scholarly journals Artificial intelligence in academic libraries: An environmental scan

2020 ◽  
Vol 39 (4) ◽  
pp. 347-356 ◽  
Author(s):  
Amanda Wheatley ◽  
Sandy Hervieux
Keyword(s):  
Artificial Intelligence ◽  
Academic Libraries ◽  
Environmental Scan

scholarly journals Proxying the Data Body: Artificial Intelligence, Federated Identity, and Machinic Subjection

2020 ◽  
Vol 15 (1) ◽  
pp. 35-50
Author(s):  
Sam Popowich
Keyword(s):  
Artificial Intelligence ◽  
Intellectual Property ◽  
Academic Libraries ◽  
User Authentication ◽  
Self Management ◽  
Artificial Intelligence Techniques ◽  
Critical Theories ◽  
Bernard Stiegler ◽  
Federated Identity

Academic libraries have recently seen a shift from self-management of user-authentication of licensed resources themselves, to cloud-based implementations of "federated identity" technologies. Such technologies aim to solve the problems of fragile access to licensed resources while also better protecting publishers' intellectual property. However, federated identity systems raise a host of issues regarding privacy, surveillance, machinic subjection, and algorithmic governance. This paper traces the development of federated identity systems out of earlier authentication processes, shows how such systems use artificial intelligence techniques to create a trackable "data body" for each student, and then analyzes this whole procedure through the critical theories of Maurizio Lazzarato and Bernard Stiegler. In conclusion, the article argues that the emergent nature of the "data body" creates ambiguity between the hyper-control of contemporary technologies and the possibility of resisting them.

scholarly journals Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach

10.2196/30940 ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. e30940
Author(s):  
David Wiljer ◽  
Mohammad Salhia ◽  
Elham Dolatabadi ◽  
Azra Dhalla ◽  
Caitlin Gillan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Health Care Providers ◽  
Scoping Review ◽  
The United States ◽  
Data Driven ◽  
Data Sets ◽  
Health Providers ◽  
Care Providers ◽  
Environmental Scan

Background Significant investments and advances in health care technologies and practices have created a need for digital and data-literate health care providers. Artificial intelligence (AI) algorithms transform the analysis, diagnosis, and treatment of medical conditions. Complex and massive data sets are informing significant health care decisions and clinical practices. The ability to read, manage, and interpret large data sets to provide data-driven care and to protect patient privacy are increasingly critical skills for today’s health care providers. Objective The aim of this study is to accelerate the appropriate adoption of data-driven and AI-enhanced care by focusing on the mindsets, skillsets, and toolsets of point-of-care health providers and their leaders in the health system. Methods To accelerate the adoption of AI and the need for organizational change at a national level, our multistepped approach includes creating awareness and capacity building, learning through innovation and adoption, developing appropriate and strategic partnerships, and building effective knowledge exchange initiatives. Education interventions designed to adapt knowledge to the local context and address any challenges to knowledge use include engagement activities to increase awareness, educational curricula for health care providers and leaders, and the development of a coaching and practice-based innovation hub. Framed by the Knowledge-to-Action framework, we are currently in the knowledge creation stage to inform the curricula for each deliverable. An environmental scan and scoping review were conducted to understand the current state of AI education programs as reported in the academic literature. Results The environmental scan identified 24 AI-accredited programs specific to health providers, of which 11 were from the United States, 6 from Canada, 4 from the United Kingdom, and 3 from Asian countries. The most common curriculum topics across the environmental scan and scoping review included AI fundamentals, applications of AI, applied machine learning in health care, ethics, data science, and challenges to and opportunities for using AI. Conclusions Technologies are advancing more rapidly than organizations, and professionals can adopt and adapt to them. To help shape AI practices, health care providers must have the skills and abilities to initiate change and shape the future of their discipline and practices for advancing high-quality care within the digital ecosystem. International Registered Report Identifier (IRRID) PRR1-10.2196/30940

scholarly journals Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach (Preprint)

2021 ◽  
Author(s):  
David Wiljer ◽  
Mohammad Salhia ◽  
Elham Dolatabadi ◽  
Azra Dhalla ◽  
Caitlin Gillan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Health Care Providers ◽  
Scoping Review ◽  
The United States ◽  
Data Driven ◽  
Data Sets ◽  
Health Providers ◽  
Care Providers ◽  
Environmental Scan

BACKGROUND Significant investments and advances in health care technologies and practices have created a need for digital and data-literate health care providers. Artificial intelligence (AI) algorithms transform the analysis, diagnosis, and treatment of medical conditions. Complex and massive data sets are informing significant health care decisions and clinical practices. The ability to read, manage, and interpret large data sets to provide data-driven care and to protect patient privacy are increasingly critical skills for today’s health care providers. OBJECTIVE The aim of this study is to accelerate the appropriate adoption of data-driven and AI-enhanced care by focusing on the mindsets, skillsets, and toolsets of point-of-care health providers and their leaders in the health system. METHODS To accelerate the adoption of AI and the need for organizational change at a national level, our multistepped approach includes creating awareness and capacity building, learning through innovation and adoption, developing appropriate and strategic partnerships, and building effective knowledge exchange initiatives. Education interventions designed to adapt knowledge to the local context and address any challenges to knowledge use include engagement activities to increase awareness, educational curricula for health care providers and leaders, and the development of a coaching and practice-based innovation hub. Framed by the Knowledge-to-Action framework, we are currently in the knowledge creation stage to inform the curricula for each deliverable. An environmental scan and scoping review were conducted to understand the current state of AI education programs as reported in the academic literature. RESULTS The environmental scan identified 24 AI-accredited programs specific to health providers, of which 11 were from the United States, 6 from Canada, 4 from the United Kingdom, and 3 from Asian countries. The most common curriculum topics across the environmental scan and scoping review included AI fundamentals, applications of AI, applied machine learning in health care, ethics, data science, and challenges to and opportunities for using AI. CONCLUSIONS Technologies are advancing more rapidly than organizations, and professionals can adopt and adapt to them. To help shape AI practices, health care providers must have the skills and abilities to initiate change and shape the future of their discipline and practices for advancing high-quality care within the digital ecosystem. CLINICALTRIAL INTERNATIONAL REGISTERED REPORT PRR1-10.2196/30940

Artificial intelligence tools and perspectives of university librarians: An overview

2020 ◽  
Vol 37 (3) ◽  
pp. 116-124
Author(s):  
Muhammad Yousuf Ali ◽  
Salaman Bin Naeem ◽  
Rubina Bhatti
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Academic Libraries ◽  
Information Science ◽  
Library And Information Science ◽  
Text Data ◽  
Online Searching ◽  
Pattern Recognition Methods ◽  
The University ◽  
Science Departments

The main purpose of this paper is to assess and examine the possible application of Artificial Intelligence (AI) tools in Pakistani academic libraries, particularly those areas of library technical and library user services where AI could be applied in the near future. A secondary purpose is to bring the library perspective on AI to the forefront of the scholarly world. This is a self-exploratory study, in which a qualitative approach interview has been conducted with 10 chief librarians/library heads (5 public + 5 private sectors) from universities regarding their views on the adoption of artificial intelligence tools in Pakistani academic libraries. Results are tabulated in a descriptive format. Librarians are aware of AI technologies. Services based on Natural Language Processing (NLP) are used in libraries, e.g. Google Assistant, Voice Searching, and Google Translate. Pattern recognition methods, such as text data mining, are also used to retrieve library material and conduct online searching. Big data is accessed via services such as cloud computing, OneDrive, and Google Drive. There is a very low level of awareness of robotics and chatbots. This study provides librarians with suggestions as to how AI tools could be used in libraries which either have yet to adopt AI technologies or wish to implement more advanced tools. Pakistani library schools could collaborate with computer science departments to establish AI Labs in the respective library and information science (LIS) departments/libraries. AI challenges funding and technological skills are the key problem to implement with AI in the University Libraries.

scholarly journals The Search for a Service Model of Copyright Best Practices in Academic Libraries

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Stephanie Lewin-Lane ◽  
Nora Dethloff ◽  
Julie Grob ◽  
Adam Townes ◽  
Ashley Lierman
Keyword(s):  
Literature Review ◽  
Best Practices ◽  
Academic Libraries ◽  
Service Model ◽  
Environmental Scan ◽  
University Of Houston ◽  
Policies And Procedures ◽  
The University

To add to their suite of available copyright services and to create a service model of best practices, the University of Houston Libraries’ newly formed Copyright Team initiated a literature review and performed an environmental scan of peer institutions’ copyright policies and procedures. This article outlines the impetus and results for both studies and offers future considerations.

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