ArticleJMIR mHealth and uHealth2021
Clinical Advice by Voice Assistants on Postpartum Depression: Cross-Sectional Investigation Using Apple Siri, Amazon Alexa, Google Assistant, and Microsoft Cortana.
Article in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 70 citations in OpenAlex.
- AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Conversational AI for perinatal mental health: promise, limits, and a human-AI stepped-care framework.Frontiers in psychiatry · 2026Article
- From predictive algorithms to generative intelligence: a decadal bibliometric mapping of global research frontiers in AI-driven mental health (2016-2025).Australian journal of psychology · 2026Review
- Article
- User Engagement with A Multimodal Conversational Agent for Self-Care and Chronic Disease Management: A Retrospective Analysis.Journal of medical systems · 2025Article
- Virtual Assistants' Response to Queries About Nicotine Replacement Therapy: A Mixed-Method Analysis.Evaluation & the health professions · 2025Article
- Persuasive chatbot-based interventions for depression: a list of recommendations for improving reporting standards.Frontiers in psychiatry · 2025Article
- Navigating promise and perils: applying artificial intelligence to the perinatal mental health care cascade.npj health systems · 2025Review
- Chatbot for Social Need Screening and Resource Sharing With Vulnerable Families: Iterative Design and Evaluation Study.JMIR human factors · 2024Article
- From Command to Care: A Scoping Review on Utilization of Smart Speakers by Patients and Providers.Mayo Clinic proceedings. Digital health · 2024Review
- Evaluation framework for conversational agents with artificial intelligence in health interventions: a systematic scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Redefining Virtual Assistants in Health Care: The Future With Large Language Models.Journal of medical Internet research · 2024Article
- The ethical aspects of integrating sentiment and emotion analysis in chatbots for depression intervention.Frontiers in psychiatry · 2024Article
- Clinical Accuracy of Large Language Models and Google Search Responses to Postpartum Depression Questions: Cross-Sectional Study.Journal of medical Internet research · 2023Article
- Collecting Food and Drink Intake Data With Voice Input: Development, Usability, and Acceptability Study.JMIR mHealth and uHealth · 2023Article
- Voice Assistants' Responses to Questions About the COVID-19 Vaccine: National Cross-sectional Study.JMIR formative research · 2023Article
- Design and Evaluation Challenges of Conversational Agents in Health Care and Well-being: Selective Review Study.Journal of medical Internet research · 2022Review
- Design and Formative Evaluation of a Virtual Voice-Based Coach for Problem-solving Treatment: Observational Study.JMIR formative research · 2022Article
- Evaluating Voice Assistants' Responses to COVID-19 Vaccination in Portuguese: Quality Assessment.JMIR human factors · 2022Article
- "Hey Siri, Help Me Take Care of My Child": A Feasibility Study With Caregivers of Children With Special Healthcare Needs Using Voice Interaction and Automatic Speech Recognition in Remote Care Management.Frontiers in public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA voice assistant (VA) is inanimate audio-interfaced software augmented with artificial intelligence, capable of 2-way dialogue, and increasingly used to access health care advice. Postpartum depression (PPD) is a common perinatal mood disorder with an annual estimated cost of $14.2 billion. Only a small percentage of PPD patients seek care due to lack of screening and insufficient knowledge of the disease, and this is, therefore, a prime candidate for a VA-based digital health intervention.
objectiveIn order to understand the capability of VAs, our aim was to assess VA responses to PPD questions in terms of accuracy, verbal response, and clinically appropriate advice given.
methodsThis cross-sectional study examined four VAs (Apple Siri, Amazon Alexa, Google Assistant, and Microsoft Cortana) installed on two mobile devices in early 2020. We posed 14 questions to each VA that were retrieved from the American College of Obstetricians and Gynecologists (ACOG) patient-focused Frequently Asked Questions (FAQ) on PPD. We scored the VA responses according to accuracy of speech recognition, presence of a verbal response, and clinically appropriate advice in accordance with ACOG FAQ, which were assessed by two board-certified physicians.
resultsAccurate recognition of the query ranged from 79% to 100%. Verbal response ranged from 36% to 79%. If no verbal response was given, queries were treated like a web search between 33% and 89% of the time. Clinically appropriate advice given by VA ranged from 14% to 29%. We compared the category proportions using the Fisher exact test. No single VA statistically outperformed other VAs in the three performance categories. Additional observations showed that two VAs (Google Assistant and Microsoft Cortana) included advertisements in their responses.
conclusionsWhile the best performing VA gave clinically appropriate advice to 29% of the PPD questions, all four VAs taken together achieved 64% clinically appropriate advice. All four VAs performed well in accurately recognizing a PPD query, but no VA achieved even a 30% threshold for providing clinically appropriate PPD information. Technology companies and clinical organizations should partner to improve guidance, screen patients for mental health disorders, and educate patients on potential treatment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.