Challenges and Potential of Using Digital Biomarkers in Healthcare and Clinical Trials
Open accessAbstract
Digital biomarkers use sensors and analytics, to offer continuous monitoring and personalized medicine. In this Perspective, we describe real-world use cases, such as glucose tracking to optimise insulin dosing and wearables to measure heart rhythms to de-risk cardiovascular trials. We also discuss the issues preventing most candidate biomarkers from reaching clinical practice. Evidence generation is costly, regulatory reviews can be redundant, commercial incentives fall short, and data silos can be biased and/or nonrepresentative. By combining harmonized qualification pathways, value-based reimbursement, modular extensions for single-trial biomarkers, and adaptive post-market evidence loops, we propose a path from experimental signal to standard of care.
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?
Digital-biomarker frameworks emphasize analytical validity, clinical validity, reproducibility, and usefulness within a clearly defined clinical context.
“More broadly, digital-biomarker frameworks emphasize analytical validity, clinical validity, reproducibility, and usefulness within a clearly defined clinical context [69].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [69]
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
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 1× · see citing works
“Algorithmic bias in data processing and interpretation can disproportionately affect minority groups, necessitating rigorousfairnessassessments inbiomarkervalidation 46 .”
Thayer, J. F., Yamamoto, S. S., & Brosschot, J. F. (2010) · International Journal of Cardiology · doi:10.1016/j.ijcard.2009.09.543 · Cited by 2,276 · see citing works
Diagnosing the decline in pharmaceutical R&D efficiency
Scannell, J. W., Blanckley, A., Boldon, H., & Warrington, B. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3681 · Cited by 1,981 · see citing works
Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation
Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1901183 · Cited by 1,917 · see citing works
Impact of Reduced Heart Rate Variability on Risk for Cardiac Events: The Framingham Heart Study
Tsuji, H., Larson, M. G., Venditti, F. J., Manders, E. S., Evans, J. C., Feldman, C. L., & Levy, D. (1996) · Circulation · doi:10.1161/01.cir.94.11.2850 · Cited by 1,762 · see citing works
Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018
Wouters, O. J., McKee, M., & Luyten, J. (2020) · JAMA · doi:10.1001/jama.2020.1166 · Cited by 1,531 · see citing works
Digital Phenotyping: Technology for a New Science of Behavior
Insel, T. R. (2017) · JAMA · doi:10.1001/jama.2017.11295 · Cited by 833 · see citing works
Continuous glucose monitoring and metrics for clinical trials: an international consensus statement
Battelino, T., Alexander, C. M., Amiel, S. A., Arreaza-Rubin, G., Beck, R. W., Bergenstal, R. M., Buckingham, B. A., Carroll, J., Ceriello, A., Chow, E., et al. (2023) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(22)00319-9 · Cited by 831 · see citing works
Torous, J., Kiang, M. V., Lorme, J., & Onnela, J.-P. (2016) · JMIR Mental Health · doi:10.2196/mental.5165 · Cited by 758 · cited 1× · see citing works
“Digital phenotyping allows for the continuous, high-resolution monitoring of individual health trajectories through everyday digital interactions 51 .”
Continuous Glucose Monitoring: A Review of Successes, Challenges, and Opportunities
Rodbard, D. (2016) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2015.0417 · Cited by 743 · see citing works
Phase II and phase III failures: 2013–2015
Harrison, R. K. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.184 · Cited by 636 · see citing works
Poste, G. (2011) · Nature · doi:10.1038/469156a · Cited by 582 · see citing works
Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers
Trebeschi, S., Drago, S. G., Birkbak, N. J., Kurilova, I., Calin, A., Pizzi, A. D., Lalezari, F., Lambregts, D. M. J., Rohaan, M. W., Parmar, C., et al. (2019) · Annals of Oncology · doi:10.1093/annonc/mdz108 · Cited by 567 · see citing works
Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch
Tison, G. H., Sanchez, J. M., Ballinger, B., Singh, A., Olgin, J. E., Pletcher, M. J., Vittinghoff, E., Lee, E. S., Fan, S. M., Gladstone, R. A., et al. (2018) · JAMA Cardiology · doi:10.1001/jamacardio.2018.0136 · Cited by 566 · see citing works
Goldsack, J. C., Coravos, A., Bakker, J. P., Bent, B., Dowling, A. V., Fitzer-Attas, C., Godfrey, A., Godino, J. G., Gujar, N., Izmailova, E., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0260-4 · Cited by 557 · see citing works
Automated assessment of psychiatric disorders using speech: A systematic review
Low, D. M., Bentley, K. H., & Ghosh, S. (2020) · Laryngoscope Investigative Otolaryngology · doi:10.1002/lio2.354 · Cited by 519 · see citing works
Coorey, G., Figtree, G. A., Fletcher, D. F., Snelson, V. J., Vernon, S. T., Winlaw, D. S., Grieve, S. M., McEwan, A. L., Yang, J. Y. H., Qian, P. C., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00640-7 · Cited by 415 · see citing works
Wearable Devices for Ambulatory Cardiac Monitoring
Sana, F., Isselbacher, E. M., Singh, J. P., Heist, E. K., Pathik, B., & Armoundas, A. A. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.01.046 · Cited by 362 · see citing works
Heart Rate Variability in Risk Stratification of Cardiac Patients
Huikuri, H. V., & Stein, P. K. (2013) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2013.07.003 · Cited by 282 · see citing works
Parkinson’s Disease Diagnosis Using Machine Learning and Voice
Wroge, T. J., Özkanca, Y., Demiroğlu, C., Si, D., Atkins, D. C., & Ghomi, R. H. (2018) · IEEE Signal Processing in Medicine and Biology Symposium · doi:10.1109/spmb.2018.8615607 · Cited by 260 · see citing works
Can Wearable Devices Accurately Measure Heart Rate Variability? A Systematic Review
Georgiou, K., Larentzakis, A. V., Khamis, N. N., Alsuhaibani, G. I., Alaska, Y. A., & Giallafos, E. J. (2018) · Folia Medica · doi:10.2478/folmed-2018-0012 · Cited by 244 · see citing works
Network-based machine learning approach to predict immunotherapy response in cancer patients
Kong, J., Ha, D., Lee, J., Kim, I., Park, M., Im, S.-H., Shin, K., & Kim, S. (2022) · Nature Communications · doi:10.1038/s41467-022-31535-6 · Cited by 219 · see citing works
Zheng, N. S., Annis, J. S., Master, H., Han, L., Gleichauf, K., Ching, J., Nasser, M., Coleman, P. L., Desine, S., Ruderfer, D. M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03155-8 · Cited by 207 · see citing works
Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence
Bonkhoff, A. K., & Grefkes, C. (2021) · Brain · doi:10.1093/brain/awab439 · Cited by 195 · see citing works
Digital biomarkers: Convergence of digital health technologies and biomarkers
Vasudevan, S., Saha, A., Tarver, M. E., & Patel, B. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00583-z · Cited by 184 · see citing works
Johannet, P. M., Coudray, N., Donnelly, D. M., Jour, G., Illa-Bochaca, I., Xia, Y., Johnson, D. B., Wheless, L., Patrinely, J. R., Nomikou, S., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-2415 · Cited by 181 · see citing works
Traditional and Digital Biomarkers: Two Worlds Apart?
Babrak, L. M., Menetski, J., Rebhan, M., Nisato, G., Zinggeler, M., Brasier, N., Baerenfaller, K., Brenzikofer, T., Baltzer, L., Vogler, C., et al. (2019) · Digital Biomarkers · doi:10.1159/000502000 · Cited by 181 · see citing works
Digital Medicine: A Primer on Measurement
Coravos, A., Goldsack, J. C., Karlin, D. R., Nebeker, C., Perakslis, E. D., Zimmerman, N., & Erb, M. K. (2019) · Digital Biomarkers · doi:10.1159/000500413 · Cited by 161 · see citing works
Machine Learning in Action: Stroke Diagnosis and Outcome Prediction
Mainali, S., Darsie, M. E., & Smetana, K. S. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.734345 · Cited by 157 · see citing works
The Use of Deep Learning to Predict Stroke Patient Mortality
Cheon, S.-H., Kim, J., & Lim, J. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16111876 · Cited by 153 · see citing works
Kuwabara, M., Harada, K., Hishiki, Y., & Kario, K. (2019) · The Journal of Clinical Hypertension · doi:10.1111/jch.13499 · Cited by 140 · see citing works
Amir, O., Gal, T. B., Weinstein, J. M., Schliamser, J. E., Burkhoff, D., Abbo, A., & Abraham, W. T. (2017) · International Journal of Cardiology · doi:10.1016/j.ijcard.2017.02.120 · Cited by 137 · see citing works
Manta, C. J., Patrick‐Lake, B., & Goldsack, J. C. (2020) · Digital Biomarkers · doi:10.1159/000509725 · Cited by 132 · see citing works
Smart Wearables for Cardiac Monitoring—Real-World Use beyond Atrial Fibrillation
Duncker, D., Ding, W. Y., Etheridge, S. P., Noseworthy, P. A., Veltmann, C., Yao, X., Bunch, T. J., & Gupta, D. (2021) · Sensors · doi:10.3390/s21072539 · Cited by 132 · see citing works
Robust automated Parkinson disease detection based on voice signals with transfer learning
Karaman, O., Çakın, H., Alhudhaif, A., & Polat, K. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115013 · Cited by 131 · see citing works
Jansen, E. M., van de Hei, S. J., Dierick, B. J. H., Kerstjens, H. A. M., Kocks, J. W. H., & van Boven, J. F. M. (2021) · Journal of Thoracic Disease · doi:10.21037/jtd-20-2360 · Cited by 126 · see citing works
Guo, L. N., Lee, M. S., Kassamali, B., Mita, C., & Nambudiri, V. E. (2022) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2021.06.884 · Cited by 122 · see citing works
Rusz, J., Hlavnička, J., Tykalová, T., Novotný, M., Dušek, P., Šonka, K., & Růžička, E. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2851787 · Cited by 118 · see citing works
Bloem, B. R., Marks, W. J., de Lima, A. L. S., Kuijf, M. L., van Laar, T., Jacobs, B., Verbeek, M. M., Helmich, R. C. G., van de Warrenburg, B. P. C., Evers, L. J. W., et al. (2019) · BMC Neurology · doi:10.1186/s12883-019-1394-3 · Cited by 117 · see citing works
Trials and Terminations: Learning from Competitors’ R&D Failures
Krieger, J. (2021) · Management Science · doi:10.1287/mnsc.2020.3775 · Cited by 99 · see citing works
Use of High-Frequency In-Home Monitoring Data May Reduce Sample Sizes Needed in Clinical Trials
Dodge, H. H., Zhu, J., Mattek, N. C., Austin, D., Kornfeld, J., & Kaye, J. A. (2015) · PLOS ONE · doi:10.1371/journal.pone.0138095 · Cited by 99 · see citing works
Rodbard, H. W., Peters, A. L., Slee, A., Cao, A., Traina, S. B., & Alba, M. (2017) · Diabetes Care · doi:10.2337/dc16-1353 · Cited by 94 · see citing works
Vocal Biomarker Is Associated With Hospitalization and Mortality Among Heart Failure Patients
Maor, E., Perry, D., Mevorach, D., Taiblum, N., Luz, Y., Mazin, I., Lerman, A., Koren, G., & Shalev, V. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.119.013359 · Cited by 82 · see citing works
Validation of the portable Air-Smart Spirometer
Hernández, C. R., Fernández, M. N., Pallarés-Sanmartín, A., Roibas, C. M., Domínguez, L. C., Rial, M. I. B., Cid, N. B., & Fernández‐Villar, A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0192789 · Cited by 82 · see citing works
What evidence do we need for biomarker qualification?
Leptak, C., Menetski, J. P., Wagner, J. A., Aubrecht, J., Brady, L. S., Brumfield, M. A., Chin, W. W., Hoffmann, S., Kelloff, G. J., Lavezzari, G., et al. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aal4599 · Cited by 78 · see citing works
Parkinson’s Disease Detection Based on Running Speech Data From Phone Calls
Laganas, C., Iakovakis, D., Hadjidimitriou, S., Charisis, V., Dias, S. B., Bostantzopoulou, S., Katsarou, Z., Klingelhoefer, L., Reichmann, H., Trivedi, D., et al. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3116935 · Cited by 76 · see citing works
Definitions of digital biomarkers: a systematic mapping of the biomedical literature
Alonso, A. K. M., Hirt, J., Woelfle, T., Janiaud, P., & Hemkens, L. G. (2024) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2023-100914 · Cited by 75 · see citing works
Servais, L., Camino, E., Clement, A., McDonald, C. M., Lukawy, J., Lowes, L. P., Eggenspieler, D., Cerreta, F., & Strijbos, P. J. L. M. (2021) · Digital Biomarkers · doi:10.1159/000517411 · Cited by 74 · see citing works
Patterns of Ownership and Usage of Wearable Devices in the United States, 2020-2022: Survey Study
Nagappan, A., Krasniansky, A., & Knowles, M. (2024) · Journal of Medical Internet Research · doi:10.2196/56504 · Cited by 66 · see citing works
Digital health technology in clinical trials
Mittermaier, M., Venkatesh, K. P., & Kvedar, J. C. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00841-8 · Cited by 65 · see citing works
Self-citationAssociation of sleep duration and risk of mental disorder: a systematic review and meta-analysis
Zhang, J., He, M., Wang, X., Jiang, H., Huang, J., & Liang, S. (2023) · Sleep And Breathing · doi:10.1007/s11325-023-02905-1 · Cited by 63 · see citing works
Castelletti, S., Dagradi, F., Goulene, K., Danza, A. I., Baldi, E., Stramba-Badiale, M., & Schwartz, P. J. (2018) · International Journal of Cardiology · doi:10.1016/j.ijcard.2018.03.097 · Cited by 63 · see citing works
Landeck, L., Kneip, C., Reischl, J., & Asadullah, K. (2016) · Experimental Dermatology · doi:10.1111/exd.12948 · Cited by 60 · see citing works
In vitro evaluation of an asthma dosing device: The smart-inhaler
Burgess, S., Wilson, S., Cooper, D. M., Sly, P. D., & Devadason, S. G. (2005) · Respiratory Medicine · doi:10.1016/j.rmed.2005.09.004 · Cited by 59 · see citing works
Digital health and digital biomarkers – enabling value chains on health data
Meister, S., Deiters, W., & Becker, S. (2016) · Current Directions in Biomedical Engineering · doi:10.1515/cdbme-2016-0128 · Cited by 53 · see citing works
Gibson, C. M., Steinhubl, S. R., Lakkireddy, D., Turakhia, M. P., PASSMAN, R. S., Jones, W. S., Bunch, T. J., Curtis, A. B., Peterson, E. D., Ruskin, J. N., et al. (2023) · American Heart Journal · doi:10.1016/j.ahj.2023.01.004 · Cited by 50 · see citing works
Gupta, S., Chang, P., Anyigbo, N., & Sabharwal, A. (2011) · ACM Workshop on Mobile Systems, Applications, and Services for Healthcare (mHealthSys) · doi:10.1145/2064942.2064944 · Cited by 47 · see citing works
Reviving an R&D pipeline: a step change in the Phase II success rate
Wu, S. S., Fernando, K., Allerton, C. M. N., Jansen, K. U., Vincent, M. S., & Dolsten, M. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.019 · Cited by 47 · see citing works
Zhou, P., Yang, L., & Huang, Y. (2019) · Sensors · doi:10.3390/s19112487 · Cited by 46 · see citing works
Gill, S. K., Barsky, A., Guan, X., Bunting, K. V., Karwath, A., Țica, O., Stanbury, M., Haynes, S., Folarin, A., Dobson, R., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03094-4 · Cited by 43 · see citing works
Sturchio, A., Marsili, L., Vizcarra, J. A., Dwivedi, A. K., Kauffman, M. A., Duker, A. P., Lu, P., Pauciulo, M. W., Wissel, B. D., Hill, E. J., et al. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.553635 · Cited by 39 · see citing works
Nathan, V., Vatanparvar, K., Rahman, M., Nemati, E., & Kuang, J. (2019) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2019.8771043 · Cited by 37 · see citing works
Industry perspectives on biomarker qualification
Lavezzari, G., & Womack, A. (2015) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.264 · Cited by 33 · see citing works
Shah, S. J., Eckman, M. H., Aspberg, S., Go, A. S., & Singer, D. E. (2018) · Annals of Internal Medicine · doi:10.7326/m17-2762 · Cited by 25 · see citing works
Fall risk prediction using temporal gait features and machine learning approaches
Lim, Z. K., Connie, T., Goh, M. K. O., & Saedon, N. I. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1425713 · Cited by 22 · see citing works
Truong, A. T. L., Tan, S., Wang, G. Z., Yip, A. W., Egermark, M., Yeung, W. L. K., Lee, V. V., Chan, M. Y., Kumar, K. S., Tan, L. W. J., et al. (2023) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztad063 · Cited by 20 · see citing works
Innovation Incentives and Biomarkers
Stern, A. D., Alexander, B. M., & Chandra, A. (2017) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.876 · Cited by 20 · see citing works
Self-citationWong, C., Un, K., Zhou, M., Cheng, Y., Lau, Y.-M., Shea, P. C., Lui, H. W., Ming Liang, Z. U. O., Yin, L. X., Chan, E. W., et al. (2022) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztac024 · Cited by 19 · see citing works
Mori, H., Wiklund, S. J., & Zhang, J. Y. (2022) · Digital Biomarkers · doi:10.1159/000525255 · Cited by 16 · see citing works
Chen, C., Kowahl, N., Rainaldi, E., Burq, M., Munsie, L. M., Battioui, C., Wang, J., Biglan, K., Marks, W. J., & Kapur, R. (2023) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2023.105355 · Cited by 14 · see citing works
Assessing the net financial benefits of employing digital endpoints in clinical trials
DiMasi, J. A., Dirks, A., Smith, Z., Valentine, S., Goldsack, J. C., Metcalfe, T., Grewal, U., Leyens, L., Conradi, U., Karlin, D. R., et al. (2024) · Clinical and Translational Science · doi:10.1111/cts.13902 · Cited by 11 · see citing works
Participants in the FDA 's Biomarker Qualification Program
Shahzad, M., & Stern, A. D. (2025) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.3661 · Cited by 8 · see citing works
Self-citationCompeting with Precision: Incentives for Developing Predictive Biomarker Tests
Brekke, K. R., Dalen, D. M., & Straume, O. R. (2022) · Scandinavian Journal of Economics · doi:10.1111/sjoe.12543 · Cited by 7 · see citing works
Pre-approval incentives to promote adoption of personalized medicine: a theoretical approach
Antoñanzas, F., Juárez-Castelló, C. A., & Rodríguez‐Ibeas, R. (2019) · Health Economics Review · doi:10.1186/s13561-019-0244-8 · Cited by 7 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [10]Mandrola, J. & Foy, A. Downsides of detecting atrial fibrillation in asymptomatic patients. Am. Fam. Physician 99, 354–355 (2019). 30874403
- [13]Gupta, S., Chang, P., Anyigbo, N. & Sabharwal, A. MobileSpiro: accurate mobile spirometry for self-management of asthma. In Proc. First ACM Workshop on Mobile Systems, Applications, and Services for Healthcare 1–6 (Association for Computing Machinery, 2011).
- [70]Lawton, M. et al. Parkinson’s disease subtypes in the Oxford Parkinson disease centre (OPDC) discovery cohort. J. Park. Dis. 5, 269–279 (2015). PMC4923737 26405788
- [76]Noh, B. et al. XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes. Sci. Rep. 11, 12183 (2021). 34108595 PMC8190134 · DOI
Cited by
Sorted by citations
Guo, X., Wei, Y., Miao, Z., Ma, S., & Ma, W. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1924506 · Cited by 5 · see citing works
Questioning Personalised Health: Limitations of Interpreting Wearable Data at the Individual Level
Kwon, J. W. (2026) · Daehan imsang geon-gang jeungjin hakoeji/Daehan imsang geon'gang jeungjin haghoeji · doi:10.15384/kjhp.2026.00255 · Cited by 1 · see citing works
The role of artificial intelligence in clinical trials in adult and pediatric rheumatology
Bella, S. L., Rebollo-Giménez, A. I., Alongi, A., Capitoli, G., Breda, L., Torre, F. L., Venerito, V., & Ruperto, N. (2026) · Current Opinion in Immunology · doi:10.1016/j.coi.2026.102818 · Cited by 1 · see citing works
Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease
Shaulian, S., Zeltser, R., & Makaryus, A. N. (2026) · Diagnostics · doi:10.3390/diagnostics16172834
The role of digital technologies in the pharmacovigilance system
Алексеевич, П. М., Zyryanov, S. K., & Yavorsky, A. N. (2026) · Kachestvennaya Klinicheskaya Praktika = Good Clinical Practice · doi:10.37489/2588-0519-gcp-0025
Neurons Die Not by One Hit, but by Signaling Convergence
Chauhan, D., Goel, F., & Rajput, M. S. (2026) · Molecular Neurobiology · doi:10.1007/s12035-026-06093-3
Computational Nutrition 2.0: an algorithmic frontier for AI agents
Xie, M., Shen, C., Zhu, R., & Lv, C. (2026) · AI Agent · doi:10.20517/aiagent.2026.32
Tudor, S. S., Tupu, A. E., Munteanu, A. E., Ștefan, C. S., & Ferţu, D. I. (2026) · Healthcare · doi:10.3390/healthcare14182894
In silico biomedicine in the era of artificial intelligence: opportunities and challenges
Mustaffa, F.-E., Sajid, T., Ali, M., & Ashraf, N. M. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100629
Jia, F., Li, X., Song, H., Liu, X., Tang, Y., Wen, Q., & Wu, P. (2026) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2026.1948186
Saliev, T. M., & Singh, P. B. (2026) · International Journal of Translational Medicine · doi:10.3390/ijtm6040045
Hu, L., & Yang, M. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1989794
Next‐Generation Brain Health Monitoring Enabled by Integrated Wearable AI Infrastructure
Masrur, M. A., Khan, B., Khalid, R. T., Sumaira, & Abdullah, S. (2026) · Smart Medicine · doi:10.1002/smmd.70053
- Authors
- Lieberwirth, J. K., Mittermaier, M., & Stern, A. D.
- Affiliations
- Hasso Plattner Institute (DE)
- Humboldt-Universität zu Berlin (DE)
- Corresponding author
- Lieberwirth, Johann K.
- Venue
- Communications Medicine
- Published
- 2026-02-21
- Volume
- 6
- Issue
- 1
- ISSN
- 2730-664X (ISSN-L)
- PMC
- PMC12996552
- OpenAlex
- W7130837187
- Cited by
- 13
- References
- 72
- Citations by year
- 2026: 13
- FWCI
- 23.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Communications Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Novo Nordisk (NNF23SA0087056)
- Novo Nordisk Fonden (NNF23SA0087056)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- digital biomarkers, wearable sensors, continuous glucose monitoring, insulin dose, cardiovascular clinical trials, cardiac rhythm monitoring, personalized medicine, regulatory review, value-based payment, data silos
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
