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Exploring Metadata Catalogs in Health Care Data Ecosystems: Taxonomy Development Study

Exploring Metadata Catalogs in Health Care Data Ecosystems: Taxonomy Development Study

To remedy this problem, the research objective is to provide actionable design knowledge that is universally applicable in real-world HMDC use cases, thus allowing to infer the following research questions (RQs): RQ1: What are taxonomy elements (ie, dimensions and characteristics) to structure HMDCs from a design science perspective? RQ2: How does the proposed taxonomy effect real-world use cases?

Simon Scheider, Mostafa Kamal Mallick

JMIR Form Res 2025;9:e63396

Current State of Community-Driven Radiological AI Deployment in Medical Imaging

Current State of Community-Driven Radiological AI Deployment in Medical Imaging

We also present a taxonomy of AI-based solution scenarios. Categorizing the AI use case in such a manner helps to have a broad categorization of AI tools and deployment scenarios. This taxonomy serves as a valuable resource for health care professionals, AI researchers, and hospitals, as it facilitates a systematic understanding of different AI use cases and their potential benefits.

Vikash Gupta, Barbaros Erdal, Carolina Ramirez, Ralf Floca, Bradley Genereaux, Sidney Bryson, Christopher Bridge, Jens Kleesiek, Felix Nensa, Rickmer Braren, Khaled Younis, Tobias Penzkofer, Andreas Michael Bucher, Ming Melvin Qin, Gigon Bae, Hyeonhoon Lee, M Jorge Cardoso, Sebastien Ourselin, Eric Kerfoot, Rahul Choudhury, Richard D White, Tessa Cook, David Bericat, Matthew Lungren, Risto Haukioja, Haris Shuaib

JMIR AI 2024;3:e55833

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

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

To answer this, we propose a multilayered taxonomy created in accordance with both the well-established method of taxonomy development by Nickerson et al [24] and the recent extension by Kundisch et al [25]. By exploring both empirical-to-conceptual (E2 C) and conceptual-to-empirical (C2 E) iterations, we pursued a bilateral development for the structure of our taxonomy, deductively from real-world examples and inductively from a structured literature review.

Marlene Blaß, Henner Gimpel, Philip Karnebogen

J Med Internet Res 2024;26:e53986

Automatic Recommender System of Development Platforms for Smart Contract–Based Health Care Insurance Fraud Detection Solutions: Taxonomy and Performance Evaluation

Automatic Recommender System of Development Platforms for Smart Contract–Based Health Care Insurance Fraud Detection Solutions: Taxonomy and Performance Evaluation

We developed a taxonomy of blockchain development platforms, used to determine the characteristics of the platforms that are available for implementing applications in the health insurance field. The platform taxonomy is based on the investigation of 102 blockchain platforms and their applications domains in the literature. We exemplified the applicability of our automated decision map recommender system by developing and implementing blockchain smart contracts for the detection of 12 fraud scenarios.

Rima Kaafarani, Leila Ismail, Oussama Zahwe

J Med Internet Res 2024;26:e50730

The SoCAP (Social Communication, Affiliation, and Presence) Taxonomy of Social Features: Scoping Review of Commercially Available eHealth Apps

The SoCAP (Social Communication, Affiliation, and Presence) Taxonomy of Social Features: Scoping Review of Commercially Available eHealth Apps

The remaining 6 apps that met inclusion criteria were reserved to test the So CAP taxonomy after it was finalized. Our first step was to create a preliminary codebook by referring to an existing social media feature taxonomy that focused exclusively on peer-based features in e Health interventions [51]. The taxonomy comprised several categories and their corresponding features.

Ian Kwok, Melanie Freedman, Lisa Kamsickas, Emily G Lattie, Dershung Yang, Judith Tedlie Moskowitz

J Med Internet Res 2024;26:e49714

Authors’ Reply: “Evaluating GPT-4’s Cognitive Functions Through the Bloom Taxonomy: Insights and Clarifications”

Authors’ Reply: “Evaluating GPT-4’s Cognitive Functions Through the Bloom Taxonomy: Insights and Clarifications”

We appreciate the thoughtful commentary titled “Evaluating GPT-4’s Cognitive Functions Through the Bloom Taxonomy: Insights and Clarifications” [1] and welcome the opportunity to clarify and expand upon our research findings [2] regarding GPT-4’s cognitive evaluation using the Bloom taxonomy. First, we acknowledge the confusion surrounding the use of the term “difficulty” in our manuscript.

Anne Herrmann-Werner, Teresa Festl-Wietek, Friederike Holderried, Lea Herschbach, Jan Griewatz, Ken Masters, Stephan Zipfel, Moritz Mahling

J Med Internet Res 2024;26:e57778

Privacy, Security, and Legal Issues in the Health Cloud: Structured Review for Taxonomy Development

Privacy, Security, and Legal Issues in the Health Cloud: Structured Review for Taxonomy Development

The term taxonomy is different from other similar words. Compared with classification, in some literature, it refers to groupings that are derived based on empirical studies with involvement of cluster analysis and statistical techniques. This definition is also referred to as numerical taxonomy [45]. Taxonomy is also considered as a classification scheme [46], and it is possible to use the terms of classification scheme, taxonomy, and typology as substitutes of each other.

Zahra Zandesh

JMIR Form Res 2024;8:e38372

What is Diminished Virtuality? A Directional and Layer-Based Taxonomy for the Reality-Virtuality Continuum

What is Diminished Virtuality? A Directional and Layer-Based Taxonomy for the Reality-Virtuality Continuum

In addition, this should all be performed in real time when a user is walking around the real world, and an algorithm has to do the following (note that the first 3 items are part of the Extent of World Knowledge axis of the taxonomy by Milgram and Kishino [1]): Detect and track the real object that has to be removed or diminished; Perform geometric modeling of the scene and objects to be added or subtracted (preexisting or captured once or in real time); Apply the lighting model of the scene to objects added

Jan Egger, Christina Gsaxner, Jens Kleesiek, Behrus Puladi

JMIR XR Spatial Comput 2024;1:e52904

Assessing ChatGPT’s Mastery of Bloom’s Taxonomy Using Psychosomatic Medicine Exam Questions: Mixed-Methods Study

Assessing ChatGPT’s Mastery of Bloom’s Taxonomy Using Psychosomatic Medicine Exam Questions: Mixed-Methods Study

While Bloom’s taxonomy is widely used and offers a structured approach to learning outcomes, some educators believe that its hierarchical nature might not always represent the complexity of learning [12]. Although derived from human learning processes, Bloom’s taxonomy provides an ideal framework to describe the cognitive processes that underlie success and failure.

Anne Herrmann-Werner, Teresa Festl-Wietek, Friederike Holderried, Lea Herschbach, Jan Griewatz, Ken Masters, Stephan Zipfel, Moritz Mahling

J Med Internet Res 2024;26:e52113

Evaluating Conversational Agents for Mental Health: Scoping Review of Outcomes and Outcome Measurement Instruments

Evaluating Conversational Agents for Mental Health: Scoping Review of Outcomes and Outcome Measurement Instruments

This is further hampered by (1) the lack of standardized taxonomy to describe the breadth of measurement instruments available [8,10,11,15] and (2) the use of outcome measurement instruments without validity evidence, which affects the credibility of study findings [16]. Lastly, objective measures to assess the performance of the system are also important [9].

Ahmad Ishqi Jabir, Laura Martinengo, Xiaowen Lin, John Torous, Mythily Subramaniam, Lorainne Tudor Car

J Med Internet Res 2023;25:e44548