Review of HIPAA, Part 2: Limitations, Rights, Violations, and Role for the Imaging Technologist

Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of "Responsibility" in Artificial Intelligence within the Healthcare Context

The increasing integration of artificial intelligence (AI) in healthcare presents a host of ethical, legal, social, and political challenges involving various stakeholders. These challenges prompt various studies proposing frameworks and guidelines to tackle these issues, emphasizing distinct phases of AI development, deployment, and oversight. As a result, the notion of responsible AI has become widespread, incorporating ethical principles such as transparency, fairness, responsibility, and privacy. This paper explores the existing literature on AI use in healthcare to examine how it addresses, defines, and discusses the concept of responsibility. We conducted a scoping review of literature related to AI responsibility in healthcare, searching databases and reference lists between January 2017 and January 2022 for terms related to "responsibility" and "AI in healthcare", and their derivatives. Following screening, 136 articles were included. Data were grouped into four thematic categories: (1) the variety of terminology used to describe and address responsibility; (2) principles and concepts associated with responsibility; (3) stakeholders' responsibilities in AI clinical development, use, and deployment; and (4) recommendations for addressing responsibility concerns. The results show the lack of a clear definition of AI responsibility in healthcare and highlight the importance of ensuring responsible development and implementation of AI in healthcare. Further research is necessary to clarify this notion to contribute to developing frameworks regarding the type of responsibility (ethical/moral/professional, legal, and causal) of various stakeholders involved in the AI lifecycle.

Management of partial-thickness rotator cuff tears with autologous adipose-derived regenerative cells is safe and more effective than injection of corticosteroid

Symptomatic, partial-thickness rotator cuff tears (sPTRCT) are problematic. This study tested the hypothesis that management of sPTRCT with injection of fresh, uncultured, unmodified, autologous, adipose-derived regenerative cells (UA-ADRCs) is safe and more effective than injection of corticosteroid even in the long run. To this end, subjects who had completed a former randomized controlled trial were enrolled in the present study. At baseline these subjects had not responded to physical therapy treatments for at least 6 weeks, and were randomly assigned to receive respectively a single injection of UA-ADRCs (n = 11) or a single injection of methylprednisolone (n = 5). Efficacy was assessed using the ASES Total score, pain visual analogue scale (VAS), RAND Short Form-36 Health Survey and range of motion at 33.2 ± 1.0 (mean ± SD) and 40.6 ± 1.9 months post-treatment. Proton density, fat-saturated, T2-weighted MRI of the index shoulder was performed at both study visits. There were no greater risks connected with injection of UA-ADRCs than those connected with injection of corticosteroid. The subjects in the UA-ADRCs group showed statistically significantly higher mean ASES Total scores than the subjects in the corticosteroid group. The MRI scans at 6 months post-treatment allowed to "watch the UA-ADRCs at work".

Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record

The electronic health record (EHR) represents a rich source of patient information, increasingly being leveraged for cardiovascular research. Although its primary use remains the seamless delivery of health care, the various longitudinally aggregated structured and unstructured data elements for each patient within the EHR can define the computational phenotypes of disease and care signatures and their association with outcomes. Although structured data elements, such as demographic characteristics, laboratory measurements, problem lists, and medications, are easily extracted, unstructured data are underused. The latter include free text in clinical narratives, documentation of procedures, and reports of imaging and pathology. Rapid scaling up of data storage and rapid innovation in natural language processing and computer vision can power insights from unstructured data streams. However, despite an array of opportunities for research using the EHR, specific expertise is necessary to adequately address confidentiality, accuracy, completeness, and heterogeneity challenges in EHR-based research. These often require methodological innovation and best practices to design and conduct successful research studies. Our review discusses these challenges and their proposed solutions. In addition, we highlight the ongoing innovations in federated learning in the EHR through a greater focus on common data models and discuss ongoing work that defines such an approach to large-scale, multicenter, federated studies. Such parallel improvements in technology and research methods enable innovative care and optimization of patient outcomes.

Diabetes Technology Meeting 2021

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 4 to November 6, 2021. This meeting brought together speakers to discuss various developments within the field of diabetes technology. Meeting topics included blood glucose monitoring, continuous glucose monitoring, novel sensors, direct-to-consumer telehealth, metrics for glycemia, software for diabetes, regulation of diabetes technology, diabetes data science, artificial pancreas, novel insulins, insulin delivery, skin trauma, metabesity, precision diabetes, diversity in diabetes technology, use of diabetes technology in pregnancy, and green diabetes. A live demonstration on a mobile app to monitor diabetic foot wounds was presented.