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PLOS Digital Health is a peer-reviewed journal published by PLOS, covering Mobile Health and mHealth Applications, Artificial Intelligence in Healthcare and Education, Digital Mental Health Interventions. Over its lifetime it has published 1,135 papers, which have received 16,779 citations.
| Publisher | PLOS |
|---|---|
| Reference style | Numbered (Vancouver) Numbered — [1], [2] in the text 1. Smith A, Jones B, Lee C. A representative article title. PLOS Digital Health. 2023;12(3):45–58.
Formats any DOI in PLOS Digital Health style. No sign-up. |
| Publishes research in | Mobile Health and mHealth Applications Artificial Intelligence in Healthcare and Education Digital Mental Health Interventions Machine Learning in Healthcare Telemedicine and Telehealth Implementation |
| ISSN | 2767-3170 |
| Citation impact (2-yr) | 5.83 |
| h-index | 51 |
| i10-index | 264 |
| Article processing charge | $2,575 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | Harvard University |
| Journal website | journals.plos.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
We evaluated the performance of a large language model called ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK, and Step 3. ChatGPT performed at or near the passing threshold for all three exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level…
The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such algorithms may be shaped by various factors such as social determinants of health that can influence health outcomes. While AI algorithms have been proposed as a tool to expand the reach of quality healthcare to underserved communities and improve health equity, recent…
BACKGROUND: While artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions. Here, we describe the landscape of AI in clinical medicine to delineate population and data-source disparities. METHODS: We performed a scoping…
Biases in medical artificial intelligence (AI) arise and compound throughout the AI lifecycle. These biases can have significant clinical consequences, especially in applications that involve clinical decision-making. Left unaddressed, biased medical AI can lead to substandard clinical decisions and the perpetuation and exacerbation of longstanding healthcare disparities. We discuss potential biases that can arise at…
INTRODUCTION: Harnessing new digital technologies can improve access to health care but can also widen the health divide for those with poor digital literacy. This scoping review aims to assess the current situation of low digital health literacy in terms of its definition, reach, impact on health and interventions for its mitigation. METHODS: A comprehensive…
