Ethical implications of conversational agents in global public health
Open accessAI snapshot
Written from the paper’s full text.
TL;DR
Conversational agents (chatbots and embodied virtual agents) could help fill global shortages in health-care and mental health services, but their widespread use raises ethical challenges around bias, harm, privacy and inequitable access. The author argues for revised professional ethics codes and an international working group to ensure ethical deployment.
Key findings
The paper is a commentary/analysis rather than an empirical study, so it reports no primary data; its claims are framed around cited figures and ethical argument.
- Mental health worker supply is estimated at as low as 2 per 100 000 population in low-income countries versus more than 70 per 100 000 in high-income nations, creating service gaps that conversational agents might help address.
- Conversational agents are susceptible to design and algorithmic bias, including preference for particular racial or ethnic backgrounds, and to bias from missing data, misclassification, measurement error and small sample sizes that produce inaccurate predictions for subgroups.
- Because agents function with some autonomy, there is risk of harm when systems fail to detect imminent safety risks (e.g., a user revealing suicidal thoughts with immediate plans to die); some patients, such as those with significant psychotic symptoms or cognitive deficits, may not be suitable candidates.
- Most companies market health-related conversational agents for information or training rather than as replacements for health professionals, so requirements such as duty-to-warn are unheeded.
Methods
This is a non-systematic commentary/analysis in the Bulletin of the World Health Organization; no study design, sample or formal analysis is reported. The argument is organized thematically around risks (bias, harm, privacy, inequitable access) and a proposal to revise or develop professional ethics codes and practical guidelines, potentially via a WHO-coordinated international working group.
Limitations
Not stated in the paper (the text does not include a limitations section). Evident from the text: the piece is a narrative commentary without empirical data, systematic review methodology or formal evidence synthesis, so its claims rest on cited literature and the author's argument; the cited workforce figures come from a 2017 WHO Mental Health Atlas reference rather than original analysis.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 27 cited sources checked · 0 supported · 0 flagged
- Premisecitation§ Ethical implications of conversational agents in global public health
Conversational agents are potentially more reliable than humans because they are not affected by fatigue, burnout and cognitive errors.
“They are also potentially more reliable than humans are because they are not affected by fatigue, burnout and cognitive errors.3”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [3] Luxton, 2014, Recommendations for the ethical use and design of artificial intelligent care providers., Artif Intell Med source
Not checked in the free run.
- Premisecitation§ Ethical implications of conversational agents in global public health
Users may experience less anxiety when discussing private concerns, such as disease exposure risk behaviours, with a conversational agent than they would with another person.
“Thus, users may experience less anxiety when discussing private concerns, such as disease exposure risk behaviours, with a conversational agent than they would with another person.3,4,7”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [3] Luxton, 2014, Recommendations for the ethical use and design of artificial intelligent care providers., Artif Intell Med source
Not checked in the free run.
- cites [4] Laranjo, 2018, Conversational agents in healthcare: a systematic review., J Am Med Inform Assoc source
Not checked in the free run.
- cites [7] Bickmore, 2010, Response to a relational agent by hospital patients with depressive symptoms., Interact Comput source
Not checked in the free run.
- Premisecitation§ Ethical implications of conversational agents in global public health
Affective computing techniques can provide conversational agents with the capability to recognize and express emotions.
“Affective computing techniques can then provide the conversational agent with the capability to recognize and express emotions”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [2] Hudlicka, 2015, Artificial intelligence in behavioral and mental healthcare.
Not checked in the free run.
- cites [3] Luxton, 2014, Recommendations for the ethical use and design of artificial intelligent care providers., Artif Intell Med source
Not checked in the free run.
- Premisecitation
Some patients, such as those with significant psychotic symptoms or cognitive deficits, may not be suitable candidates for the use of conversational agents.
“Also, some patients, such as those with significant psychotic symptoms or cognitive deficits, may not be suitable candidates for the use of conversational agents.3”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [3] Luxton, 2014, Recommendations for the ethical use and design of artificial intelligent care providers., Artif Intell Med source
Not checked in the free run.
- Interpretationreasoning
There are ethical challenges to widespread use of conversational agents despite their potential benefits.
“Despite the potential benefits of the technology, there are ethical challenges to widespread use of conversational agents.”
Quote in the paperSubstantial claim
- Premisecitation
The implicit values of the programmers and organizations who collect, select or use data to train algorithms may introduce bias.
“Furthermore, the implicit values of the programmers and organizations who collect, select or use data to train algorithms may also introduce bias.10”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10] Gianfrancesco, 2018, Potential biases in machine learning algorithms using electronic health record data., JAMA Intern Med source
Not checked in the free run.
- Premisecitation§ Ethical implications of conversational agents in global public health
Designing conversational agents to match a user's cultural background can help establish rapport with users, possibly contributing to adherence to treatments and better health outcomes.
“This can help to establish rapport with users, possibly contributing to adherence to treatments and better health outcomes.3”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [3] Luxton, 2014, Recommendations for the ethical use and design of artificial intelligent care providers., Artif Intell Med source
Not checked in the free run.
- Premisecitation
Lack of investment in technological infrastructure in underserved communities and countries, as well as its cost, is a major limitation to the adoption of any advanced technology.
“Lack of investment in technological infrastructure in underserved communities and countries, as well as the cost of it, is a major limitation to the adoption of any advanced technology.11”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [11] Telemedicine: opportunities and developments in Member States: report on the second global survey on eHealth 2009. Geneva: World Health Organization; 2010. Available from: https://www.who.int/goe/publications/goe_telemedicine_2010.pdf [cited 2019 May 8].
Not checked in the free run.
- Premisecitation
Laws and regulations regarding assurance of user privacy can vary significantly across international borders, similar to telehealth services.
“Similar to the use of telehealth services, the laws and regulations regarding assurance of user privacy can vary significantly across international borders.11”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11] Telemedicine: opportunities and developments in Member States: report on the second global survey on eHealth 2009. Geneva: World Health Organization; 2010. Available from: https://www.who.int/goe/publications/goe_telemedicine_2010.pdf [cited 2019 May 8].
Not checked in the free run.
- Interpretationreasoning
Realizing the potential of conversational agents in health-care services depends on whether these technologies are accessible to the populations that could benefit from them.
“Realizing the potential of conversational agents in health-care services depends on whether these technologies are accessible to the populations that could benefit from them.”
Quote in the paperSubstantial claim- Rests on an assumption: Lack of education and low technology literacy are other issues that could limit people's access to and use of conversational agents, resulting in greater health-care disparities across populations.
- Rests on an assumption: As with traditional telemedicine (videoconferencing, telephone consultations and remote monitoring), limitations of the technology or end-user preferences may make conversational agents unsuitable to use.
- Classified as probably not a substantial claim. 5
- May combine two claims. 6
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ Ethical implications of conversational agents in global public healthSpeech recognition and natural language processing technologies can allow conversational agents to converse with humans.
“Speech recognition and natural language processing technologies can allow conversational agents to converse with humans.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [2] Hudlicka, 2015, Artificial intelligence in behavioral and mental healthcare.
Not checked in the free run.
May combine two claims.
Premisecitation§ Ethical implications of conversational agents in global public healthThis shortage in health-care providers results in major gaps in services, contributing to unmet health-care needs and greater disease burden in underserved regions.
“This shortage in health-care providers results in major gaps in services, contributing to unmet health-care needs and greater disease burden in underserved regions.1”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [1] WHO's Mental Health Atlas 2017 highlights global shortage of health workers trained in mental health [internet]. Geneva: World Health Organization; 2017. https://www.who.int/hrh/news/2018/WHO-MentalHealthAtlas2017-highlights-HW-shortage/en/ [cited 2019 May 19].
Not checked in the free run.
May combine two claims.
Premisecitation§ Ethical implications of conversational agents in global public healthOther uses of conversational agents include replacing nurses to provide hospital discharge planning, collecting relevant health information (such as public health surveys), training health-care providers by simulating patients, and providing public health information and education.
“Other use of conversational agents include replacing nurses to provide hospital discharge planning, for collecting relevant health information (such as public health surveys), for training health-care providers by simulating patients, and for providing public health information and education.3,5–9”
Quote in the paperNot a substantial claim0 of 6 sources checked- cites [3] Luxton, 2014, Recommendations for the ethical use and design of artificial intelligent care providers., Artif Intell Med source
Not checked in the free run.
- cites [5] Luxton, 2020, Innovations in global mental health.
Not checked in the free run.
- cites [6] Vaidyam, 2019, Chatbots and conversational agents in mental health: a review of the psychiatric landscape., Can J Psychiatry source
Not checked in the free run.
- cites [7] Bickmore, 2010, Response to a relational agent by hospital patients with depressive symptoms., Interact Comput source
Not checked in the free run.
- cites [8] Luxton, 2015, Artificial intelligence in behavioral and mental health care.
Not checked in the free run.
- cites [9] Rizzo, 2011, An intelligent virtual human system for providing healthcare information and support., Stud Health Technol Inform
Not checked in the free run.
May combine two claims.
Premisecitation§ Ethical implications of conversational agents in global public healthConversational agents are being tested and used to provide and collect health-related information from people, and in some cases, provide treatment and counselling services.
“Conversational agents are being tested and used to provide and collect health-related information from people, and in some cases, provide treatment and counselling services.4–6”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [4] Laranjo, 2018, Conversational agents in healthcare: a systematic review., J Am Med Inform Assoc source
Not checked in the free run.
- cites [5] Luxton, 2020, Innovations in global mental health.
Not checked in the free run.
- cites [6] Vaidyam, 2019, Chatbots and conversational agents in mental health: a review of the psychiatric landscape., Can J Psychiatry source
Not checked in the free run.
May combine two claims.
Premisecitation§ Ethical implications of conversational agents in global public healthThis capability gives conversational agents the ability to adapt to the changing states and needs of people while establishing empathetic relationships with them.
“giving them the ability to adapt to the changing states and needs of people while establishing empathetic relationships with them.2,3”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [2] Hudlicka, 2015, Artificial intelligence in behavioral and mental healthcare.
Not checked in the free run.
- cites [3] Luxton, 2014, Recommendations for the ethical use and design of artificial intelligent care providers., Artif Intell Med source
Not checked in the free run.
May combine two claims.
Premisecitation§ Ethical implications of conversational agents in global public healthThe supply of mental health workers in low-income countries is estimated to be as low as 2 per 100,000 population, compared with more than 70 per 100,000 in high-income nations.
“The supply of mental health workers in low-income countries is estimated to be as low as 2 per 100 000 population, compared with more than 70 per 100 000 in high-income nations.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [1] WHO's Mental Health Atlas 2017 highlights global shortage of health workers trained in mental health [internet]. Geneva: World Health Organization; 2017. https://www.who.int/hrh/news/2018/WHO-MentalHealthAtlas2017-highlights-HW-shortage/en/ [cited 2019 May 19].
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ Ethical implications of conversational agents in global public healthEmbodied conversational agents are used in therapeutic computer games and virtual reality environments to provide interactive skills training and treatments for conditions such as autism spectrum disorder, depression and anxiety disorders.
“Embodied conversational agents are also used in therapeutic computer games and virtual reality environments to provide interactive skills training and treatments for conditions, such as autism spectrum disorder, depression and anxiety disorders.2,5”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [2] Hudlicka, 2015, Artificial intelligence in behavioral and mental healthcare.
Not checked in the free run.
- cites [5] Luxton, 2020, Innovations in global mental health.
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenThe application of conversational agents across different countries and cultures is particularly susceptible to design biases if the organizations deploying the technology have not adequately considered the demographic characteristics and specific needs of the target end-users.
“The application of conversational agents across different countries and different cultures is particularly susceptible to these design biases if the organizations deploying the technology have not adequately considered the demographic characteristics and specific needs of the target end-users.”
Quote in the paperNot a substantial claim- Rests on an assumption: The overt appearance and behaviour of conversational agents are susceptible to design biases, such as preference for particular racial or ethnic background in their design.
- Rests on an assumption: The knowledge base used to train the machine-learning algorithms and the ways that the conversational agent generates new knowledge is also susceptible to bias that is less observable than physical appearance or overt language biases.
- Rests on an assumption: The sources of bias in the data sets used to develop conversational agents may include problems with missing data, misclassification and measurement error and small sample sizes, resulting in underestimation and inaccurate predictions for subgroups of patients.
Classified as probably not a substantial claim.
InterpretationreasoningBecause conversational agents function with some level of autonomy, there is a potential risk of harm to people if the technology does not adequately address scenarios when the system detects potential or imminent safety risks.
“Since conversational agents function with some level of autonomy, there is a potential risk of harm to people if the technology does not adequately address scenarios when the system detects potential or imminent safety risks.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
InterpretationreasoningUser privacy, if not appropriately addressed, could potentially cause harm to users of conversational agents.
“User privacy is another issue that if not appropriately addressed could potentially cause harm to the users of conversational agents.”
Quote in the paperSubstantial claim- Rests on an assumption: Conversational agents can collect large amounts of private and sensitive information when people interact with them.
May combine two claims.
InterpretationreasoningMany ethical and legal requirements that human health-care professionals should comply with, such as duty-to-warn, are unheeded because most companies market conversational agents for information or training rather than as replacements for health professionals.
“Therefore, many of the ethical and legal requirements that human health-care professionals should comply with, such as duty-to-warn, are unheeded.”
Quote in the paperSubstantial claim- Rests on an assumption: At present, most companies that produce conversational agents for health-related uses market them to the public for providing information or training, and not as replacements for health professionals.
The paper relies on this without support.
Conversational agents may be perceived by users as being free from personal bias.
“Conversational agents may also be perceived by users as being free from personal bias.”
The paper relies on this without support.
Conversational agents can be replicated (scaled-up) easily and affordably to meet demand, and users can access them via the internet at any time and almost anywhere.
“A benefit of conversational agents is that they can be replicated (scaled-up) easily and affordably to meet demand, and, unlike with human professionals, users can access them via the internet at any time and almost anywhere.”
The paper relies on this without support.
Developers can design conversational agents to have modifiable physical appearance and mannerisms, language and speech dialect, and other characteristics that match them to a user's cultural background, including race and ethnicity or socioeconomic status.
“Developers can design conversational agents to have modifiable physical appearance and mannerisms, language and speech dialect, and other characteristics that match them to a user’s cultural background, including race and ethnicity or socioeconomic status.”
The paper relies on this without support.
The overt appearance and behaviour of conversational agents are susceptible to design biases, such as preference for particular racial or ethnic background in their design.
“The overt appearance and behaviour of conversational agents are susceptible to design biases, such as preference for particular racial or ethnic background in their design.”
The paper relies on this without support.
The knowledge base used to train the machine-learning algorithms and the ways that the conversational agent generates new knowledge is also susceptible to bias that is less observable than physical appearance or overt language biases.
“The knowledge base used to train the machine-learning algorithms and the ways that the conversational agent generates new knowledge is also susceptible to bias that is less observable than physical appearance or overt language biases.”
The paper relies on this without support.
The sources of bias in the data sets used to develop conversational agents may include problems with missing data, misclassification and measurement error and small sample sizes, resulting in underestimation and inaccurate predictions for subgroups of patients.
“The sources of bias in the data sets used to develop conversational agents may include problems with missing data, misclassification and measurement error and small sample sizes, resulting in underestimation and inaccurate predictions for subgroups of patients.”
The paper relies on this without support.
At present, most companies that produce conversational agents for health-related uses market them to the public for providing information or training, and not as replacements for health professionals.
“At present, most companies that produce conversational agents for health-related uses market them to the public for providing information or training, and not as replacements for health professionals.”
The paper relies on this without support.
Conversational agents can collect large amounts of private and sensitive information when people interact with them.
“Conversational agents can collect large amounts of private and sensitive information when people interact with them.”
The paper relies on this without support.
Lack of education and low technology literacy are other issues that could limit people's access to and use of conversational agents, resulting in greater health-care disparities across populations.
“Lack of education and low technology literacy are other issues that could limit people’s access to and use of conversational agents, resulting in greater health-care disparities across populations.”
The paper relies on this without support.
As with traditional telemedicine (videoconferencing, telephone consultations and remote monitoring), limitations of the technology or end-user preferences may make conversational agents unsuitable to use.
“As with traditional telemedicine (videoconferencing, telephone consultations and remote monitoring) limitations of the technology or end-user preferences may make conversational agents unsuitable to use.”
The paper relies on this without support.
Most current health profession ethics codes and practice guidelines do not address the use of technologies that simulate and replace human professionals.
“Most current health profession ethics codes and practice guidelines do not address the use of technologies that simulate and replace human professionals.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Conversational agents have the potential to be affordably scaled up to reach large proportions of a population.
“They also have the potential to be affordably scaled up to reach large proportions of a population [3].”
From The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (2020) · cites this paper as [3]
Cites
Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data
Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.3763 · Cited by 1,395 · see citing works
Conversational agents in healthcare: a systematic review
Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy072 · Cited by 1,388 · cited 1× · see citing works
“Thus, users may experience less anxiety when discussing private concerns, such as disease exposure risk behaviours, with a conversational agent than they would with another person.(3,4,7) Developers can design conversational agents to have modifiable physical appearance and mannerisms, language and speech dialect, and…”
BackgroundChatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape
Vaidyam, A. N., Wisniewski, H., Halamka, J. D., Kashavan, M. S., & Torous, J. B. (2019) · The Canadian Journal of Psychiatry · doi:10.1177/0706743719828977 · Cited by 1,048 · see citing works
Recommendations for the ethical use and design of artificial intelligent care providers
Luxton, D. D. (2014) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2014.06.004 · Cited by 243 · see citing works
Self-citationResponse to a relational agent by hospital patients with depressive symptoms
Bickmore, T. W., Mitchell, S. E., Jack, B. W., Paasche-Orlow, M. K., Pfeifer, L. M., & O’Donnell, J. (2010) · Interacting with Computers · doi:10.1016/j.intcom.2009.12.001 · Cited by 218 · see citing works
Artificial Intelligence in Behavioral and Mental Health Care
(2016) · Elsevier eBooks · doi:10.1016/c2013-0-12824-3 · Cited by 117 · see citing works
An Intelligent Virtual Human System for Providing Healthcare Information and Support
Rizzo, A., Lange, B. S., Buckwalter, J. G., Forbell, E., Kim, J. M., Sagae, K., Josh, W., ROTHBAUM, B. O., JoAnn, D., Reger, G. M., et al. (2011) · Studies in health technology and informatics · doi:10.3233/978-1-60750-706-2-503 · Cited by 68 · see citing works
Innovations in Global Mental Health
Das, P., & Bhugra, D. (2021) · Springer eBooks · doi:10.1007/978-3-030-57296-9_137 · Cited by 21 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Hudlicka, 2015, Artificial intelligence in behavioral and mental healthcare.
- Luxton, 2015, Artificial intelligence in behavioral and mental health care.
Cited by
Sorted by citations
The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review
Milne-Ives, M., de Cock, C., Lim, E., Shehadeh, M. H., de Pennington, N., Mole, G., Normando, E., & Meinert, E. (2020) · Journal of Medical Internet Research · doi:10.2196/20346 · Cited by 626 · see citing works
Li, H., Zhang, R., Lee, Y.-C., Kraut, R. E., & Mohr, D. C. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00979-5 · Cited by 598 · see citing works
“Their unpredictable nature may generate fl awed, potentially harmful outcomes leading to unexpected negative consequences 11 .”
BackgroundPerceptions and opinions of patients about mental health chatbots: scoping review
Abd-Alrazaq, A. A., Alajlani, M., Ali, N., Denecke, K., Bewick, B. M., & Househ, M. (2021) · Journal of Medical Internet Research · doi:10.2196/17828 · Cited by 301 · see citing works
Artificial Intelligence for Chatbots in Mental Health: Opportunities and Challenges
Denecke, K., Abd-Alrazaq, A., & Househ, M. (2021) · Lecture Notes in Bioengineering · doi:10.1007/978-3-030-67303-1_10 · Cited by 177 · see citing works
Grodniewicz, J. P., & Hohol, M. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1190084 · Cited by 100 · see citing works
The Use and Promise of Conversational Agents in Digital Health
Dingler, T., Kwasnicka, D., Wei, J., Gong, E., & Oldenburg, B. (2021) · Yearbook of Medical Informatics · doi:10.1055/s-0041-1726510 · Cited by 81 · see citing works
User Experiences of Well-Being Chatbots
Kettle, L., & Lee, Y.-C. (2024) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208231162453 · Cited by 45 · see citing works
Examining Humanness as a Metaphor to Design Voice User Interfaces
Desai, S., Dubiel, M., & Leiva, L. A. (2024) · ACM Conversational User Interfaces 2024 · doi:10.1145/3640794.3665535 · Cited by 21 · see citing works
Guglielmucci, F., & Di Basilio, D. (2025) · JMIR Human Factors · doi:10.2196/70698 · Cited by 10 · see citing works
Donnelly, D.-L. (2022) · Potchefstroom Electronic Law Journal · doi:10.17159/1727-3781/2022/v25i0a11118 · Cited by 8 · see citing works
- Authors
- Luxton, D. D.
- Affiliations
- University of Washington (US)
- Corresponding author
- Luxton, David D
- ORCID
- Luxton, David D
- Venue
- Bulletin of the World Health Organization
- Publisher
- WHO Press
- Published
- 2020-01-27
- Volume
- 98
- Issue
- 4
- Pages
- 285-287
- ISSN
- 0042-9686 (ISSN-L)
- PMC
- PMC7133471
- OpenAlex
- W3014634077
- Cited by
- 108
- References
- 8
- Citations by year
- 2026: 24 · 2025: 27 · 2024: 14 · 2023: 12 · 2022: 14 · 2021: 13 · 2020: 4
- FWCI
- 13.84
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Bulletin of the World Health Organization (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Keywords
- chatbots, AI ethics, global public health, health communication, digital health
- MeSH terms
- Digital Technology, Communication, Health Services Accessibility, Humans, Mental Health Services, Public Health, Global Health, Bias, Privacy
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