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Allied Health Professionals’ Perceptions of Artificial Intelligence in the Clinical Setting: Cross-Sectional Survey

Allied Health Professionals’ Perceptions of Artificial Intelligence in the Clinical Setting: Cross-Sectional Survey

Initial CFA using the original SHAIP tool showed that question 10 “I believe that should AI technology make an error, full responsibility lies with the healthcare professional” had a low correlation (0.097, P=.26) with the preparedness for AI factor.

Jane Hoffman, Laetitia Hattingh, Lucy Shinners, Rebecca L Angus, Brent Richards, Ian Hughes, Rachel Wenke

JMIR Form Res 2024;8:e57204

Heuristics Identified in Health Data–Sharing Preferences of Patients With Cancer: Qualitative Focus Group Study

Heuristics Identified in Health Data–Sharing Preferences of Patients With Cancer: Qualitative Focus Group Study

We conducted a qualitative focus group study with patients with cancer and survivors in Canada as part of the Canadian Network for Learning Healthcare Systems and Cost Effective ‘Omics Innovation (CLEO) project, an initiative evaluating 6 precision oncology programs in Canada to inform the design of a learning health care system for cancer research and care [27].

Anna Hermansen, Samantha Pollard, Kimberlyn McGrail, Nick Bansback, Dean A Regier

J Med Internet Res 2024;26:e63155

Implementation and User Satisfaction of a Comprehensive Telemedicine Approach for SARS-CoV-2 Self-Sampling: Monocentric, Prospective, Interventional, Open-Label, Controlled, Two-Arm Feasibility Study

Implementation and User Satisfaction of a Comprehensive Telemedicine Approach for SARS-CoV-2 Self-Sampling: Monocentric, Prospective, Interventional, Open-Label, Controlled, Two-Arm Feasibility Study

Interoperable data were used as the TG made anonymized, standardized datasets available for extraction using Health Level 7-Fast Healthcare Interoperability Resources (HL7-FHIR), contributing to the national COVID-19 Data Exchange Platform (CODEX) [15] and enabling epidemiological evaluation based on the German Corona Consensus (GECCO) [16] dataset.

Florian Voit, Johanna Erber, Silvia Egert-Schwender, Michael Hanselmann, Michael Laxy, Victoria Kehl, Dieter Hoffmann, Samuel D Jeske, Thomas Michler, Ulrike Protzer, Florian Kohlmayer, Roland M Schmid, Christoph D Spinner, Simon Weidlich

JMIR Form Res 2024;8:e57608

Video Remote Sign Language Interpreting in Health Communication for Deaf People: Protocol for a Randomized Controlled Trial

Video Remote Sign Language Interpreting in Health Communication for Deaf People: Protocol for a Randomized Controlled Trial

Reference 3: Healthcare providers' and deaf patients' interpreting preferences for critical care and Reference 6: Deaf patients' preferred communication in clinical settings: implications for healthcarehealthcare

Minerva Rivas Velarde, Laura Catalina Izquierdo Martinez, Jyoti Dalal, Angela Martinez-R, Danna Lesley Cruz Reyes, Jess Cuculick, Alexie Vallejo-Silva, Jonathan Irreño-Sotomonte, Nora Groce

JMIR Res Protoc 2024;13:e64590

A Taxonomy and Archetypes of AI-Based Health Care Services: Qualitative Study

A Taxonomy and Archetypes of AI-Based Health Care Services: Qualitative Study

By using the exact search string “(“artificial intelligence” OR “AI” OR “machine learning” OR “deep learning”) AND (“healthcare” OR “health care” OR “clinical” OR “medicine”) AND (“service” OR “application”),” we were able to exclude any literature irrelevant to our study. This left us with 9318 relevant results. Furthermore, 2 members of the research team (MB and PK) then analyzed the title and abstract of each paper.

Marlene Blaß, Henner Gimpel, Philip Karnebogen

J Med Internet Res 2024;26:e53986