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User-Driven Development of a Digital Behavioral Intervention for Chronic Pain: Multimethod Multiphase Study

User-Driven Development of a Digital Behavioral Intervention for Chronic Pain: Multimethod Multiphase Study

I put more stock in ACT. Acceptance, which is something I’ve worked really hard on because I’ve been a bit black or white.” [Patient 5] “I personally would have had a more difficult time if [I] met the patients face-to-face in this type of treatment without any support... Having a treatment program that you follow, I feel that the knowledge I have [then] about chronic pain is good enough.”

Afra Selma Taygar, Sara Laureen Bartels, Rocío de la Vega, Ida Flink, Linnéa Engman, Suzanne Petersson, Sophie I Johnsson, Katja Boersma, Lance M McCracken, Rikard K Wicksell

JMIR Form Res 2025;9:e74064

Quality and Privacy Policy Compliance of Mental Health Care Apps in China: Cross-Sectional Evaluation Study

Quality and Privacy Policy Compliance of Mental Health Care Apps in China: Cross-Sectional Evaluation Study

In health care, privacy is of paramount importance, and the lack of privacy policies has raised concerns about the legitimacy of the “I agree” option presented to users [65]. Surveys have indicated that apprehension about personal data collection is a frequently cited reason for rejecting health care app adoption [66,67]. Legal frameworks have historically recognized the privacy and confidentiality of personally identifiable information as fundamental human rights [68].

Xinying Lin, Xingxing Wu, Ziping Zhu, Danting Chen, Hong Li, Rong Lin

J Med Internet Res 2025;27:e66762

Generative AI in Medicine: Pioneering Progress or Perpetuating Historical Inaccuracies? Cross-Sectional Study Evaluating Implicit Bias

Generative AI in Medicine: Pioneering Progress or Perpetuating Historical Inaccuracies? Cross-Sectional Study Evaluating Implicit Bias

Lin et al [10] evaluated 12 distinctive images per specialty and demonstrated no significant differences between the AAMC residency data and the ethnic makeup of AI -generated faces. Their results are inconsistent with our data where we instead demonstrated a significant difference in gender among 12/19 specialties when comparing AI-generated images to AAMC residency data.

Philip Sutera, Rohini Bhatia, Timothy Lin, Leslie Chang, Andrea Brown, Reshma Jagsi

JMIR AI 2025;4:e56891