Patients’ Utilization and Perception of an Artificial Intelligence–Based Symptom Assessment and Advice Technology in a British Primary Care Waiting Room: Exploratory Pilot Study
Open accessPublished version of a preprint
Abstract
Background: When someone needs to know whether and when to seek medical attention, there are a range of options to consider. Each will have consequences for the individual (primarily considering trust, convenience, usefulness, and opportunity costs) and for the wider health system (affecting clinical throughput, cost, and system efficiency). Digital symptom assessment technologies that leverage artificial intelligence may help patients navigate to the right type of care with the correct degree of urgency. However, a recent review highlighted a gap in the literature on the real-world usability of these technologies. Objective: We sought to explore the usability, acceptability, and utility of one such symptom assessment technology, Ada, in a primary care setting. Methods: Patients with a new complaint attending a primary care clinic in South London were invited to use a custom version of the Ada symptom assessment mobile app. This exploratory pilot study was conducted between November 2017 and January 2018 in a practice with 20,000 registered patients. Participants were asked to complete an Ada self-assessment about their presenting complaint on a study smartphone, with assistance provided if required. Perceptions on the app and its utility were collected through a self-completed study questionnaire following completion of the Ada self-assessment. Results: Over a 3-month period, 523 patients participated. Most were female (n=325, 62.1%), mean age 39.79 years (SD 17.7 years), with a larger proportion (413/506, 81.6%) of working-age individuals (aged 15-64) than the general population (66.0%). Participants rated Ada’s ease of use highly, with most (511/522, 97.8%) reporting it was very or quite easy. Most would use Ada again (443/503, 88.1%) and agreed they would recommend it to a friend or relative (444/520, 85.3%). We identified a number of age-related trends among respondents, with a directional trend for more young respondents to report Ada had provided helpful advice (50/54, 93%, 18-24-year olds reported helpful) than older respondents (19/32, 59%, adults aged 70+ reported helpful). We found no sex differences on any of the usability questions fielded. While most respondents reported that using the symptom checker would not have made a difference in their care-seeking behavior (425/494, 86.0%), a sizable minority (63/494, 12.8%) reported they would have used lower-intensity care such as self-care, pharmacy, or delaying their appointment. The proportion was higher for patients aged 18-24 (11/50, 22%) than aged 70+ (0/28, 0%). Conclusions: In this exploratory pilot study, the digital symptom checker was rated as highly usable and acceptable by patients in a primary care setting. Further research is needed to confirm whether the app might appropriately direct patients to timely care, and understand how this might save resources for the health system. More work is also needed to ensure the benefits accrue equally to older age groups.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Participants rated the usability of ADA as high, which is in line with data from a previous study in primary care patients.
“Furthermore, the participants rated the usability of ADA as high, which is in line with data from a previous study in primary care patients [30].”
From Diagnostic Performance of an App-Based Symptom Checker in Mental Disorders: Comparative Study in Psychotherapy Outpatients (2022) · cites this paper as [30]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., A'Court, C., Hinder, S., Fahy, N., Procter, R., & Shaw, S. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.8775 · Cited by 2,887 · cited 1× · see citing works
“While there is much excitement about the potential for video consultations and the UK National Health Service (NHS) GP contract even states “every patient will have the right to online and video consultation by April 2021,” the accumulated experience has been that health IT solutions within the NHS tend to suffer “non…”
BackgroundThompson, A. E., Anisimowicz, Y., Miedema, B. B., Hogg, W. E., Wodchis, W. P., & Aubrey‐Bassler, K. (2016) · BMC Family Practice · doi:10.1186/s12875-016-0440-0 · Cited by 1,171 · cited 3× · see citing works
“Relative to 2011 UK Census data, and the practice’s own data for all registered patients, this represents a higher proportion of females, although females are known to use health care services more frequently [1].”
BackgroundInfluentialO’Brien, H. L., Cairns, P., & Hall, M. (2018) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2018.01.004 · Cited by 1,014 · cited 1× · see citing works
“Similarly, an independent study by an external academic group unrelated to Ada sought to understand the applicability of a multidimensional short form User Engagement Scale [27] in mobile health apps, using the Ada symptom checker as an example [28].”
BackgroundHobbs, R. F., Bankhead, C. R., Mukhtar, T. K., Stevens, S., Perera, R., Holt, T., & Salisbury, C. (2016) · The Lancet · doi:10.1016/s0140-6736(16)00620-6 · Cited by 609 · see citing works
Patients’ Satisfaction with and Preference for Telehealth Visits
Polinski, J. M., Barker, T., Gagliano, N., Sussman, A., Brennan, T. A., & Shrank, W. H. (2016) · Journal of General Internal Medicine · doi:10.1007/s11606-015-3489-x · Cited by 574 · cited 1× · see citing works
“More recently, internet-enabled options have emerged such as using a search engine to look up symptoms (Dr Google), high-quality online resources such as NHS Choices [4], symptom checkers [5,6], telehealth consultations by phone or videocall [7], Minute Clinics [8] that can be booked via smartphone, and peer-to-peer n…”
BackgroundEvaluation of symptom checkers for self diagnosis and triage: audit study
Semigran, H. L., Linder, J. A., Gidengil, C., & Mehrotra, A. (2015) · BMJ · doi:10.1136/bmj.h3480 · Cited by 489 · cited 4× · see citing works
“Some two-thirds of patients have searched their symptoms online before a doctor visit [10], with risks of inappropriate information and a lack of appropriate triage for urgent cases [5].”
BackgroundInfluentialA systematic review of triage-related interventions to improve patient flow in emergency departments
Oredsson, S., Jonsson, H., Rognes, J., Lind, L., Göransson, K. E., Ehrenberg, A., Asplund, K., Castrén, M. K., & Farrohknia, N. (2011) · Scandinavian Journal of Trauma Resuscitation and Emergency Medicine · doi:10.1186/1757-7241-19-43 · Cited by 305 · cited 2× · see citing works
“Accordingly, supply-orientated improvements to traditional processes such as diversion of nonurgent patients [13], nurse triage, fast-tracking [12], and telephone triage [14] seek to more optimally use professional resources.”
BackgroundAccess And Cost Barriers To Mental Health Care, By Insurance Status, 1999–2010
Rowan, K., McAlpine, D. D., & Blewett, L. A. (2013) · Health Affairs · doi:10.1377/hlthaff.2013.0133 · Cited by 277 · see citing works
Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., A’Court, C., Hinder, S., Procter, R., & Shaw, S. (2018) · BMC Medicine · doi:10.1186/s12916-018-1050-6 · Cited by 232 · cited 1× · see citing works
“While there is much excitement about the potential for video consultations and the UK National Health Service (NHS) GP contract even states “every patient will have the right to online and video consultation by April 2021,” the accumulated experience has been that health IT solutions within the NHS tend to suffer “non…”
BackgroundChambers, D., Cantrell, A. J., Johnson, M., Preston, L., Baxter, S. K., Booth, A., & Turner, J. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-027743 · Cited by 218 · cited 1× · see citing works
“More recently, internet-enabled options have emerged such as using a search engine to look up symptoms (Dr Google), high-quality online resources such as NHS Choices [4], symptom checkers [5,6], telehealth consultations by phone or videocall [7], Minute Clinics [8] that can be booked via smartphone, and peer-to-peer n…”
BackgroundGilbert, S., Mehl, A., Baluch, A., Cawley, C., Challiner, J., Fraser, H., Millen, E., Montazeri, M., Multmeier, J., Pick, F., et al. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-040269 · Cited by 183 · cited 1× · see citing works
“In a preprint from our group (currently undergoing peer review), we evaluated the performance of 8 popular symptom checkers against one another and 7 human GP raters, as well as a gold-standard diagnostic suggestion using 200 clinical vignettes [31].”
MethodSelf-citationSafety of patient-facing digital symptom checkers
Fraser, H., Coiera, E. W., & Wong, D. (2018) · The Lancet · doi:10.1016/s0140-6736(18)32819-8 · Cited by 182 · cited 2× · see citing works
“Another analysis from a large US population suggests a misdiagnosis rate by physicians of about 5% [22].”
BackgroundMeyer, A. N. D., Giardina, T. D., Spitzmueller, C., Shahid, U., Scott, T. M. T., & Singh, H. (2019) · Journal of Medical Internet Research · doi:10.2196/14679 · Cited by 162 · cited 2× · see citing works
“It remains to be seen how many patients would actually follow advice on where to go next, but in the survey of US Isabel symptom checker users, about half (14/26, 54%) of those advised to go to the emergency department reported that they did so [29].”
BackgroundVan Riel, N., Auwerx, K., Debbaut, P., Van Hees, S., & Schoenmakers, B. (2017) · BJGP Open · doi:10.3399/bjgpopen17x100833 · Cited by 159 · cited 1× · see citing works
“Some two-thirds of patients have searched their symptoms online before a doctor visit [10], with risks of inappropriate information and a lack of appropriate triage for urgent cases [5].”
BackgroundOpportunity costs of ambulatory medical care in the United States.
Ray, K. N., Chari, A. V., Engberg, J., Bertolet, M. H., & Mehrotra, A. (2015) · PubMed · Cited by 143 · see citing works
Blank, L., Coster, J. E., O’Cathain, A., Knowles, E., Tosh, J. C., Turner, J., & Nicholl, J. P. (2012) · Journal of Advanced Nursing · doi:10.1111/j.1365-2648.2012.06052.x · Cited by 108 · cited 1× · see citing works
“Accordingly, supply-orientated improvements to traditional processes such as diversion of nonurgent patients [13], nurse triage, fast-tracking [12], and telephone triage [14] seek to more optimally use professional resources.”
BackgroundAfter Dr Google: Peer-to-Peer Health Care
Fox, S. (2013) · PEDIATRICS · doi:10.1542/peds.2012-3786k · Cited by 75 · cited 1× · see citing works
“More recently, internet-enabled options have emerged such as using a search engine to look up symptoms (Dr Google), high-quality online resources such as NHS Choices [4], symptom checkers [5,6], telehealth consultations by phone or videocall [7], Minute Clinics [8] that can be booked via smartphone, and peer-to-peer n…”
BackgroundAssociation of Use of Online Symptom Checkers With Patients’ Plans for Seeking Care
Winn, A. N., Somai, M. M., Fergestrom, N. M., & Crotty, B. H. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.18561 · Cited by 67 · cited 1× · see citing works
“6% increased the urgency of their intended level of care [30].”
BackgroundKirkland, S. W., Soleimani, A., Rowe, B. H., & Newton, A. S. (2018) · Emergency Medicine Journal · doi:10.1136/emermed-2017-207045 · Cited by 60 · cited 2× · see citing works
“chronically under-resourced system, making relatively minor adjustments will yield relatively small results [13], and applying a broad approach to dissuading use of medical resources may have unintended negative consequences; most people cannot adequately distinguish between problems that are urgent, emergency, and ro…”
BackgroundApplicability of the User Engagement Scale to Mobile Health: A Survey-Based Quantitative Study
(2019) · doi:10.2196/13244 · Cited by 54 · cited 2× · see citing works
“4 years; range 18-73), they reported ratings were high for perceived usability and aesthetic appeal [28].”
BackgroundLee, S. (2018) · ACM SIGUCCS Annual Conference (SIGUCCS) · doi:10.1145/3235715.3235752 · Cited by 28 · see citing works
Henninger, S., Spencer, B., & Pasche, O. (2019) · European Journal of General Practice · doi:10.1080/13814788.2019.1634688 · Cited by 27 · see citing works
GP at hand: a digital revolution for health care provision?
Burki, T. K. (2019) · The Lancet · doi:10.1016/s0140-6736(19)31802-1 · Cited by 26 · cited 1× · see citing works
“More recently, internet-enabled options have emerged such as using a search engine to look up symptoms (Dr Google), high-quality online resources such as NHS Choices [4], symptom checkers [5,6], telehealth consultations by phone or videocall [7], Minute Clinics [8] that can be booked via smartphone, and peer-to-peer n…”
BackgroundElliot, A. J., Kara, E., Loveridge, P., Bawa, Z., Morbey, R., MOTH, M., Large, S., & Smith, G. (2015) · Epidemiology and Infection · doi:10.1017/s0950268815000503 · Cited by 23 · cited 2× · see citing works
“More recently, internet-enabled options have emerged such as using a search engine to look up symptoms (Dr Google), high-quality online resources such as NHS Choices [4], symptom checkers [5,6], telehealth consultations by phone or videocall [7], Minute Clinics [8] that can be booked via smartphone, and peer-to-peer n…”
BackgroundTurnbull, J. C., McKenna, G., Prichard, J. S., Rogers, A. E., Crouch, R., Lennon, A., & Pope, C. J. (2019) · Health Services and Delivery Research · doi:10.3310/hsdr07260 · Cited by 23 · see citing works
Chen, C., Chen, C. T., Hu, J., & Mehrotra, A. (2017) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011774.pub2 · Cited by 21 · see citing works
It’s a long shot, but it just might work! Perspectives on the future of medicine
Wicks, P. J., Hotopf, M. H., Narayan, V. A., Basch, E., Weatherall, J., & Gray, M. (2016) · BMC Medicine · doi:10.1186/s12916-016-0727-y · Cited by 15 · cited 1× · see citing works
“Patients seem ready to embrace such approaches given the preponderance of technology in their daily lives [19].”
BackgroundSelf-citationChen, C. T., Hu, J., Chen, C., & Mehrotra, A. (2015) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011774 · Cited by 8 · see citing works
Patients find GP online services “cumbersome,” survey finds
Torjesen, I. (2019) · BMJ · doi:10.1136/bmj.l4800 · Cited by 5 · cited 1× · see citing works
“This survey has been influential in UK health policy circles, receiving press attention and prompting responses from NHS England and NHSX, a UK government policy unit with responsibility for developing best practice and national policy for technology in health [21].”
BackgroundNational Cancer Institute (2020) · Definitions · doi:10.32388/8t6czz · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [23]Medical Research Council (MRC) Regulatory Support Centre Health Research Authority (HRA) Do I Need NHS REC Review? 2020-06-23 http://www.hra-decisiontools.org.uk/
Cited by
Sorted by citations
Young, A. T., Amara, D., Bhattacharya, A., & Wei, M. L. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00132-1 · Cited by 400 · see citing works
Artificial intelligence-based clinical decision support in pediatrics
Ramgopal, S., Sanchez-Pinto, L. N., Horvat, C. M., Carroll, M. S., Luo, Y., & Florin, T. A. (2022) · Pediatric Research · doi:10.1038/s41390-022-02226-1 · Cited by 187 · see citing works
Chenais, G., Lagarde, E., & Gil-Jardiné, C. (2023) · Journal of Medical Internet Research · doi:10.2196/40031 · Cited by 142 · see citing works
d’Elia, A., Gabbay, M., Rodgers, S., Kierans, C., Jones, E., Durrani, I., Thomas, A., & Frith, L. (2022) · Family Medicine and Community Health · doi:10.1136/fmch-2022-001670 · Cited by 129 · see citing works
Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review
Asan, O., & Choudhury, A. (2021) · JMIR Human Factors · doi:10.2196/28236 · Cited by 114 · see citing works
“Two recent studies measured the perceived usability of AI-based decision-making tools: Ada, an AI tool that helps patients navigate the right type of care [69], and PE-Dx CDS, a tool for diagnosing pulmonary embolism [66].”
BackgroundKhanijahani, A., Iezadi, S., Dudley, S., Goettler, M., Kroetsch, P., & Wise, J. (2022) · Health Policy and Technology · doi:10.1016/j.hlpt.2022.100602 · Cited by 106 · see citing works
Triage and Diagnostic Accuracy of Online Symptom Checkers: Systematic Review
Sasco, E. R., El-Osta, A. A. R., Alaa, A., Webber, I., Karki, M., Asmar, M. L. E., Purohit, K., Painter, A., & Hayhoe, B. W. J. (2023) · Journal of Medical Internet Research · doi:10.2196/43803 · Cited by 76 · see citing works
“Meanwhile, 2 other studies reported less encouraging results, with few users following the ED visit advice [8] or a majority of patients in a primary care waiting room not changing their decision to see the GP despite the OSC’s alternative advice to wait and self-care [35].”
Knitza, J., Mohn, J., Bergmann, C., Kampylafka, E. I., Hagen, M., Bohr, D., Morf, H., Araujo, E. G., Englbrecht, M., Simón, D., et al. (2021) · Arthritis Research & Therapy · doi:10.1186/s13075-021-02498-8 · Cited by 65 · see citing works
Beyond self-diagnosis: how a chatbot-based symptom checker should respond
You, Y., Tsai, C.-H., Li, Y., Ma, F., Heron, C., & Gui, X. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3589959 · Cited by 60 · see citing works
“As prior Human-Computer Interaction (HCI) studies have argued, healthcare systems should include human-like features [29, 108, 156] and improve their transparency and explainability [62, 101, 128, 141, 155], begging the question of how to tailor and improve CSC conversational design in consideration of these qualities.”
BackgroundAboueid, S., Meyer, S. B., Wallace, J. R., Mahajan, S., & Chaurasia, A. K. (2020) · JMIR Public Health and Surveillance · doi:10.2196/22637 · Cited by 62 · see citing works
“1%) would recommend the platform to a relative [16].”
BackgroundKang, E. Y.-N., Chen, D., & Chen, Y. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107529 · Cited by 59 · see citing works
Kerstan, S., Bienefeld, N., & Grote, G. (2024) · Risk Analysis · doi:10.1111/risa.14216 · Cited by 55 · see citing works
“For instance, younger people and people who identify as male typically have more positive perceptions of AI-based healthcare technologies and are more willing to engage with them (Miller et al., 2020; Spänig et al., 2019).”
BackgroundMartinengo, L., Jabir, A. I., Goh, W. W. T., Lo, N. Y. W., Ho, M. R., Kowatsch, T., Atun, R. A., Michie, S., & Car, L. T. (2022) · Journal of Medical Internet Research · doi:10.2196/39243 · Cited by 48 · see citing works
“…needs, such as providing timely information [5], supporting mental health disorder management [6,7], assisting with triage in clinical settings [8,9], supporting chronic disease self-management, or delivering lifestyle change interventions, such as physical activity [10] and dietary changes,…”
Yousefi, F., Dehnavieh, R., Laberge, M., Gagnon, M.-P., Ghaemi, M. M., Nadali, M., & Azizi, N. (2025) · BMC Primary Care · doi:10.1186/s12875-025-02785-2 · Cited by 48 · see citing works
“AI-driven AI-driven automation can also mitigate workload pressures in healthcare [28, 62, 67, 68, 72, 73, 77, 87, 89, 92, 101, 104, 106, 112, 118–120, 135, 145, 146, 148, 159], addressing one of the long-standing concerns regarding provider burnout and inefficiencies in PHC workflows.”
InfluentialEnam, M. A., Murmu, C., & Dixon, E. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2476710 · Cited by 53 · see citing works
“With the exception of this prior work (Shandilya & Fan, 2022), much of the past research in AI for older adults focused on building systems from the ground up with older adults as participants in user studies (Balsa et al., 2020; Li et al., 2023; Miller et al., 2020; Yu & Chattopadhyay, 2024).”
AI and Primary Care: Scoping Review
Katonai, G., Árvai, N., & Meskó, B. (2025) · Journal of Medical Internet Research · doi:10.2196/65950 · Cited by 48 · see citing works
“In a pilot conducted in a GP waiting room, most patients, especially younger users, found an AI-driven symptom checker helpful for initial self-assessment [89].”
Cotte, F., Mueller, T., Gilbert, S., Blümke, B., Multmeier, J., Hirsch, M. C., Wicks, P. J., Wolanski, J., Tutschkow, D., Schade‐Brittinger, C., et al. (2022) · JMIR mhealth and uhealth · doi:10.2196/32340 · Cited by 41 · see citing works
“Individual apps within this class differ in their intended purpose; for example, some can only be used for a narrow range of conditions, age groups, or health care settings [15-20].”
BackgroundSelf-citationMahlknecht, A., Engl, A., Piccoliori, G., & Wiedermann, C. J. (2023) · BMC Primary Care · doi:10.1186/s12875-023-02143-0 · Cited by 41 · see citing works
“…a certain significance (neither clearly positive nor negative results), for future studies the use of a 4-point answer scale like in other studies [32] could be considered to obtain more meaningful results, e.g. ‘very satisfied,’ ‘rather satisfied,’ ‘rather dissatisfied, ‘ ‘very dissatisfied.’”
ResultKnitza, J., Muehlensiepen, F., Ignatyev, Y., Fuchs, F., Mohn, J., Simón, D., Kleyer, A., Fagni, F., Boeltz, S., Morf, H., et al. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.844669 · Cited by 38 · see citing works
Parental Perceptions on Use of Artificial Intelligence in Pediatric Acute Care
Ramgopal, S., Heffernan, M. E., Bendelow, A. C., Davis, M. M., Carroll, M. S., Florin, T. A., Alpern, E. R., & Macy, M. L. (2022) · Academic Pediatrics · doi:10.1016/j.acap.2022.05.006 · Cited by 37 · see citing works
Determinants of Laypersons’ Trust in Medical Decision Aids: Randomized Controlled Trial
Kopka, M., Schmieding, M. L., Rieger, T., Roesler, E., Balzer, F., & Feufel, M. A. (2022) · JMIR Human Factors · doi:10.2196/35219 · Cited by 37 · see citing works
“However, both findings stand in contrast to those indicating a low behavioral trust in symptom checkers; for example, Meyer et al [15] reported that only a minority of those advised by a symptom checker to visit the emergency department followed this advice, and Miller et al [61] found that most patients presenting to…”
Scheder-Bieschin, J., Blümke, B., de Buijzer, E., Cotte, F., Echterdiek, F., Nacsa, J., Ondrésik, M., Ott, M., Paul, G., Schilling, T., et al. (2021) · JMIR Formative Research · doi:10.2196/28199 · Cited by 36 · see citing works
“A number of studies [9,13,14] have described a range of tools for self-assessment of urgency and triage in the general practice and acute primary care setting, but studies evaluating the handover of history and symptom information to HCPs have not been carried out with real patients.”
BackgroundSelf-citationHennemann, S., Kuhn, S., Witthöft, M., & Jungmann, S. M. (2022) · JMIR Mental Health · doi:10.2196/32832 · Cited by 31 · see citing works
“This particular app was selected for various reasons: (1) the diagnostic coverage is wide [35], including mental disorders, and ADA has shown acceptable diagnostic performance in this diagnostic spectrum recently [39]; (2) it is free of charge and widely available (>10 million users and 7 languages) for Android- and i…”
BackgroundResultDigital Health Solutions for Weight Loss and Obesity: A Narrative Review
Irvin, L., Madden, L. A., Marshall, P., & Vince, R. V. (2023) · Nutrients · doi:10.3390/nu15081858 · Cited by 34 · see citing works
“They also improve diagnosis and treatment by providing a faster and more personalised service, information on symptoms, and advice [17], thereby reducing pressure on primary care services and physicians [18].”
BackgroundChallenges in evaluating the accuracy of AI-containing digital triage systems: A systematic review
Ilicki, J. (2022) · PLoS ONE · doi:10.1371/journal.pone.0279636 · Cited by 33 · see citing works
Fraser, H., Cohan, G., Koehler, C., Anderson, J. S., Lawrence, A., Patena, J. V., Bacher, I., & Ranney, M. L. (2022) · JMIR mhealth and uhealth · doi:10.2196/38364 · Cited by 32 · see citing works
“The choice of Ada for this study was based on its widespread use in many countries, preliminary evidence of strong performance, and a willingness to collaborate with outside, independent research teams, as shown by the wide range of published evaluation studies [13,28,29].”
BackgroundMethodInfluentialOnline Symptom Checkers: Recommendations for a Vignette-Based Clinical Evaluation Standard
Painter, A., Hayhoe, B. W. J., Sasco, E. R., & El-Osta, A. A. R. (2022) · Journal of Medical Internet Research · doi:10.2196/37408 · Cited by 30 · see citing works
Characteristics of Users and Nonusers of Symptom Checkers in Germany: Cross-Sectional Survey Study
Kopka, M., Scatturin, L., Napierala, H., Fürstenau, D., Feufel, M. A., Balzer, F., & Schmieding, M. L. (2023) · Journal of Medical Internet Research · doi:10.2196/46231 · Cited by 26 · see citing works
“Users also seem more commonly to be female (estimates range from 62% to 85%), although gender has also been reported to not impact the willingness to use such tools [19,20,22].”
BackgroundResultEthical, legal, and social aspects of symptom checker applications: a scoping review
Müller, R., Klemmt, M., Ehni, H., Henking, T., Kuhnmünch, A., Preiser, C., Koch, R., & Ranisch, R. (2022) · Medicine Health Care and Philosophy · doi:10.1007/s11019-022-10114-y · Cited by 29 · see citing works
Pharmacists’ perceptions of artificial intelligence: A national survey
Gustafson, K. A., Rowe, C., Gavaza, P., Bernknopf, A. C., Nogid, A., Hoffman, A., Jones, E., Showman, L., Miller, V., Aziz, M. H. A., et al. (2024) · Journal of the American Pharmacists Association · doi:10.1016/j.japh.2024.102306 · Cited by 27 · see citing works
Frommeyer, T. C., Fursmidt, R. M., Gilbert, M. M., & Bett, E. S. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.831123 · Cited by 25 · see citing works
Knitza, J., Taşçılar, K., Fuchs, F., Mohn, J., Kühn, S., Bohr, D., Muehlensiepen, F., Bergmann, C., Labinsky, H., Morf, H., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/55542 · Cited by 24 · see citing works
“Ada’s diagnostic suggestions are driven by a Bayesian network that is continuously updated [20].”
Faqar-Uz-Zaman, S. F., Anantharajah, L., Baumartz, P., Sobotta, P., Filmann, N., Zmuc, D., Wagner, M., Detemble, C., Sliwinski, S., Marschall, U., et al. (2022) · Annals of Surgery · doi:10.1097/sla.0000000000005614 · Cited by 25 · see citing works
“8,9,19 – 23 Gilbert et al 10 recently evaluated the performance of eight digital symptom assessment apps, showing that Ada achieved a higher accuracy (71”
BackgroundPerformance of a new symptom checker in patient triage: Canadian cohort study
Chan, F., Lai, S., Pieterman, M., Richardson, L., Singh, A., Peters, J., Toy, A., Piccininni, C. R., Rouault, T., Wong, K., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0260696 · Cited by 23 · see citing works
Use of Artificial Intelligence in Clinical Neurology
Hillis, J. M., & Bizzo, B. C. (2022) · Seminars in Neurology · doi:10.1055/s-0041-1742180 · Cited by 23 · see citing works
Kujala, S., & Hörhammer, I. (2022) · Journal of Medical Internet Research · doi:10.2196/33505 · Cited by 21 · see citing works
“Many health organizations have started using symptom checkers to guide patients to the most appropriate course of action [2-5].”
Mixed methods assessment of the influence of demographics on medical advice of ChatGPT
Andreadis, K., Newman, D. R., Twan, C., Shunk, A., Mann, D. M., & Stevens, E. R. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae086 · Cited by 20 · see citing works
Montazeri, M., Multmeier, J., Novorol, C., Upadhyay, S., Wicks, P. J., & Gilbert, S. (2021) · JMIR Formative Research · doi:10.2196/26402 · Cited by 19 · see citing works
“8% (511/522) of patients found the symptom-taking system easy to use in the primary care waiting room [14].”
BackgroundSelf-citationArtificial Intelligence–Based Consumer Health Informatics Application: Scoping Review
Asan, O., Choi, E., & Wang, X. (2023) · Journal of Medical Internet Research · doi:10.2196/47260 · Cited by 21 · see citing works
The Medical Authority of AI: A Study of AI-enabled Consumer-Facing Health Technology
You, Y., Kou, Y., Ding, X., & Gui, X. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445657 · Cited by 18 · see citing works
“In healthcare, a stream of research has investigated the accuracy [57,111] as well as usability [84,87] of AISC apps, factors influencing the authority of medical professionals [47,59], and the effect of online social forums [39,133] and Internet searches [120] on medical authority.”
BackgroundGilbert, S., Fenech, M. E., Upadhyay, S., Wicks, P. J., & Novorol, C. (2021) · Australian Journal of Primary Health · doi:10.1071/py21032 · Cited by 17 · see citing works
Self-citationFrost, E. K., Bosward, R., Aquino, Y. S. J., Braunack‐Mayer, A., & Carter, S. M. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105417 · Cited by 16 · see citing works
Evaluating the Diagnostic Performance of Symptom Checkers: Clinical Vignette Study
Hammoud, M., Douglas, S., Darmach, M., Alawneh, S., Sanyal, S., & Kanbour, Y. (2024) · JMIR AI · doi:10.2196/46875 · Cited by 15 · see citing works
“Aside from studying the accuracy of symptom checkers, real patients can be involved in testing the usability of such tools (eg, by using a self-completed questionnaire after self-diagnosing with symptom checkers as in the study by Miller et al [55]).”
BackgroundMethodElectronic Discharge Communication Tools Used in Pediatric Emergency Departments: Systematic Review
Wozney, L., Curran, J., Archambault, P. M., Cassidy, C. E., Jabbour, M., Mackay, R., Newton, A. S., Plint, A. C., & Somerville, M. (2022) · JMIR Pediatrics and Parenting · doi:10.2196/36878 · Cited by 15 · see citing works
“Indeed, they are already being studied in ED communication for the adult population [78,79].”
BackgroundAlbrink, K., Joos, C., Schröder, D., Müller, F., Hummers, E., & Noack, E. M. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01928-0 · Cited by 14 · see citing works
“Usability of such tools have been demonstrated among some smaller cohorts [9], but necessary research findings especially among certain subgroups are still lacking.”
BackgroundMethodChang, L., Sun, C., Chen, T.-H., Tsai, D., Lin, H., & Liao, L. (2024) · Digital Health · doi:10.1177/20552076241277021 · Cited by 14 · see citing works
Digital health for allergen immunotherapy
Dramburg, S., Matricardi, P. M., Pfaar, O., & Klimek, L. (2022) · Allergologie select · doi:10.5414/alx02301e · Cited by 13 · see citing works
“As digitization is taking hold of many spheres of life, also healthcare professionals can benefit from digital support in the implementation of routine activities, especially with regard to repetitive actions and data-based decision support [15, 16].”
BackgroundKnitza, J., Hasanaj, R., Beyer, J., Ganzer, F., Slagman, A. C., Bolanaki, M., Napierala, H., Schmieding, M. L., Al-Zaher, N., Orlemann, T., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/56514 · Cited by 12 · see citing works
“The negative NPS indicates poor acceptance and contrasts the previously reported rate of 73% (440/600) [12] and 85% (444/520) [36] of patients who would recommend Ada.”
“That’s just Future Medicine” - a qualitative study on users’ experiences of symptom checker apps
Müller, R., Klemmt, M., Koch, R., Ehni, H., Henking, T., Langmann, E., Wiesing, U., & Ranisch, R. (2024) · BMC Medical Ethics · doi:10.1186/s12910-024-01011-5 · Cited by 13 · see citing works
“Further studies criticised the missing real-word data in these debates [e.g., 10; 12; 13].”
BackgroundMethodInfluentialYang, M., Yan, Y., Zhong, X., Liu, H., Jing, R., Zheng, Y., Cai, Z., Liu, Z., & Gong, K. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-12787-6 · Cited by 14 · see citing works
“On this basis, the ex-ante judgment of the consul-tation types can be made with the assistance of artificial intelligence [46–49].”
Leaders’ support for using online symptom checkers in Finnish primary care: Survey study
Laukka, E., Kujala, S., Gluschkoff, K., Kanste, O. I., Hörhammer, I., & Heponiemi, T. (2021) · Health Informatics Journal · doi:10.1177/14604582211052259 · Cited by 12 · see citing works
“According to another study, SCs have been widely used in response to the COVID-19 pandemic.”
BackgroundMethodWetzel, A.-J., Koch, R., Koch, N. N., Klemmt, M., Müller, R., Preiser, C., Rieger, M. A., Rösel, I., Ranisch, R., Ehni, H., et al. (2024) · Digital Health · doi:10.1177/20552076241231555 · Cited by 11 · see citing works
Smart Waiting Room: A Systematic Literature Review and a Proposal
Spoladore, D., Mondellini, M., Mahroo, A., Giglioli, I. A. C., Gaspari, S. D., Lernia, D. D., Riva, G., Bellini, E., Setola, N., & Sacco, M. (2024) · Electronics · doi:10.3390/electronics13020388 · Cited by 12 · see citing works
Characteristics of Acute Childhood Illness Apps for Parents: Environmental Scan
Benoit, J. R. A., Hartling, L., Chan, M. C., & Scott, S. D. (2021) · Journal of Medical Internet Research · doi:10.2196/29441 · Cited by 12 · see citing works
Lundberg, K., Qin, L., Aulin, C., van Spil, W. E., Maurits, M. P., & Knevel, R. (2023) · RMD Open · doi:10.1136/rmdopen-2022-002974 · Cited by 10 · see citing works
Pawelczyk, J., Kraus, M., Voigtlaender, S., Siebenlist, S., & Rupp, M. (2025) · HSS Journal® The Musculoskeletal Journal of Hospital for Special Surgery · doi:10.1177/15563316251341321 · Cited by 10 · see citing works
“Patients primarily use symptom checkers to understand symptom etiology, determine care necessity, and select appropriate healthcare levels, perceiving these tools to be highly usable and clinically beneficial [30,31].”
Faqar-Uz-Zaman, S. F., Filmann, N., Mahkovic, D., Wagner, M., Detemble, C., Kippke, U., Marschall, U., Anantharajah, L., Baumartz, P., Sobotta, P., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-041396 · Cited by 10 · see citing works
Development of a Social Network for People Without a Diagnosis (RarePairs): Evaluation Study
Kühnle, L., Mücke, U., Lechner, W. M., Klawonn, F., & Grigull, L. (2020) · Journal of Medical Internet Research · doi:10.2196/21849 · Cited by 10 · see citing works
“The display of the matches should contain a form of ranking, possibly showing how many questions were answered similarly or which questions had the biggest effect for the matching (that is how the diagnostic app Ada [34] does it.).”
Contrasting rule and machine learning based digital self triage systems in the USA
Naved, B. A., & Luo, Y. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01367-3 · Cited by 9 · see citing works
Technology-supported self-triage decision making
Kopka, M., Wang, S. M., Kunz, S., Schmid, C., & Feufel, M. A. (2025) · npj Health Systems · doi:10.1038/s44401-024-00008-x · Cited by 11 · see citing works
“This result aligns with previous research, which highlighted the usefulness of SAAs in encouraging users to treat their conditions at home (when appropriate) and reducing unnecessary visits to healthcare facilities 22,40,41 .”
Schmude, M., Salim, N., Azadzoy, H., Bane, M., Millen, E., O’Donnell, L., Bode, P., Türk, E., Vaidya, R., & Gilbert, S. (2022) · JMIR Research Protocols · doi:10.2196/34298 · Cited by 9 · see citing works
“…including by a study conducted in a British primary care waiting room that investigated the usability, acceptability, and utility of an SAA [24], a study conducted in a German emergency department that assessed urgent-care advice provided by an SAA [25], and another study in a German…”
Self-citationDonner, S., Knauer, P., Kienzle, A., Dinneen, J. D., Burger, J. A., Perka, C., & Donner, S. (2025) · Journal of Medical Internet Research · doi:10.2196/73717 · Cited by 8 · see citing works
“Miller et al [46], who studied patients’ use and perception of an AI-based symptom assessment and advice technology in a British primary care waiting room, identified several age-related trends among respondents, with a directional trend for more young respondents (18-to 24-year-olds) to report…”
User Experience of Symptom Checkers: A Systematic Review
You, Y., Ma, R., & Gui, X. (2022) · PubMed Central · doi:10.48550/arxiv.2208.09100 · Cited by 8 · see citing works
“%) measured whether users accepted SCs 18,28,29,36 .”
BackgroundMethodInfluentialFactors Influencing the Use of Online Symptom Checkers in the United Kingdom: Cross-Sectional Study
El-Osta, A. A. R., Sasco, E. R., Al-Ammouri, M., Altalib, S., Neves, A. L., Majeed, A., & Hayhoe, B. W. J. (2025) · JMIR Formative Research · doi:10.2196/65314 · Cited by 6 · see citing works
“Demographic factors, including age and gender, as well as perceptions such as perceived usability, effectiveness, reliability, accuracy, safety, and privacy, have been identified as influential factors from prior studies [6,16,19,20,23,24].”
Deng, Z., Tian, Z., Xue, J., & Gupta, S. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2272191 · Cited by 6 · see citing works
“Prior to consulting any doctor, patients generally conduct network search, seek recommendations from acquaintances, or call hospital helpdesks (Jiang et al. 2022; Miller et al. 2020; Unluturk 2012).”
Curiello, S., Iannuzzi, E., Meissner, D., & Nigro, C. (2025) · European Journal of Innovation Management · doi:10.1108/ejim-01-2024-0078 · Cited by 7 · see citing works
Liu, V., Koskela, T., & Kaila, M. (2023) · JMIR Research Protocols · doi:10.2196/41423 · Cited by 6 · see citing works
“However, users have mostly been satisfied with the electronic symptom checkers they use [10,15-18].”
Huang, X., Heng, Y., & Qiao, Y. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1264275 · Cited by 5 · see citing works
Perceptions of Artificial Intelligence-Assisted Care for Children With a Respiratory Complaint
Ramgopal, S., Kapes, J., Alpern, E. R., Carroll, M. S., Heffernan, M. E., Simon, N.-J. E., Florin, T. A., & Macy, M. L. (2023) · Hospital Pediatrics · doi:10.1542/hpeds.2022-007066 · Cited by 5 · see citing works
Koch, R., Steffen, M.-T., Wetzel, A.-J., Preiser, C., Klemmt, M., Ehni, H., Mueller, R., & Joos, S. (2025) · JMIR Formative Research · doi:10.2196/60647 · Cited by 5 · see citing works
“The findings of this study support the idea that, for the implementation of symptom checker apps in health care, transparency is crucial [7,12].”
Martinengo, L., Lo, N. Y. W., Goh, W. W. T., & Car, L. T. (2021) · JMIR Research Protocols · doi:10.2196/30166 · Cited by 5 · see citing works
A Collaborative Model for Integration of Artificial Intelligence in Primary Care
Dolgikh, S. (2021) · Journal of Human Earth and Future · doi:10.28991/hef-2021-02-04-07 · Cited by 5 · see citing works
Avey: An Accurate AI Algorithm for Self-DiagnosisPreprint
Hammoud, M., Douglas, S., Darmach, M., Alawneh, S., Sanyal, S., & Kanbour, Y. (2022) · medRxiv · doi:10.1101/2022.03.08.22272076 · Cited by 5 · see citing works
“[40] presented a real-world usability study of Ada over 523 participants (patients) in a South London primary care clinic over a period of 3 months.”
BackgroundCan clinical decision support systems be an asset in medical education? An experimental approach
Kafke, S. D., Kuhlmey, A., Schuster, J., Blüher, S., Czimmeck, C., Zoellick, J. C., & Grosse, P. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04568-8 · Cited by 4 · see citing works
“Ada ® then asks a series of follow-up questions through a reasoning engine which bases its questions, diagnoses, and recommendations on probability estimations and its medical knowledge database [34].”
BackgroundEuropean regulation of digital respiratory healthcare
Gilbert, S. (2023) · European Respiratory Society eBooks · doi:10.1183/2312508x.10000923 · Cited by 5 · see citing works
Self-citationOlickal, J. J., Chinnakali, P., Suryanarayana, B. S., Rajanarayanan, S., Vivekanandhan, T., Saya, G. K., Ganapathy, K., & Subrahmanyam, D. K. S. (2022) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2022.102694 · Cited by 4 · see citing works
Mielke, A. M., Ghanem, M. H., Back, D. A., Digitalisierung, A., Dgou, A. L., Fröhlich, S., & Seemann, R. J. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04213-4 · Cited by 4 · see citing works
Joos, C., Albrink, K., Hummers, E., Müller, F., Antweiler, K., Schröder, D., & Noack, E. M. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae102 · Cited by 3 · see citing works
“6,29 For example, Melms et al 30 evaluated patient satisfaction with an app used in the emergency department.”
BackgroundSchmude, M., Gilbert, S., Larsson, C. M., Gqada, N., Lemon, K., Kotze, L., Omar, R., January, E., Schlagenhauf, D., Azadzoy, H., et al. (2026) · Nature Health · doi:10.1038/s44360-026-00125-x · Cited by 3 · see citing works
“The DDSS design was informed by health-seeking behaviour theory and models 35,36 , behavioural design thinking, human factors and usability engineering studies, which focused on how the user journey affected ultimate patient health choices 37 .”
Self-citationPankow, A., Meißner-Bendzko, N., Kaufeld, J. K., Fouquette, L., Cotte, F., Gilbert, S., Türk, E., Das, A. M., Terkamp, C., Burmester, G.-R., et al. (2025) · JMIR AI · doi:10.2196/55001 · Cited by 3 · see citing works
“The approach of SCs often involves extracting medical knowledge from vast amounts of data, often obtained through comprehensive literature reviews [16].”
Self-citationMillen, E., Salim, N., Azadzoy, H., Bane, M., O’Donnell, L., Schmude, M., Bode, P., Türk, E., Vaidya, R., & Gilbert, S. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-055915 · Cited by 3 · see citing works
Self-citationGilbert, S., Fenech, M. T., Idris, A., & Türk, E. (2021) · Journal of Medical Internet Research · doi:10.2196/26514 · Cited by 3 · see citing works
“Our own internal validation testing shows an improvement in Ada's medical intelligence in all-condition top-3 suggestion accuracy (also known as M3, as defined by Miller et al [5]) of 4.8% between 2018 and 2020.”
MethodInfluentialSelf-citationKünstliche Intelligenz in der internistischen Versorgung
Eckstein, J. (2023) · Die Innere Medizin · doi:10.1007/s00108-023-01604-z · Cited by 2 · see citing works
Digital Allergology and Allergen-Immunotherapy in Germany
Klimek, L., Walter, U., Becker, S., Casper, I., Kianfar, R., Röseler, S., Schareina, A., Wrede, H., & Dramburg, S. (2025) · Health informatics · doi:10.1007/978-3-031-71021-6_20 · Cited by 2 · see citing works
Langmann, E., Henking, T., Joos, S., Klemmt, M., Mueller, R., Preiser, C., Ranisch, R., Koch, R., Rieger, M. A., Wetzel, A.-J., et al. (2025) · Ethik in der Medizin · doi:10.1007/s00481-025-00851-y · Cited by 3 · see citing works
“In weiteren Studien wurde zudem das Fehlen von „real-world data“ in den Debatten um SCAs kritisiert (Fraser et al. 2022; Miller et al. 2020; Meyer et al. 2020).”
Dr AI will see you now: could artificial intelligence replace general practitioners?
Cronin, T., & Travers, J. (2025) · Irish Journal of Medical Science (1971 -) · doi:10.1007/s11845-025-04006-2 · Cited by 1 · see citing works
“A previous questionnaire study of 523 patients using an AI-based symptom assessment app, known as ‘Ada’, reported that the tool was rated highly usable and acceptable to patients in a primary care setting [31].”
Primary Care and Care Coordination
Juzwishin, D., MacNeil, M., Meisen, A., & Stolee, P. (2025) · Synthesis lectures on technology and health · doi:10.1007/978-3-031-86885-6_7 · Cited by 2 · see citing works
Banerjee, A., Baibhav, P., Kumar, N., Singh, A. P., & Gouthaman, P. (2024) · International Conference on Sustainable Communication Networks and Application (ICSCNA) · doi:10.1109/icscna63714.2024.10864063 · Cited by 1 · see citing works
Masayoshi, K., Hashimoto, M., Toda, N., Mori, H., Kobayashi, G., Haque, H. A., So, M., & Jinzaki, M. (2025) · JMIR AI · doi:10.2196/68020 · Cited by 1 · see citing works
“For instance, the term “AI” encompasses a broad spectrum of technologies, from simple symptom checkers [36,37] to sophisticated LLMs [38,39].”
Painter, A., Hayhoe, B. W. J., Sasco, E. R., & El-Osta, A. A. R. (2022) · doi:10.2196/preprints.42481 · Cited by 1 · see citing works
El-Osta, A. A. R., Sasco, E. R., Al-Ammouri, M., Altalib, S., Neves, A. L., Majeed, A., & Hayhoe, B. W. J. (2024) · doi:10.2196/preprints.65314 · Cited by 1 · see citing works
Kumar, A., Kumar, D. V. S., Sinha, P., & Megha, R. (2024) · Research Square · doi:10.21203/rs.3.rs-4979499/v1 · Cited by 1 · see citing works
Fraser, H., Cohan, G., Koehler, C., Anderson, J. S., Lawrence, A., Patena, J. V., Bacher, I., & Ranney, M. L. (2022) · doi:10.2196/preprints.38364
Martinengo, L., Jabir, A. I., Goh, W. W. T., Lo, N. Y. W., Ho, M. R., Kowatsch, T., Atun, R. A., Michie, S., & Car, L. T. (2022) · doi:10.2196/preprints.39243
Mobile Sensors in Healthcare: Technical, Ethical, and Medical Aspects
Eckstein, J. (2022) · Future of business and finance · doi:10.1007/978-3-030-99838-7_3
Freyer, O., Bergey, F., Cotte, F., Gilbert, S., & Mehl, A. (2023) · doi:10.2196/preprints.52486
Self-citationPankow, A., Meißner-Bendzko, N., Kaufeld, J. K., Fouquette, L., Cotte, F., Gilbert, S., Türk, E., Das, A. M., Terkamp, C., Burmester, G.-R., et al. (2023) · doi:10.2196/preprints.55001
Self-citationHagemann, J., Becker, S., & Klimek, L. (2024) · Allergo Journal · doi:10.1007/s15007-024-6261-x
Yusuf, A. O., Afolabi, A. O., Akanmu, A. A., Murzi, H., Ofori-Boadu, A. N., & Ball, S. B. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2375689
“Using a questionnaire, Miller et al. (2020) showed that younger patients (18–24 years old) perceived a web-based platform for symptom assessment and advice more helpful than older patients (70 years and above).”
Artificial Intelligence and Primary Care: A Scoping Review (Preprint)Preprint
Katonai, G., Árvai, N., & Meskó, B. (2024) · doi:10.2196/preprints.65950
- Authors
- Miller, S., Gilbert, S., Virani, V., & Wicks, P.
- Corresponding author
- Gilbert, Stephen
- Venue
- JMIR Human Factors
- Publisher
- JMIR Publications Inc.
- Published
- 2020-07-10
- Volume
- 7
- Issue
- 3
- Pages
- e19713
- ISSN
- 2292-9495 (ISSN-L)
- PMC
- PMC7382011
- OpenAlex
- W3035307628
- Cited by
- 117
- References
- 30
- Citations by year
- 2026: 7 · 2025: 19 · 2024: 21 · 2023: 23 · 2022: 30 · 2021: 15 · 2020: 2
- FWCI
- 5.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Human Factors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Ada, symptom assessment, artificial intelligence, mobile health apps, usability, acceptability, patient perspectives, self-assessment, age differences, sex differences
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
