Call for Papers Theme Issue: Artificial Intelligence (AI) and ChatGPT in Asian and Pacific Islander (API) Health

Asian/Pacific Island Nursing Journal (APINJ) is the official journal of the Asian American / Pacific Islander Nurses Association (AAPINA), devoted to the exchange of knowledge in relation to Asian and Pacific Islander (API) health and nursing care. APINJ is pleased to announce a new Call for Papers on the topic of the use of generative artificial intelligence (AI) and ChatGPT (Chat Generative Pre-trained Transformer) in Asian and Pacific Islander Health.

ChatGPT is a large language model based on the GPT (Generative Pre-trained Transformer) architecture that was launched by OpenAI on November 30, 2022. It is designed to generate human-like responses to natural language inputs and has been trained on a massive corpus of text data from the internet. Developers, researchers, practitioners, clinicians, and businesses alike can use ChatGPT to develop chatbots, virtual assistants, recommendation systems, language translation, and other language-based AI applications. 

Studies have also shown preliminary evidence that ChatGPT has promising applications across the clinical workflow. Moreover, ChatGPT and similar generative AI models have specific applications within medical education, including clinical vignette generation and communications training with AI virtual patients; however, they also pose a number of challenges that future research needs to carefully address

Generative AI, like ChatGPT, can have many applications in health care and medicine, particularly in addressing the unique needs and challenges faced by API communities. Potential topics related to the use of generative AI in health care and nursing care specific to API health that we would consider for APINJ include:

  • Developing culturally responsive virtual health assistants: Virtual health assistants powered by generative AI can help to address language barriers and cultural differences in health care communication with API patients. These assistants can be trained to recognize and respond to cultural cues and provide personalized care recommendations based on the patient's cultural background.

  • Predicting health outcomes for API patients: Generative AI can be used to analyze large amounts of health data to identify risk factors and predict health outcomes for API patients. For example, AI models could be trained to predict the likelihood of developing type 2 diabetes or hypertension in API populations based on genetic and lifestyle factors.

  • Improving nursing care for API patients: Generative AI can help nurses to provide better care to API patients by providing real-time translation services and cultural context during patient interactions. AI-powered tools can also be used to monitor patients’ vital signs and alert nurses to potential health concerns.

  • Enhancing precision medicine for API patients: Generative AI can be used to analyze genomic data from API patients to identify specific genetic variations that are more prevalent in certain API populations. This information can be used to develop more targeted treatments and medications that are tailored to the unique needs of API patients.

  • Promoting mental health and wellness in API communities: Generative AI can be used to develop personalized mental health interventions and treatment plans for API patients. AI models can be trained to recognize cultural nuances and provide culturally sensitive care recommendations to help improve mental health outcomes in API communities.

These are just a few examples of how generative AI can be applied to health care and nursing care specific to API health. As AI technology continues to evolve, there will likely be many more opportunities to improve health care outcomes for API patients.

APINJ welcomes submissions from researchers and practitioners in medicine, health care, computer science, and related fields. We encourage submissions of original research, literature reviews, and viewpoints. Submissions must be original and unpublished. Preprints are highly encouraged to be submitted for peer-review. All submissions will undergo a rigorous peer-review process, and accepted articles will be published as part of a special issue on AI and ChatGPT in Asian and Pacific Islander Health and Nursing Care.

To submit an article to this JMIR theme issue, please go here and select the journal section entitled “Theme Issue (2023): AI & ChatGPT in Asian & Pacific Islander (API) Health”

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Guest Editors

Shu-Fen Wung, Ph.D., RN, ACNP-BC, FAAN

Associate Professor, Biobehavioral Health Science

Associate Professor, BIO5 Institute
C2SHIP UA Director of Translational Health Sciences

Member, Data Science Institute
Member, Sarver Heart Center

All articles submitted to this theme issue will be shared and published rapidly through the following mechanisms:

  • All peer-reviewed articles in this theme issue will be immediately and permanently made open access. This is the standard for all titles within the JMIR Publications portfolio.

  • Articles can be made immediately available in JMIR Preprints (with a DOI) after submission if authors select the preprint option at submission to enable this service.

Submissions not reviewed or accepted for publication in this APINJ theme issue (e-collection) may be offered cascading peer-review or transfer to other JMIR journals according to standard JMIR Publications policies. 

Early-stage formative work that informs the design of future interventions or research may better fit the scope for JMIR Formative Research. Authors are encouraged to submit study protocols or grant proposals to JMIR Research Protocols before data acquisition to pre-register the study (registered reports); subsequent acceptance in one of the JMIR Publications journals is then guaranteed.

The article processing fee (APF) for members of the Asian American / Pacific Islander Nurses Association (AAPINA) is US $300. As such, all manuscripts submitted under the membership will be verified with AAPINA. The APF for non-members is US $1500 upon acceptance.

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