Evidence assessing the diagnostic performance of medical smartphone apps: a systematic review and exploratory meta-analysis
Open accessAbstract
Objective: The number of mobile applications addressing health topics is increasing. Whether these apps underwent scientific evaluation is unclear. We comprehensively assessed papers investigating the diagnostic value of available diagnostic health applications using inbuilt smartphone sensors. Methods: Systematic Review—MEDLINE, Scopus, Web of Science inclusive Medical Informatics and Business Source Premier (by citation of reference) were searched from inception until 15 December 2016. Checking of reference lists of review articles and of included articles complemented electronic searches. We included all studies investigating a health application that used inbuilt sensors of a smartphone for diagnosis of disease. The methodological quality of 11 studies used in an exploratory meta-analysis was assessed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the reporting quality with the ’STAndards for the Reporting of Diagnostic accuracy studies' (STARD) statement. Sensitivity and specificity of studies reporting two-by-two tables were calculated and summarised. Results: We screened 3296 references for eligibility. Eleven studies, most of them assessing melanoma screening apps, reported 17 two-by-two tables. Quality assessment revealed high risk of bias in all studies. Included papers studied 1048 subjects (758 with the target conditions and 290 healthy volunteers). Overall, the summary estimate for sensitivity was 0.82 (95 % CI 0.56 to 0.94) and 0.89 (95 %CI 0.70 to 0.97) for specificity. Conclusions: The diagnostic evidence of available health apps on Apple’s and Google’s app stores is scarce. Consumers and healthcare professionals should be aware of this when using or recommending them. PROSPERO registration number: 42016033049.
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?
A 2017 systematic review identified 30 manuscripts investigating 35 apps most commonly intended to screen photographs for melanoma or diagnose tremor through analyses of movements.
“A 2017 systematic review identified 30 manuscripts investigating 35 apps most commonly intended to screen photographs for melanoma or diagnose tremor through analyses of movements.”
From What is the clinical value of mHealth for patients? (2020) · cites this paper as [5]
A previous systematic review with a search date of December 2016 identified six studies that reported on diagnosis of melanoma, but accuracy seems to have been overestimated because it included findings from both development and validation studies.
“A previous systematic review with a search date of December 2016 examined the accuracy of mobile health apps for multiple conditions. This review identified six studies that reported on the diagnosis of melanoma, but accuracy seems to have been overestimated because it included findings from both development and validation studies.9”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [9]
The review excluded between three and five studies that reported the development (without independent validation) of new apps that were included in the other two reviews because such studies are likely to overestimate accuracy.
“We also excluded between three42 and five9 studies that reported the development (without independent validation) of new apps that were included in the other two reviews because such studies are likely to overestimate accuracy.”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [9]
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
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823 · see citing works
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
(2009) · PubMed Central · Cited by 44,859 · see citing works
QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies
Whiting, P. F., Rutjes, A. W. S., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M. M. G., Sterne, J. A. C., Bossuyt, P. M. M., & the QUADAS-2 Group* (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-8-201110180-00009 · Cited by 14,199 · see citing works
Cillà, S. D., Farruggio, S., Vujosevic, S., Raina, G., Filippini, D. A., Gatti, V., Clemente, N., Mary, D., Vezzola, D., Casini, G., et al. (2017) · Cellular Physiology and Biochemistry · doi:10.1159/000479441 · Cited by 5,153 · see citing works
Towards Complete and Accurate Reporting of Studies of Diagnostic Accuracy: The STARD Initiative
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P., Irwig, L., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., et al. (2003) · Clinical Biochemistry · doi:10.1016/s0009-9120(02)00443-5 · Cited by 3,182 · see citing works
The Efficacy of Diagnostic Imaging
Fryback, D. G., & Thornbury, J. R. (1991) · Medical Decision Making · doi:10.1177/0272989x9101100203 · Cited by 1,294 · see citing works
Smartphones for smarter delivery of mental health programs: A systematic review
Donker, T., Petrie, K., Proudfoot, J., Clarke, J., Birch, M.-R., & Christensen, H. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2791 · Cited by 1,136 · see citing works
Mental Health Smartphone Apps: Review and Evidence-Based Recommendations for Future Developments
Bakker, D., Kazantzis, N., Rickwood, D., & Rickard, N. (2016) · JMIR Mental Health · doi:10.2196/mental.4984 · Cited by 886 · see citing works
QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies
Whiting, P. F., Rutjes, A. W., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M., Sterne, J. A. C., & Bossuyt, P. M. (2011) · Data Archiving and Networked Services (DANS) · doi:10.1059/0003-4819-155-8-201110180-00009 · Cited by 601 · see citing works
Mobile Health Apps to Facilitate Self-Care: A Qualitative Study of User Experiences
Anderson, K., Burford, O., & Emmerton, L. (2016) · PLOS ONE · doi:10.1371/journal.pone.0156164 · Cited by 502 · see citing works
Diagnostic Inaccuracy of Smartphone Applications for Melanoma Detection
Wolf, J. A., Moreau, J. F., Akilov, O., Patton, T., English, J. C., Ho, J., & Ferris, L. K. (2013) · JAMA Dermatology · doi:10.1001/jamadermatol.2013.2382 · Cited by 393 · see citing works
Detecting and monitoring the symptoms of Parkinson's disease using smartphones: A pilot study
Arora, S., Venkataraman, V., Zhan, A., Donohue, S., Biglan, K. M., Dorsey, E. R., & Little, M. A. (2015) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2015.02.026 · Cited by 387 · see citing works
McManus, D. D., Lee, J., Maitas, O., Esa, N., Pidikiti, R., Carlucci, A., Harrington, J., Mick, E., & Chon, K. H. (2013) · Heart Rhythm · doi:10.1016/j.hrthm.2012.12.001 · Cited by 253 · see citing works
Atrial Fibrillation Detection Using an iPhone 4S
Lee, J., Reyes, B. A., McManus, D. D., Maitas, O. L., & Chon, K. H. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2208112 · Cited by 238 · see citing works
Harbord, R. M., Whiting, P. F., Sterne, J. A. C., Egger, M., Deeks, J. J., Shang, A., & Bachmann, L. M. (2008) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2007.09.013 · Cited by 215 · see citing works
Self-citationIdentifying Diagnostic Studies in MEDLINE: Reducing the Number Needed to Read
Bachmann, L. M. (2002) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1124 · Cited by 173 · see citing works
Self-citationA Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease
Ellis, R. J., Ng, Y. S., Zhu, S., Tan, D. M. L., Anderson, B., Gottfried Schlaug, A., & Wang, Y. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141694 · Cited by 152 · see citing works
A Smartphone-Based Tool for Assessing Parkinsonian Hand Tremor
Kostikis, N., Hristu-Varsakelis, D., Arnaoutoglou, M., & Kotsavasiloglou, C. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2471093 · Cited by 139 · see citing works
A Mobile Automated Skin Lesion Classification System
Ramlakhan, K., & Shang, Y. (2011) · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · doi:10.1109/ictai.2011.29 · Cited by 124 · see citing works
Maier, T., Kulichova, D., Schotten, K., Astrid, R., Ruzicka, T., Berking, C., & Udrea, A. (2015) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.12648 · Cited by 103 · see citing works
Objective assessment of abnormal gait in patients with rheumatoid arthritis using a smartphone
Yamada, M., Aoyama, T., Mori, S., Nishiguchi, S., Okamoto, K., Ito, T., Muto, S., Ishihara, T., Yoshitomi, H., & Ito, H. (2011) · Rheumatology International · doi:10.1007/s00296-011-2283-2 · Cited by 91 · see citing works
Lutein Leads to a Decrease of Factor D Secretion by Cultured Mature Human Adipocytes
Tian, Y., Kijlstra, A., Renes, J., Wabitsch, M., Webers, C. A. B., & Berendschot, T. T. J. M. (2015) · Journal of Ophthalmology · doi:10.1155/2015/430741 · Cited by 90 · see citing works
Wang, Y., He, Y.-G., Mitzel, G. M., Zhang, S., & Bartlett, M. (2013) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.13-12037 · Cited by 87 · see citing works
Using a smart phone as a standalone platform for detection and monitoring of pathological tremors
Daneault, J.-F., Carignan, B., Codère, C. É., Sadikot, A. F., & Duval, C. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00357 · Cited by 85 · see citing works
Arora, S., Venkataraman, V., Donohue, S., Biglan, K., Dorsey, E. R., & Little, M. A. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854280 · Cited by 85 · see citing works
Implementation of the 7-point checklist for melanoma detection on smart handheld devices
Wadhawan, T., Situ, N., Rui, H., Lancaster, K., Yuan, X., & Zouridakis, G. (2011) · 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2011.6090866 · Cited by 72 · see citing works
Emerging Therapies for Neovascular Age-Related Macular Degeneration
Kaiser, P. K. (2013) · Ophthalmology · doi:10.1016/j.ophtha.2013.01.061 · Cited by 69 · see citing works
Parkinson’s disease and essential tremor classification on mobile device
Woods, A. M., Nowostawski, M., Franz, E. A., & Purvis, M. (2014) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2013.10.002 · Cited by 69 · see citing works
Isho, T., Tashiro, H., & Usuda, S. (2015) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2015.02.004 · Cited by 62 · see citing works
Automated skin lesion assessment using mobile technologies and cloud platforms
Doukas, C. N., Stagkopoulos, P., Kiranoudis, C. T., & Maglogiannis, I. G. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346458 · Cited by 56 · see citing works
Evaluating more naturalistic outcome measures
Bove, R. M., White, C. C., Giovannoni, G., Glanz, B. I., Golubchikov, V. A., Hujol, J., Jennings, C. G. B., Langdon, D. W., Lee, M. H., Legedza, A., et al. (2015) · Neurology Neuroimmunology & Neuroinflammation · doi:10.1212/nxi.0000000000000162 · Cited by 56 · see citing works
Raknim, P., & Lan, K.-C. (2015) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2015.0005 · Cited by 49 · see citing works
Smartphone application for classification of motor impairment severity in Parkinson's disease
Printy, B. P., Renken, L. M., Herrmann, J. P., Lee, I., Johnson, B., Knight, E. J., Varga, G., & Whitmer, D. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944176 · Cited by 49 · see citing works
SkinScan©: A portable library for melanoma detection on handheld devices
Wadhawan, T., Situ, N., Lancaster, K., Yuan, X., & Zouridakis, G. (2011) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2011.5872372 · Cited by 48 · see citing works
Kostikis, N., Hristu-Varsakelis, D., Arnaoutoglou, M., & Kotsavasiloglou, C. (2014) · 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc.2014.6943738 · Cited by 46 · see citing works
Self-Assessment Tool of Disease Activity of Rheumatoid Arthritis by Using a Smartphone Application
Nishiguchi, S., Ito, H., Yamada, M., Yoshitomi, H., Furu, M., Ito, T., Shinohara, A., Ura, T., Okamoto, K., & Aoyama, T. (2014) · Telemedicine and e-Health · doi:10.1089/tmj.2013.0162 · Cited by 45 · see citing works
Early melanoma diagnosis with mobile imaging
Do, T.-T., Zhou, Y., Zheng, H., Cheung, N. M., & Koh, D. C. I. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6945178 · Cited by 40 · see citing works
Mobile cataract detection using optimal combination of statistical texture analysis
Fuadah, Y. N., Setiawan, A. W., Mengko, T. L. R., & Budiman, B. (2015) · International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering · doi:10.1109/icici-bme.2015.7401368 · Cited by 35 · see citing works
Using the smartphone camera to monitor heart rate and rhythm in heart failure patients
Lagido, R., Lobo, J., Leite, S., Sousa, C., Ferreira, L., & Silva‐Cardoso, J. (2014) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2014.6864425 · Cited by 22 · see citing works
Winther, C., & Frisén, L. (2015) · Journal of Ophthalmology · doi:10.1155/2015/285463 · Cited by 18 · see citing works
Mobile Medical Applications for Melanoma Risk Assessment: False Assurance or Valuable Tool?
Chadwick, X., Loescher, L. J., Janda, M., & Soyer, H. P. (2014) · 2014 47th Hawaii International Conference on System Sciences · doi:10.1109/hicss.2014.337 · Cited by 17 · see citing works
What Makes Hyper-local Online Discussion Forums Sustainable?
López, C., Lin, Y., & Farzan, R. (2015) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2015.292 · Cited by 15 · see citing works
Validating an iOS-based Rhythmic Auditory Cueing Evaluation (iRACE) for Parkinson's Disease
Zhu, S., Ellis, R. J., Gottfried Schlaug, A., Ng, Y. S., & Wang, Y. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654952 · Cited by 15 · see citing works
Development of lifelog sharing system for rheumatoid arthritis patients using smartphone
Shinohara, A., Ito, T., Ura, T., Nishiguchi, S., Ito, H., Yamada, M., Yoshitomi, H., Furu, M., Okamoto, K., & Aoyama, T. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6611235 · Cited by 14 · see citing works
Majeed-Ariss, R., Hall, A., McDonagh, J., Fallon, D., & Swallow, V. (2015) · JMIR Research Protocols · doi:10.2196/resprot.4159 · Cited by 2 · see citing works
Flagged first, then by citations
3 of the 48 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [4]NCBI. Mobile applications. 2017 https://www.ncbi.nlm.nih.gov/mesh/?term=mobile+application
Cited by
Sorted by citations
What is the clinical value of mHealth for patients?
Rowland, S. P., Fitzgerald, J. E., Holme, T., Powell, J., & McGregor, A. (2020) · npj Digital Medicine · doi:10.1038/s41746-019-0206-x · Cited by 454 · see citing works
Fujisawa, Y., Otomo, Y., Ogata, Y., Nakamura, Y., Fujita, R., Ishitsuka, Y., Watanabe, R., Okiyama, N., Ohara, K., & Fujimoto, M. (2018) · British Journal of Dermatology · doi:10.1111/bjd.16924 · Cited by 316 · see citing works
Jinnai, S., Yamazaki, N., Hirano, Y., Sugawara, Y., Ohe, Y., & Hamamoto, R. (2020) · Biomolecules · doi:10.3390/biom10081123 · Cited by 276 · see citing works
Freeman, K., Dinnes, J., Chuchu, N., Takwoingi, Y., Bayliss, S. E., Matin, R. N., Jain, A., Walter, F. M., Williams, H. C., & Deeks, J. J. (2020) · BMJ · doi:10.1136/bmj.m127 · Cited by 260 · see citing works
Smartphone use in ophthalmology: What is their place in clinical practice?
Hogarty, D. T., Hogarty, J. P., & Hewitt, A. W. (2019) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2019.09.001 · Cited by 67 · see citing works
Mobile applications in oncology: A systematic review of health science databases
Ayora, A. F., Loreto, M.-S., Montesinos, M. J. L., Salas‐Medina, P., Julio, M. P. M., & Llor, A. M. S. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.104001 · Cited by 64 · see citing works
Alamoodi, A., Garfan, S., Zaidan, B. B., Zaidan, A., Shuwandy, M. L., Alaa, M., Alsalem, M., Mohammed, A., Aleesa, A. M., Albahri, O., et al. (2020) · Health technology · doi:10.1007/s12553-020-00451-4 · Cited by 51 · see citing works
Nicolaidou, I. A., Aristeidis, L., & Lambrinos, L. (2022) · Digital Health · doi:10.1177/20552076221109059 · Cited by 43 · see citing works
Improving the early diagnosis of early nodular melanoma: can we do better?
Corneli, P., Zalaudek, I., Rizzi, G. M., & di Meo, N. (2018) · Expert Review of Anticancer Therapy · doi:10.1080/14737140.2018.1507822 · Cited by 36 · see citing works
Smartphone applications validated for joint angle measurement: a systematic review
Longoni, L., Brunati, R., Sale, P., Casale, R., Ronconi, G., & Ferriero, G. (2019) · International Journal of Rehabilitation Research · doi:10.1097/mrr.0000000000000332 · Cited by 35 · see citing works
Zaar, O., Larson, A., Polesie, S., Saleh, K., Tarstedt, M., Olives, A., Suárez, A. L., Gillstedt, M., & Neittaanmäki, N. (2020) · Acta Dermato Venereologica · doi:10.2340/00015555-3624 · Cited by 30 · see citing works
Usefulness of Smartphones in Dermatology: A US-Based Review
Ouellette, S., & Rao, B. K. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19063553 · Cited by 32 · see citing works
Fan, J., Gu, F., Lv, L., Zhang, Z., Zhu, C., Qi, J., Wang, H., Liu, X., Yang, J., & Zhu, Q. (2022) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-022-05826-4 · Cited by 30 · see citing works
Barnett, S., Huckvale, K., Christensen, H. M., Venkatesh, S., Mouzakis, K., & Vasa, R. (2019) · Journal of Medical Internet Research · doi:10.2196/16399 · Cited by 28 · see citing works
Home monitoring as a useful extension of modern tele-ophthalmology
Faes, L., Bachmann, L. M., & Sim, D. A. (2020) · Eye · doi:10.1038/s41433-020-0964-3 · Cited by 27 · see citing works
Self-citationThe validation of smartphone applications for heart rate measurement
Pipitprapat, W., Harnchoowong, S., Suchonwanit, P., & Sriphrapradang, C. (2018) · Annals of Medicine · doi:10.1080/07853890.2018.1531144 · Cited by 24 · see citing works
Mercurio, M., Larsen, M. E., Wisniewski, H., Henson, P., Lagan, S., & Torous, J. B. (2020) · Evidence-Based Mental Health · doi:10.1136/ebmental-2019-300137 · Cited by 23 · see citing works
Nogueira-Leite, D., Diniz, J. M., & Cruz-Correia, R. J. (2023) · JMIR Human Factors · doi:10.2196/45949 · Cited by 21 · see citing works
Cazzaniga, S., Castelli, E., Landro, A. D., Mercurio, M. D., Imberti, G., Locatelli, G. A., Raponi, F., Vezzoli, P., Gambini, D. M., Damiani, G. B., et al. (2019) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2019.02.036 · Cited by 17 · see citing works
Bondaronek, P., Slee, A. E., Hamilton, F. L., & Murray, E. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-027536 · Cited by 16 · see citing works
Chung, Y., Sande, A. A., de Roos, K.-P., Bekkenk, M. W., de Haas, E. R. M., Kelleners-Smeets, N. W. J., & Kukutsch, N. A. (2019) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.15873 · Cited by 14 · see citing works
Person-Generated Health Data in Women’s Health: Protocol for a Scoping Review
Karim, J. L., & Talhouk, A. (2021) · JMIR Research Protocols · doi:10.2196/26110 · Cited by 14 · see citing works
Jukić, T., Ihan, A., Strojnik, V., Štubljar, D., & Starc, A. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01347-z · Cited by 12 · see citing works
Nogueira-Leite, D., & Cruz-Correia, R. J. (2023) · JMIR Research Protocols · doi:10.2196/41040 · Cited by 10 · see citing works
Hyzy, M., Bond, R., Mulvenna, M. D., Bai, L., Dix, A. J., Daly, R., Frey, A.-L., & Leigh, S. (2023) · JMIR mhealth and uhealth · doi:10.2196/47043 · Cited by 11 · see citing works
Lamotkin, A. I., Иванович, К. Д., Olisova, O. Y., & Анатольевич, Л. И. (2025) · FARMAKOEKONOMIKA Modern Pharmacoeconomics and Pharmacoepidemiology · doi:10.17749/2070-4909/farmakoekonomika.2025.294 · Cited by 11 · see citing works
Helweg-Jørgensen, S., Lichtenstein, M. B., Fruzzetti, A. E., Dahl, C. M., & Pedersen, S. S. (2020) · JMIR Research Protocols · doi:10.2196/17737 · Cited by 11 · see citing works
Lowe, C., Sing, H. H., Browne, M., Alwashmi, M. F., Marsh, W., & Morrissey, D. (2021) · JMIR Research Protocols · doi:10.2196/27205 · Cited by 8 · see citing works
Jazayeri, S. M. H. M., & Jamshidnezhad, A. (2019) · Malaysian Journal of Medical Sciences · doi:10.21315/mjms2019.26.1.2 · Cited by 8 · see citing works
Artificial Intelligence in Skin Cancer: Diagnosis and Therapy
Das, T., Kumar, V., Prakash, A., & Lynn, A. M. (2021) · Springer eBooks · doi:10.1007/978-981-16-0364-8_9 · Cited by 7 · see citing works
Abdelaziz, A., & Mahmoud, A. N. (2022) · Fusion Practice and Applications · doi:10.54216/fpa.080201 · Cited by 6 · see citing works
Morley, J., Laitila, J., Ross, J. S., Schamroth, J., Zhang, J., & Floridi, L. (2025) · Minds and Machines · doi:10.1007/s11023-025-09710-7 · Cited by 5 · see citing works
Applications of smartphones in analysis: Challenges and solutions
de Jesus, J. R., Ferrão, M. F., da Costa, A. B., Helfer, G. A., & Arruda, M. A. Z. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823696-3.00009-x · Cited by 4 · see citing works
Lowe, C., Sing, H. H., Marsh, W., & Morrissey, D. (2021) · JMIR Research Protocols · doi:10.2196/31541 · Cited by 3 · see citing works
Apps y Medicina: una visión global y la situación chilena
Tala, Á., Vásquez, E., Rojas, E., & Marín, R. (2022) · Revista médica de Chile · doi:10.4067/s0034-98872022000200206 · Cited by 3 · see citing works
Optimización de la comunicación hospitalaria en redes sociales: Análisis y estrategias de engagement
Pacanowski, T. G., Aguerrebere, P. M., Medina, E., & López-Molina, P. (2021) · Hipertext net Revista Académica sobre Documentación Digital y Comunicación Interactiva · doi:10.31009/hipertext.net.2021.i22.06 · Cited by 3 · see citing works
Smartphone Applications in Dermatology With Special Focus on AI : A European‐Continent‐Based Review
Sangers, T. E., Shifai, N., de Pasqualini, D., Barata, C., Braun, R. P., Brinker, T. J., Cartocci, A., Esdaile, B. A., Goldinger, S. M., Guitera, P., et al. (2026) · International Journal of Dermatology · doi:10.1111/ijd.70582 · Cited by 2 · see citing works
Boillat, T., Tuffnell, C., Rivas, H., Aloul, F., & Montana-Hoyos, C. A. (2020) · Advances in Science and Engineering Technology International Conferences (ASET) · doi:10.1109/aset48392.2020.9118202 · Cited by 2 · see citing works
Reliability of Measuring Leg Segments and Joint Angles Using Smartphones during Aquatic Exercise
Lee, D. H., & Han, S. (2022) · Healthcare Informatics Research · doi:10.4258/hir.2022.28.1.95 · Cited by 2 · see citing works
An Introduction to Digital Health: Current and Future Trends
Rivas, H., & Boillat, T. (2023) · Health informatics · doi:10.1007/978-3-031-17666-1_1 · Cited by 2 · see citing works
Brenes-Chacón, H., Silva, F. M., Aladab, A., Vieira, A., Paulo, I. D. C. D. E. D. S., Ordek, H., Minetto, J., Al-Busaid, S., Rey, A. M., Catalan, C., et al. (2023) · Principles and Practice of Clinical Research Journal · doi:10.21801/ppcrj.2023.93.4 · Cited by 2 · see citing works
Diagnosis of skin melanoma using artificial intelligence using the computer vision program
Lamotkin, A. I., Иванович, К. Д., & Анатольевич, Л. И. (2025) · Clinical Medicine (Russian Journal) · doi:10.30629/0023-2149-2025-103-7-540-545 · Cited by 1 · see citing works
Wearable Gait Analysis – stepping towards the mainstream
Morrow, P., Zheng, H., McCalmont, G., Haiying, Wang, H., & McClean, S. I. (2020) · Ulster University Research Portal (Ulster University) · Cited by 1 · see citing works
Barriers to Adoptions of IoT-Based Solutions for Disease Screening
Hiwale, S. S., Ulman, S., & Subbaraman, K. (2018) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/978-1-5225-7131-5.ch002 · Cited by 1 · see citing works
Zhu, Q., Fan, J., Gu, F., Lv, L., Zhang, Z., Zhu, C., Qi, J. H., Wang, H., Liu, X., & Yang, J. (2022) · Research Square · doi:10.21203/rs.3.rs-1117513/v1 · Cited by 1 · see citing works
Soeiro, A. D. M., Leal, T. D. C. A. T., Bossa, A. S., Soeiro, M. C. F. D. A., Junior, C. V. S., & de Oliveira, M. T. (2018) · Revista da Associação Médica Brasileira · doi:10.1590/1806-9282.64.08.658
Jamshidnezhad, A., Koohmareh, Z., & Pazahr, A. (2020) · Journal of Contemporary Medical Sciences · doi:10.22317/jcms.v6i1.583
Mobile Applications for Chronic Pain Management
Gupta, B., & Ahluwalia, P. (2021) · Indian Journal of Pain · doi:10.4103/ijpn.ijpn_148_20
Biofotônica e seu uso terapêutico: uma revisão narrativa
Pereira, S. M., dos Reis, E. B. B., Santos, P. C. F., Sulino, L., & Fogolim, M. V. (2024) · OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA · doi:10.55905/oelv22n4-077
Comparing Smartphone BPM Apps and Vital Signs Monitors for Pulse Measurement Accuracy
Wibawa, A. D., Sanjaya, N. S., Nur, R. R. A., Suryani, S. D., Hisam, A. D. F., Silaban, K. M. S., Naibaho, R. J., & Rahmawati, L. (2025) · Engineering headway · doi:10.4028/p-npss2m
- Authors
- Buechi, R., Faes, L., Bachmann, L. M., Thiel, M. A., Bodmer, N. S., Schmid, M. K., Job, O., & Lienhard, K. R.
- Affiliations
- Luzerner Kantonsspital (CH)
- Medignition (Switzerland) (CH)
- University of Lausanne (CH)
- Corresponding author
- Bachmann, Lucas M
- Venue
- BMJ Open
- Publisher
- BMJ
- Published
- 2017-12
- Volume
- 7
- Issue
- 12
- Pages
- e018280
- ISSN
- 2044-6055 (ISSN-L)
- PMC
- PMC5735404
- OpenAlex
- W2771419433
- Cited by
- 50
- References
- 48
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 1 · 2023: 5 · 2022: 7 · 2021: 7 · 2020: 12 · 2019: 8 · 2018: 5
- FWCI
- 1.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- BMJ Open (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- medignition Inc.
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- mobile health apps, smartphone sensors, diagnostic accuracy, melanoma screening, sensitivity and specificity, health technology assessment, risk of bias
- MeSH terms
- Smartphone, Humans, Diagnostic Equipment, Mobile Applications
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
