One of the dramatic trends at the intersection of computing and healthcare has been patients' increased access to medical information, ranging from self-tracked physiological data to genetic data, tests, and scans. Increasingly however, patients and clinicians have access to advanced machine learning-based tools for diagnosis, prediction, and recommendation based on large amounts of data, some of it patient-generated. Consequently, just as organizations have had to deal with a "Bring Your Own Device" (BYOD) reality in which employees use their personal devices (phones and tablets) for some aspects of their work, a similar reality of "Bring Your Own Algorithm" (BYOA) is emerging in healthcare with its own challenges and support demands. BYOA is changing patient-clinician interactions and the technologies, skills and workflows related to them. In this paper we argue that: (1) BYOA is changing the patient-clinician relationship and the nature of expert work in healthcare, and (2) better patient-clinician-information-interpretation relationships can be facilitated with solutions that integrate technological and organizational perspectives.
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework
Grado Department of Industrial & Systems Engineering, Virginia Tech（弗吉尼亚理工大学格拉多工业与系统工程系）；Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University（迪肯大学智能系统研究与创新研究所）；School of Mathematics, Physics, and Computing, University of Southern Queensland（南昆士兰大学数学、物理与计算学院）；Center for Health Research, University of Southern Queensland（南昆士兰大学健康研究中心）
Clinician Perspectives on Type 1 Diabetes Guidelines and Glucose Data Interpretation
University of Manchester（曼彻斯特大学）；Manchester University NHS Foundation Trust（曼彻斯特大学NHS基金会信托）；Division of Diabetes, Endocrinology and Gastroenterology, University of Manchester（曼彻斯特大学糖尿病、内分泌与胃肠病系）
MIND: Empowering Mental Health Clinicians with Multimodal Data Insights through a Narrative Dashboard
Columbia University（哥伦比亚大学）；University of Washington（华盛顿大学）；Hamilton-Madison House（汉密尔顿-麦迪逊之家）；New York-Presbyterian Hospital/ Weill Cornell Medicine（纽约长老会医院/威尔康奈尔医学）；University of Oregon（俄勒冈大学）；Cornell University（康奈尔大学）；Northeastern University（东北大学）
VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models
Hong Kong University of Science and Technology（香港科技大学）；Massachusetts Institute of Technology（麻省理工学院）；Adobe Research（Adobe研究院）；Children’s Hospital of Zhejiang University School of Medicine（浙江大学医学院附属儿童医院）
"We'll have to see how it works": An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare research
William Harvey Research Institute（威廉·哈维研究所）；Queen Mary University of London（伦敦玛丽女王大学）；Wolfson Institute of Population Health（沃尔夫森人口健康研究所）
Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication
School of Engineering and Technology, University of Washington Tacoma（华盛顿大学塔科马分校工程与技术学院）；Institute for Clinical Effectiveness and Health Policy（临床效果与健康政策研究所）；Centre for Research on Epidemiology and Public Health (CIESP), CONICET（CONICET流行病学与公共卫生研究中心）；School of Nursing, University of Washington Seattle（华盛顿大学西雅图分校护理学院）；School of Nursing and Healthcare Leadership, University of Washington Tacoma（华盛顿大学塔科马分校护理与医疗保健领导学院）
The case for delegated AI autonomy for Human AI teaming in healthcare
University of York（约克大学）；University Hospitals Coventry and Warwickshire NHS Trust（考文垂与沃里克郡大学医院NHS信托基金会）；University of Warwick（华威大学）；Histofy Ltd（Histofy有限公司）；Improvement Academy, Bradford Teaching Hospitals NHS Foundation Trust（布拉德福德教学医院NHS基金会信托基金会改进学院）
Redesigning Electronic Health Record Systems to Support Developing Countries
Groupe de Recherche de Prospection et Valorisation des Données (Greprovad)（数据勘探与增值研究组（Greprovad））；Université de Sherbrooke（舍布鲁克大学）；University of Ottawa（渥太华大学）；Université TÉLUQ（泰卢克大学）；Université Paris-Saclay（巴黎-萨克雷大学）；University of Kinshasa（金沙萨大学）；Montreal Behavioural Medicine Centre, CIUSSS-NIM/UQAM/Concordia（蒙特利尔行为医学中心，CIUSSS-NIM/魁北克大学蒙特利尔分校/康考迪亚大学）；Hospital Monkole（蒙科莱医院）
Clinical knowledge in LLMs does not translate to human interactions
Oxford Internet Institute（牛津互联网研究所）；University of Oxford（牛津大学）；Nuffield Department of Primary Care（初级保健部门）；North Wales Medical School（北威尔士医学院）；Bangor University（班戈大学）；Contextual AI（上下文AI）；MLCommons；Factored AI；Birmingham Women’s and Children’s NHS Foundation Trust（伯明翰妇女和儿童国家卫生信托基金会）；Institute of Biomedical Engineering（生物医学工程研究所）
