Human-AI Collaboration in Healthcare: AI-Enabled Adaptive Learning Systems

医疗保健领域的人机协作：人工智能支持的自适应学习系统

• 批准号: 2722218
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2722218
• 关键词: Human; AI; Collaboration; Healthcare; Enabled

Human-AI collaboration (HAIC) describes systems whereby humans and artificial intelligence (AI) systems work in tandem to produce outcomes superior to independent solutions. Today, AI systems are beginning to be deployed in healthcare systems, for workflow optimisation, and potential economical and productivity benefits. They have been shown to encroach or even outperform the performance of trained experts in some clinical settings ([1]). However, these benefits do not come without drawbacks. Specific to the healthcare domain, AI such as deep learning models are susceptible to bias, can be prone to poor generalisation, and can produce uninterpretable predictions ([2]). Humans are, of course, also not without weaknesses. A study approximated that medical errors from radiologists rank as the third most significant cause of death, with an annual occurrence rate of up to 9.5% ([3]). The error rate can be attributed to several factors, such as high concentration, large workload and quick turnover, which contributes to fatigue of the radiologists ([4]). HAIC seeks to mitigate the individual weaknesses of humans and AI while leveraging their respective strengths, ultimately developing an enhanced system. HAIC encompasses a wide range of topics, including out-of-distribution generalisation, deferral-based systems, explainable AI, audio-visual computer vision, and multimodal AI models. In this DPhil project, our initial goal is to develop large multimodal language models for creating AI-enabled adaptive learning systems. For instance, sonographers face the demanding profession of maintaining high diagnostic precision in stressful situations with time constraints, which requires a high level of skill. Transferring expert knowledge and expertise to new trainees presents a significant challenge.Streamlining this time- and cost-intensive process can be achieved through HAIC by developing AI systems that convey task-specific expert knowledge to novice trainees while adapting to their evolving expertise levels over time. This project falls under the EPSRC's research area of human-computer interaction and will be carried out in collaboration with the OxSTaR (Oxford Simulation, Teaching, and Research) team. HAIC is a relatively new field in healthcare; therefore, there are many novel problems to address.Although there have been some preliminary developments in various types of AI-enabled adaptive learning systems, these have primarily been implemented in the education domain. There are no publications of similar work in the healthcare domain.References[1] Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes Van Diest, Bram Van Ginneken, NicoKarssemeijer, Geert Litjens, Jeroen AWM Van Der Laak, Meyke Hermsen, Quirine F Manson,Maschenka Balkenhol, et al. Diagnostic assessment of deep learning algorithms for detection oflymph node metastases in women with breast cancer. Jama, 318(22):2199-2210, 2017.[2] Milena A. Gianfrancesco, Suzanne Tamang, Jinoos Yazdany, and Gabriela Schmajuk. PotentialBiases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA InternalMedicine, 178(11):1544-1547, 11 2018.[3] Martin A Makary and Michael Daniel. Medical error-the third leading cause of death in the us.Bmj, 353, 2016.[4] Stephen Waite, Srinivas Kolla, Jean Jeudy, Alan Legasto, Stephen L Macknik, Susana Martinez-Conde, Elizabeth A Krupinski, and Deborah L Reede. Tired in the reading room: the influence offatigue in radiology. Journal of the American College of Radiology, 14(2):191-197, 2017.

Human-Ai合作（HAIC）描述了人类和人工智能（AI）系统同时起作用的系统，以产生优于独立解决方案的结果。如今，AI系统开始在医疗保健系统中部署，以进行工作流程优化以及潜在的经济和生产力效益。他们已被证明在某些临床环境中侵占甚至胜过训练有素的专家的表现（[1]）。但是，这些好处并非没有缺点。特定于医疗保健领域，诸如深度学习模型之类的AI容易受到偏见的影响，可能容易受到概括的不良，并且可以产生无法解释的预测（[2]）。当然，人类也不是没有弱点。一项研究近似于放射科医生的医疗错误是第三大重要的死亡原因，年增长率高达9.5％（[3]）。错误率可以归因于几个因素，例如高浓度，大工作量和快速营业额，这导致了放射科医生的疲劳（[4]）。海克试图减轻人类和人工智能的个人弱点，同时利用各自的优势，最终发展增强的系统。 HAIC涵盖了广泛的主题，包括分布外的概括，基于延迟的系统，可解释的AI，视听计算机视觉和多模式AI模型。在这个DPHIL项目中，我们的最初目标是开发大型的多模式模型，以创建AI-ables Adipaptive学习系统。例如，超声检查员面临着苛刻的职业，即在压力很大的情况下保持高度诊断精度，这需要高水平的技能。将专家知识和专业知识转移给新学员带来了重大挑战。通过开发AI系统，可以通过HAIC来实现这一时间和成本密集的过程，这些AI系统可以随着时间的推移将特定于任务的专家知识传达给新手受训者，同时适应其不断发展的专业知识。该项目属于EPSRC的人力计算研究领域，并将与Oxstar（Oxford Simulation，Teachert和Research）团队合作进行。海克是医疗保健中相对较新的领域。因此，有许多新的问题需要解决。尽管在各种AI-na-a-apaptive自适应学习系统中有一些初步发展，但这些发展主要是在教育领域实施的。 There are no publications of similar work in the healthcare domain.References[1] Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes Van Diest, Bram Van Ginneken, NicoKarssemeijer, Geert Litjens, Jeroen AWM Van Der Laak, Meyke Hermsen, Quirine F Manson,Maschenka Balkenhol, et al.对乳腺癌女性检测远程淋巴结转移检测深度学习算法的诊断评估。 JAMA，318（22）：2199-2210，2017年。[2] Milena A. Gianfrancesco，Suzanne Tamang，Jinoos Yazdany和Gabriela Schmajuk。使用电子健康记录数据的机器学习算法中的潜在次数。 Jama Internalmedicine，178（11）：1544-1547，118。[3]马丁是马丁和迈克尔·丹尼尔。医疗错误 - 美国第三大死亡原因。BMJ，353，2016。[4] Stephen Waite，Srinivas Kolla，Jean Jeudy，Alan Legasto，Stephen L Macknik，Susana Martinez-Conde，Elizabeth A Krupinski和Deborah L Reede。在阅读室里疲倦：放射学的影响力。美国放射学院杂志，14（2）：191-197，2017。