong guidance provided by a perceptive medical education suspicious of 'smart' solutions to complex problems.
“冷”技术与“热”实践医学需要携手并进：深度学习人工智能（AI）等技术为棘手、复杂的问题提供了良性的解决方案；但这种观点是有误导性的。尽管 AI 已经彻底改变了医学技术的某些方面，但它也带来了实际的、概念上的、教学法上的和伦理上的难题。例如，技术的广泛采用可能会导致从“实践”的身体临床工作转向非身体的“技术增强”模糊场景，从而模糊了伦理责任。虽然 AI 可以提供强大的诊断准确性和治疗方案的提升，但“冷”技术与“热”实践医学需要携手并进。这带来了一个基于历史先例的教学法挑战：在 Vesalian 解剖学引入“身体是机器”的主导隐喻之后，仪器科学医学的兴起使品质医学的地位降低。医学中的 AI 时代有望加倍强化机器隐喻，将复杂的患者体验简化为线性问题解决干预措施，从而承诺“解决方案主义”。作为一种工具干预，AI 可以使患者客观化，挫败对话的好处，因为患者对疾病的复杂且常常不可预测的肉体体验将以关注解决方案的计算术语重新计算。对解决方案的怀疑：新技术的数量和复杂性的变化速度令人望而生畏；它们包括手术机器人、植入物、计算机编程和基因干预，如成簇规则间隔短回文重复（CRISPR）。为了关注本期特刊的“解决方案主义”，我们探讨了 AI 是如何经常被推广为解决复杂问题的全面答案的，包括教学法，在教学法中，床边实践医学的学习已经证明了超越技术的好处。当 AI 和体现医学具有不同的认识论、本体论和价值论根源时，我们绝不能想象它们会轻易地携手走向幸福的婚姻。它们的结合将是不稳定的，需要由具有洞察力的医学教育提供终身指导，对复杂问题的“智能”解决方案持怀疑态度。
