为医疗保健专业人员解密人工智能:作为转型的代理人,持续的专业发展导致人工智能增强的实践
Eleftherios K Soleas1, Douglas Dittmer, Ashley Waddington
1Dr. Soleas: Director of Lifelong Learning and Innovation, Queen's Health Sciences, Kingston, Ontario, Canada. Dr. Dittmer: Physical Medicine and Rehabilitation, Grand River Hospital, Kitchener, Ontario, Canada. Dr. van Wylick: Vice-Dean, Health Sciences Education, Queen's Health Sciences, and Associate Professor, Pediatrics, Queen's Health Sciences, Kingston, Ontario, Canada. Dr. Waddington: Assistant Dean, Continuing Professional Development, Associate Professor, Obstetrics and Gynecology, Queen's Health Sciences, Kingston, Ontario, Canada.
医疗保健提供者对人工智能 (AI) 的教育落后于其快速发展. 弥合这一差距需要对专业人士进行全面的AI机制,语言和伦理方面的培训,以利用AI.
科学领域:
- 医疗保健教育卫生保健教育
- 医学中的人工智能
背景情况:
- 人工智能 (AI) 正在迅速改变社会,在医疗保健领域具有显著的诊断和个性化治疗潜力.
- 目前对医疗保健提供者的AI教育不足,造成了关键的知识和技能差距.
- COVID-19大流行突出了诸如不合格的人工智能专家的兴起等问题,强调了结构化培训的必要性.
研究的目的:
- 探索教育医疗保健专业人员关于人工智能的挑战.
- 倡导开发专门的教育计划,将AI整合到医疗保健实践中.
- 强调培训医疗保健专业人员有效利用人工智能工具的必要性.
主要方法:
- 这篇观点文章回顾了医疗保健中人工智能教育的现状.
- 它分析了创建有效人工智能培训课程的障碍和要求.
- 作者提出了跨学科合作和持续专业发展的策略.
主要成果:
- 医疗保健提供者对人工智能方面的教育存在显著的滞后.
- 综合性培训必须涵盖AI语言,机制和道德考虑.
- 需要一个新的医疗保健专业人员干部,在实践和信息学方面都具备技能.
结论:
- 教育编程的紧急发展是必要的,以帮助医疗保健专业人员利用人工智能.
- 跨学科的合作,持续的教育和激励对于成功的AI集成至关重要.
- 投资人工智能教育将确保医疗保健最大限度地受益于技术进步.
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