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  • Review Article
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Mobile medical systems for equitable healthcare

Nature Reviews Bioengineering (2025)Cite this article

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Abstract

Major barriers to accessible healthcare include the high cost of medical devices and limited healthcare facilities. Mobile computing technologies, such as smartphones and smart watches, include high-quality hardware, such as microphones, speakers and cameras, which can be leveraged for the design of low-cost mobile medical systems intended to be remotely applied to monitor health and disease. In this Review, we discuss low-cost and accessible hardware — in particular, mobile phones — that can be used in mobile medical systems to aid in medical diagnostics and monitoring. Specifically, we outline acoustic-based systems, vision-based systems and sensor fusion systems that allow different levels of health and disease assessment, relying on the speakers, microphones and sensors of smart mobile devices. We highlight the challenges related to the deployment of mobile medical systems in the clinical continuum, including scaling, generalizability, bias, trust and privacy. Finally, we examine clinical integration and regulatory considerations with regard to mobile medical devices as well as future applications.

Key points

  • Mobile medical systems leverage smart devices and their sensing capabilities for the remote detection of health conditions.

  • Acoustics-based systems rely on microphones and speakers to monitor vital signs in a contactless manner and to passively sense audible biomarkers to detect medical conditions.

  • Vision-based systems combine cameras and actuators in smart devices with computer vision algorithms to track physiological signals, diagnose medical conditions and analyse biofluids.

  • Sensor fusion systems combine passively measured sensor data, digital activity traces and questionnaire responses to assess digital biomarkers associated with physical, mental and behavioural health.

  • The scaling and adoption of mobile medical systems require generalizability to diverse hardware designs, adaptation to real-world environments, assurance of patient privacy and mitigation of clinical bias.

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Fig. 1: Mobile medical systems.
Fig. 2: Contactless monitoring of vital signs using active sonar.
Fig. 3: Low-cost earable systems to detect ear disorders.
Fig. 4: Vision-based systems.

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References

  1. World Health Organization. Local production and technology transfer to increase access to medical devices — addressing the barriers and challenges in low-and middle-income countries; https://www.who.int/publications/i/item/9789241504546 (2012).

  2. Diaconu, K. et al. Methods for medical device and equipment procurement and prioritization within low-and middle-income countries: findings of a systematic literature review. Glob. Health 13, 59 (2017).

    Article  Google Scholar 

  3. Vasan, A. & Friend, J. Medical devices for low- and middle-income countries: a review and directions for development. J. Med. Devices 14, 010803 (2020).

    Article  Google Scholar 

  4. Haile, L. M. et al. Hearing loss prevalence and years lived with disability, 1990–2019: findings from the Global Burden of Disease Study 2019. Lancet 397, 996–1009 (2021).

    Article  Google Scholar 

  5. Hay-McCutcheon, M. J., Threadgill, M., Yang, X. & Phillips, F. Access to hearing health care, geographical residency, and quality of life in adults with and without hearing loss. J. Am. Acad. Aud. 31, 485–495 (2020).

    Google Scholar 

  6. Singer, D. E. et al. Impact of global geographic region on time in therapeutic range on warfarin anticoagulant therapy: data from the ROCKET AF clinical trial. J. Am. Heart Assoc. 2, e000067 (2013).

    Article  Google Scholar 

  7. Mwita, J. C. et al. Quality of anticoagulation with warfarin at a tertiary hospital in Botswana. Clin. Appl. Thromb. 24, 596–601 (2018).

    Article  Google Scholar 

  8. Semakula, J. R. et al. A cross-sectional evaluation of five warfarin anticoagulation services in Uganda and South Africa. PLoS ONE 15, e0227458 (2020).

    Article  Google Scholar 

  9. Nandakumar, R., Gollakota, S. & Watson, N. Contactless sleep apnea detection on smartphones. In Proc. 13th Ann. Int. Conf. Mobile Systems, Applications, and Services 45–57 (ACM, 2015). This article reports a smartphone-based system that detects sleep apnoea events using its built-in speakers and microphones as an active sonar to track subtle chest movements.

  10. Nandakumar, R., Gollakota, S. & Sunshine, J. E. Opioid overdose detection using smartphones. Sci. Transl. Med. 11, eaau8914 (2019).

    Article  Google Scholar 

  11. Larson, E. C., Lee, T., Liu, S., Rosenfeld, M. & Patel, S. N. Accurate and privacy preserving cough sensing using a low-cost microphone. In Proc. 13th Int. Conf. Ubiquitous Computing (UbiComp ’11) 375–384 (ACM, 2011). This article reports a privacy-preserving cough detection system that uses mobile phone microphone audio while ensuring speech cannot be reconstructed intelligibly.

  12. Wang, R. et al. StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proc. Int. Joint Conf. Pervasive and Ubiquitous Computing 3–14 (ACM, 2014). This article reports a smartphone-based passive sensing app that continuously measures how workload affects stress, sleep, activity, mood, sociability, mental well-being and academic performance in a college student population.

  13. Wang, R. et al. CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia. In Proc. Int. Joint Conf. Pervasive and Ubiquitous Computing 886–897 (ACM, 2016).

  14. Chikersal, P. et al. Predicting multiple sclerosis outcomes during the COVID-19 stay-at-home period: observational study using passively sensed behaviors and digital phenotyping. JMIR Ment. Health 9, e38495 (2022).

    Article  Google Scholar 

  15. Chan, J., Raju, S., Nandakumar, R., Bly, R. & Gollakota, S. Detecting middle ear fluid using smartphones. Sci. Transl. Med. 11, eaav1102 (2019). This article reports a smartphone-based system that detects middle-ear fluid using its built-in speakers and microphones to assess eardrum mobility, and a paper funnel to channel sound in and out of the ear canal.

    Article  Google Scholar 

  16. Chan, J., Michaelsen, K., Estergreen, J. K., Sabath, D. E. & Gollakota, S. Micro-mechanical blood clot testing using smartphones. Nat. Commun. 13, 831 (2022).

    Article  Google Scholar 

  17. Goel, M. et al. Spirocall: measuring lung function over a phone call. In Proc. Conf. Human Factors in Computing Systems (CHI ’16) 5675–5685 (ACM, 2016). This article reports a system that enables spirometry on the phone using the standard telephony voice channel to transmit the sound of spirometry; this system can operate with or without a 3D-printed vortex whistle.

  18. Barry, C., Xuan, Y., Fascetti, A., Moore, A. & Wang, E. J. Oscillometric blood pressure measurements on smartphones using vibrometric force estimation. Sci. Rep. 14, 26206 (2024).

    Article  Google Scholar 

  19. Wang, A., Sunshine, J. E. & Gollakota, S. Contactless infant monitoring using white noise. In 25th Ann. Int. Conf. on Mobile Computing and Networking (MobiCom ’19) 52 (ACM, 2019).

  20. Wang, A., Nguyen, D., Sridhar, A. R. & Gollakota, S. Using smart speakers to contactlessly monitor heart rhythms. Commun. Biol. 4, 319 (2021). This article reports a smart speaker-based system that uses active sonar to detect individual heartbeats and measure heart rate as well as inter-beat intervals for both regular and irregular rhythms.

    Article  Google Scholar 

  21. Chan, J. et al. Performing tympanometry using smartphones. Commun. Med. 2, 57 (2022).

    Article  Google Scholar 

  22. Ali, N. et al. An open-source smartphone otoacoustic emissions test for infants. Pediatrics 6, e2024068068 (2025).

    Article  Google Scholar 

  23. Chan, J. et al. Wireless earbuds for low-cost hearing screening. In Proc. 21st Ann. Int. Conf. Mobile Systems, Applications and Services 84–95 (ACM, 2023).

  24. Chan, J. et al. An off-the-shelf otoacoustic-emission probe for hearing screening via a smartphone. Nat. Biomed. Eng. 6, 1203–1213 (2022).

    Article  Google Scholar 

  25. Chan, J. et al. Detecting clinical medication errors with AI enabled wearable cameras. npj Digit. Med. 7, 287 (2024).

    Article  Google Scholar 

  26. Palumbo, A. Microsoft HoloLens 2 in medical and healthcare context: state of the art and future prospects. Sensors 22, 7709 (2022).

    Article  Google Scholar 

  27. Muensterer, O. J., Lacher, M., Zoeller, C., Bronstein, M. & Kübler, J. Google Glass in pediatric surgery: an exploratory study. Int. J. Surg. 12, 281–289 (2014).

    Article  Google Scholar 

  28. Wei, N. J., Dougherty, B., Myers, A. & Badawy, S. M. Using Google Glass in surgical settings: systematic review. JMIR mHealth uHealth 6, e9409 (2018).

    Article  Google Scholar 

  29. Chan, J., Rea, T., Gollakota, S. & Sunshine, J. E. Contactless cardiac arrest detection using smart devices. npj Digit. Med. 2, 52 (2019).

    Article  Google Scholar 

  30. Song, X. et al. SpiroSonic: monitoring human lung function via acoustic sensing on commodity smartphones. In Proc. 26th Ann. Int. Conf. Mobile Computing and Networking (MobiCom ’20) 52 (2020).

  31. Yin, X. et al. PTEase: objective airway examination for pulmonary telemedicine using commodity smartphones. In Proc. 21st Ann. Int. Conf. Mobile Systems, Applications and Services 110–123 (ACM, 2023).

  32. Watkins, T. Angelcare recalls baby monitors after 2 deaths. CNN Health https://edition.cnn.com/2013/11/22/health/baby-monitor-recall (22 November 2013).

  33. Skin integrity issues associated with pulse oximetry. Pennsylv. PSRS Patient Safety Advis. 2, 25–29 (2005).

  34. Jin, Y. et al. Earhealth: an earphone-based acoustic otoscope for detection of multiple ear diseases in daily life. In Proc. 20th Ann. Int. Conf. Mobile Systems, Applications and Services 397–408 (ACM, 2022).

  35. Fan, X., Pearl, D., Howard, R., Shangguan, L. & Thormundsson, T. APG: audioplethysmography for cardiac monitoring in hearables. In Proc. 29th Ann. Int. Conf. Mobile Computing and Networking (MobiCom ’23) 67 (ACM, 2023).

  36. Poets, C. F., Meny, R. G., Chobanian, M. R. & Bonofiglo, R. E. Gasping and other cardiorespiratory patterns during sudden infant deaths. Pediatr. Res. 45, 350–354 (1999).

    Article  Google Scholar 

  37. Lumsden, T. Observations on the respiratory centres in the cat. J. Physiol. 57, 153–160 (1923).

    Google Scholar 

  38. Zhang, H. et al. PDVocal: towards privacy-preserving Parkinson’s disease detection using non-speech body sounds. In 25th Ann. Int. Conf. Mobile Computing and Networking (MobiCom ’19) 16 (ACM, 2019).

  39. Rahman, T. et al. BodyBeat: a mobile system for sensing non-speech body sounds. MobiSys 14, 2–594 (2014).

    Google Scholar 

  40. Dicpinigaitis, P. V. Chronic cough due to asthma: ACCP evidence-based clinical practice guidelines. Chest 129, 75S–79S (2006).

    Article  Google Scholar 

  41. Fathi, H. et al. Cough in adult cystic fibrosis: diagnosis and response to fundoplication. Cough 5, 1 (2009).

    Article  Google Scholar 

  42. Smith, J. & Woodcock, A. Cough and its importance in COPD. Int. J. Chron. Obstruct. Pulmon. Dis. 1, 305–314 (2006).

    Google Scholar 

  43. Al Ismail, M., Deshmukh, S. & Singh, R. Detection of COVID-19 through the analysis of vocal fold oscillations. In Int. Conf. Acoustics, Speech and Signal Processing (ICASSP) 1035–1039 (IEEE, 2021).

  44. Laguarta, J., Hueto, F. & Subirana, B. COVID-19 artificial intelligence diagnosis using only cough recordings. IEEE Open. J. Eng. Med. Biol. 1, 275–281 (2020).

    Article  Google Scholar 

  45. Brown, C. et al. Exploring automatic diagnosis of COVID-19 from crowdsourced respiratory sound data. In Proc. 26th ACM/SIGKDD Int. Conf. Knowledge Discovery and Data Mining 3474–3484 (ACM, 2020).

  46. Pinkas, G. et al. SARS-CoV-2 detection from voice. IEEE Open. J. Eng. Med. Biol. 1, 268–274 (2020).

    Article  Google Scholar 

  47. Han, J. et al. Sounds of COVID-19: exploring realistic performance of audio-based digital testing. npj Digit. Med. 5, 16 (2022).

    Article  Google Scholar 

  48. Dang, T. et al. Exploring longitudinal cough, breath, and voice data for COVID-19 progression prediction via sequential deep learning: model development and validation. J. Med. Internet Res. 24, e37004 (2022).

    Article  Google Scholar 

  49. Whitehill, M., Garrison, J. & Patel, S. Whosecough: in-the-wild cougher verification using multitask learning. In Int. Conf. Acoustics, Speech and Signal Processing (ICASSP) 896–900 (IEEE, 2020).

  50. Al Hossain, F., Lover, A. A., Corey, G. A., Reich, N. G. & Rahman, T. FluSense: a contactless syndromic surveillance platform for influenza-like illness in hospital waiting areas. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4, 1–28 (2020).

    Article  Google Scholar 

  51. Dorafshanian, M., Aitsam, M., Mejri, M. & Di Nuovo, A. Beyond data collection: safeguarding user privacy in social robotics. In Int. Conf. Industrial Technology (ICIT) 1–6 (IEEE, 2024).

  52. Little, M., McSharry, P., Hunter, E., Spielman, J. & Ramig, L. Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans. Biomed. Eng. 56, 1015–1022 (2009).

    Article  Google Scholar 

  53. Lu, H. et al. Stresssense: detecting stress in unconstrained acoustic environments using smartphones. In Proc. Conf. Ubiquitous Computing 351–360 (ACM, 2012). This article reports a smartphone-based system that detects stress in a user’s voice using microphone recordings captured in both indoor and outdoor environments.

  54. Rachuri, K. K. et al. EmotionSense: a mobile phones based adaptive platform for experimental social psychology research. In Proc. 12th Int. Conf. Ubiquitous Computing 281–290 (ACM, 2010). This article reports a mobile sensing platform for social psychology studies that uses passive smartphone data to infer individual emotions, activities, and verbal and proximity interactions in a group.

  55. Ma, D., Ferlini, A. & Mascolo, C. OESense: employing occlusion effect for in-ear human sensing. In Proc. 19th Ann. Int. Conf. Mobile Systems, Applications, and Services 175–187 (ACM, 2021).

  56. Butkow, K.-J., Dang, T., Ferlini, A., Ma, D. & Mascolo, C. hEARt: motion-resilient heart rate monitoring with in-ear microphones. In Int. Conf. Pervasive Computing and Communications (PerCom) 200–209 (IEEE, 2023).

  57. Gilliam, F. R. III et al. In-ear infrasonic hemodynography with a digital health device for cardiovascular monitoring using the human audiome. npj Digit. Med. 5, 189 (2022).

    Article  Google Scholar 

  58. Prakash, J., Yang, Z., Wei, Y.-L., Hassanieh, H. & Choudhury, R. R. EarSense: earphones as a teeth activity sensor. In Proc. 26th Ann. Int. Conf. Mobile Computing and Networking (MobiCom ’20) 40 (ACM, 2020).

  59. Chen, T. et al. Exploring the feasibility of remote cardiac auscultation using earphones. In Proc. 30th Ann. Int. Conf. Mobile Computing and Networking (Mobicom ’24) 357–372 (ACM, 2024).

  60. Larson, E. C. et al. SpiroSmart: using a microphone to measure lung function on a mobile phone. In Proc. Conf. Ubiquitous Computing 280–289 (ACM, 2012).

  61. Balakrishnan, G., Durand, F. & Guttag, J. Detecting pulse from head motions in video. In Proc. IEEE Conf. Computer Vision and Pattern Recognition 3430–3437 (IEEE, 2013).

  62. De Haan, G. & Jeanne, V. Robust pulse rate from chrominance-based rPPG. IEEE Trans. Biomed. Eng. 60, 2878–2886 (2013).

    Article  Google Scholar 

  63. Li, X., Chen, J., Zhao, G. & Pietikainen, M. Remote heart rate measurement from face videos under realistic situations. In Proc. Conf. Computer Vision and Pattern Recognition 4264–4271 (IEEE, 2014).

  64. Poh, M.-Z., McDuff, D. J. & Picard, R. W. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Express 18, 10762–10774 (2010).

    Article  Google Scholar 

  65. Poh, M.-Z., McDuff, D. J. & Picard, R. W. Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans. Biomed. Eng. 58, 7–11 (2010).

    Article  Google Scholar 

  66. Tran, D. N., Lee, H. & Kim, C. A robust real time system for remote heart rate measurement via camera. In Int. Conf. Multimedia and Expo (ICME) 1–6 (IEEE, 2015).

  67. Tulyakov, S. et al. Self-adaptive matrix completion for heart rate estimation from face videos under realistic conditions. In Proc. Conf. Computer Vision and Pattern Recognition 2396–2404 (IEEE, 2016).

  68. Verkruysse, W., Svaasand, L. O. & Nelson, J. S. Remote plethysmographic imaging using ambient light. Opt. Express 16, 21434–21445 (2008).

    Article  Google Scholar 

  69. Adhikary, R., Sadeh, M., Batra, N. & Goel, M. JoulesEye: energy expenditure estimation and respiration sensing from thermal imagery while exercising. Proc. ACM Interact. Mob. Wearable Ubiq. Technol. 7, 1–29 (2024).

    Google Scholar 

  70. Mariakakis, A. et al. PupilScreen: using smartphones to assess traumatic brain injury. Proc. ACM Interact. Mob. Wearable Ubiq. Technol. 1, 1–27 (2017).

    Google Scholar 

  71. Barry, C. et al. At-home pupillometry using smartphone facial identification cameras. In Proc. Conf. Human Factors in Computing Systems (CHI ’22) 1–12 (ACM, 2022).

  72. Barry, C. & Wang, E. Racially fair pupillometry measurements for RGB smartphone cameras using the far red spectrum. Sci. Rep. 13, 13841 (2023).

    Article  Google Scholar 

  73. Mariakakis, A., Wang, E., Patel, S. & Wen, J. C. A smartphone-based system for assessing intraocular pressure. In 38th Ann. Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC) 4353–4356 (IEEE, 2016).

  74. You, C.-W. et al. Carsafe app: alerting drowsy and distracted drivers using dual cameras on smartphones. In Proc. 11th Ann. Int. Conf. Mobile Systems, Applications, and Services (MobiSys ’13) 13–26 (ACM, 2013).

  75. Khurana, R. & Goel, M. Eyes on the road: detecting phone usage by drivers using on-device cameras. In Proc. Conf. Human Factors in Computing Systems (CHI ’20) 1–11 (ACM, 2020).

  76. Yue, S. & Katabi, D. Liquid testing with your smartphone. In Proc. 17th Ann. Int. Conf. on Mobile Systems, Applications, and Services (MobiSys ’19) 275–286 (ACM, 2019).

  77. Chan, J., Raghunath, A., Michaelsen, K. E. & Gollakota, S. Testing a drop of liquid using smartphone LiDAR. Proc. ACM Interact. Mob. Wearable Ubiq. Technol. 6, 1–27 (2022).

    Article  Google Scholar 

  78. Wang, E. J. et al. HemaApp: noninvasive blood screening of hemoglobin using smartphone cameras. In Proc. Int. Joint Conf. Pervasive and Ubiquitous Computing 593–604 (ACM, 2016). This article reports a smartphone-based system that noninvasively measures blood haemoglobin concentration by analysing the colour of blood in a user’s finger placed over the smartphone camera, using either the built-in light-emitting diode (LED) flash or incandescent light as the illumination source.

  79. Wang, E. J., Zhu, J., Li, W., Rana, R. & Patel, S. HemaApp IR: noninvasive hemoglobin measurement using unmodified smartphone cameras and built-in LEDs. In Proc. Int. Joint Conf. Pervasive and Ubiquitous Computing and Proc. Int. Symp. Wearable Computers 305–308 (ACM, 2017).

  80. Wang, E. J., Li, W., Zhu, J., Rana, R. & Patel, S. N. Noninvasive hemoglobin measurement using unmodified smartphone camera and white flash. In 39th Ann. Int. Conf. Engineering in Medicine and Biology Society (EMBC) 2333–2336 (IEEE, 2017).

  81. Hoffman, J. S. et al. Smartphone camera oximetry in an induced hypoxemia study. npj Digit. Med. 5, 146 (2022).

    Article  Google Scholar 

  82. Bui, N. et al. Pho2: smartphone based blood oxygen level measurement systems using near-IR and red wave-guided light. In Proc. 15th Conf. Embedded Network Sensor Systems 26 (ACM, 2017).

  83. Wang, E. J. et al. Seismo: blood pressure monitoring using built-in smartphone accelerometer and camera. In Proc. Conf. Human Factors in Computing Systems (CHI ’18) 425 (ACM, 2018).

  84. Xuan, Y. et al. Ultra-low-cost mechanical smartphone attachment for no-calibration blood pressure measurement. Sci. Rep. 13, 8105 (2023).

    Article  Google Scholar 

  85. De Greef, L. et al. Bilicam: using mobile phones to monitor newborn jaundice. In Proc. Int. Joint Conf. Pervasive and Ubiquitous Computing 331–342 (ACM, 2014). This article reports a smartphone-based system that uses an onboard camera to monitor newborn jaundice by detecting yellow discolouration, and leverages a colour calibration card to ensure consistency across different smartphones.

  86. Taylor, J. A. et al. Use of a smartphone app to assess neonatal jaundice. Pediatrics 140, e20170312 (2017).

    Article  Google Scholar 

  87. Mariakakis, A. et al. Biliscreen: smartphone-based scleral jaundice monitoring for liver and pancreatic disorders. Proc. ACM Interact. Mob. Wearable Ubiq. Technol. 1, 1–26 (2017).

    Google Scholar 

  88. Mannino, R. G. et al. Smartphone app for non-invasive detection of anemia using only patient-sourced photos. Nat. Commun. 9, 4924 (2018).

    Article  Google Scholar 

  89. Babenko, B. et al. Detection of signs of disease in external photographs of the eyes via deep learning. Nat. Biomed. Eng. 6, 1370–1383 (2022).

    Article  Google Scholar 

  90. Strutt, J. et al. CAPAPP: smartphone-based capillary refill index assessment in healthy children. In Proc. Conf. Design of Medical Devices V001T09A009 (ASME, 2023).

  91. Eom, S., Sykes, D., Rahimpour, S. & Gorlatova, M. NeuroLens: augmented reality-based contextual guidance through surgical tool tracking in neurosurgery. In Int. Symp. Mixed and Augmented Reality (ISMAR) 355–364 (IEEE, 2022).

  92. Jin, A. et al. Tool detection and operative skill assessment in surgical videos using region-based convolutional neural networks. In Winter Conf. Applications of Computer Vision (WACV) 691–699 (IEEE, 2018).

  93. Shi, P., Zhao, Z., Hu, S. & Chang, F. Real-time surgical tool detection in minimally invasive surgery based on attention-guided convolutional neural network. IEEE Access 8, 228853–228862 (2020).

    Article  Google Scholar 

  94. Goodman, E. D. et al. Analyzing surgical technique in diverse open surgical videos with multitask machine learning. JAMA Surg. 159, 185–192 (2024).

    Article  Google Scholar 

  95. Fujii, R., Saito, H. & Kajita, H. EgoSurgery-tool: a dataset of surgical tool and hand detection from egocentric open surgery videos. Preprint at https://doi.org/10.48550/arXiv.2406.03095 (2024).

  96. Wang, R. et al. Tracking depression dynamics in college students using mobile phone and wearable sensing. Proc. ACM Interact. Mob. Wearable Ubiq. Technol. 2, 43 (2018).

    Google Scholar 

  97. Chikersal, P. et al. Detecting depression and predicting its onset using longitudinal symptoms captured by passive sensing: a machine learning approach with robust feature selection. ACM Trans. Computer-Human Interact. (TOCHI) 28, 3 (2021).

    Article  Google Scholar 

  98. Huckins, J. F. et al. Mental health and behavior of college students during the early phases of the COVID-19 pandemic: longitudinal smartphone and ecological momentary assessment study. J. Med. Internet Res. 22, e20185 (2020).

    Article  Google Scholar 

  99. Wang, R. et al. Predicting symptom trajectories of schizophrenia using mobile sensing. Proc. ACM Interact. Mob. Wearable Ubiq. Technol. 1, 1–24 (2017).

    Google Scholar 

  100. Adler, D. A. et al. Predicting early warning signs of psychotic relapse from passive sensing data: an approach using encoder-decoder neural networks. JMIR mHealth uHealth 8, e19962 (2020).

    Article  Google Scholar 

  101. Kawsar, F., Min, C., Mathur, A. & Montanari, A. Earables for personal-scale behavior analytics. IEEE Pervasive Comput. 17, 83–89 (2018).

    Article  Google Scholar 

  102. Min, C., Mathur, A. & Kawsar, F. Exploring audio and kinetic sensing on earable devices. In Proc. 4th Worksh. Wearable Systems and Applications 5–10 (ACM, 2018).

  103. Bondareva, E., Hauksdóttir, E. R. & Mascolo, C. Earables for detection of bruxism: a feasibility study. In Adjunct Proc. Int. Joint Conf. Pervasive and Ubiquitous Computing and Proc. Int. Symp. Wearable Computers 146–151 (ACM, 2021).

  104. Bui, N. et al. eBP: a wearable system for frequent and comfortable blood pressure monitoring from user’s ear. In 25th Ann. Int. Conf. Mobile Computing and Networking (MobiCom ’19) 53 (ACM, 2019).

  105. Pham, N. et al. WAKE: a behind-the-ear wearable system for microsleep detection. In Proc. 18th Int. Conf. Mobile Systems, Applications, and Services 404–418 (ACM, 2020).

  106. Nguyen, A. et al. LIBS: a bioelectrical sensing system from human ears for staging whole-night sleep study. Commun. ACM 61, 157–165 (2018).

    Article  Google Scholar 

  107. Aziz, A. et al. An unobtrusive and lightweight ear-worn system for continuous epileptic seizure detection. ACM Trans. Comput. Healthc. 6, 6 (2024).

    Google Scholar 

  108. Chan, J. et al. PACT: privacy sensitive protocols and mechanisms for mobile contact tracing. IEEE Data Eng. Bull. 43, 15–35 (2020).

    Google Scholar 

  109. Stevens, H. & Haines, M. B. TraceTogether: pandemic response, democracy, and technology. East. Asian Sci. Technol. Soc. Int. J. 14, 523–532 (2020).

    Article  Google Scholar 

  110. Group, C. P. H. et al. Digital contact tracing technologies in epidemics: a rapid review. Cochrane Database Syst. Rev. 8, CD013699 (2020).

    Google Scholar 

  111. Jones, N. R. et al. Two metres or one: what is the evidence for physical distancing in covid-19? BMJ 370, m3223 (2020).

    Article  Google Scholar 

  112. Althoff, T. et al. Large-scale physical activity data reveal worldwide activity inequality. Nature 547, 336–339 (2017).

    Article  Google Scholar 

  113. Lester, J., Choudhury, T. & Borriello, G. A practical approach to recognizing physical activities. In 4th Int. Conf. Pervasive Computing 1–16 (ACM, 2006).

  114. Lu, H., Pan, W., Lane, N. D., Choudhury, T. & Campbell, A. T. Soundsense: scalable sound sensing for people-centric applications on mobile phones. In Proc. 7th Int. Conf. Mobile Systems, Applications, and Services 165–178 (ACM, 2009).

  115. Chen, Z. et al. Unobtrusive sleep monitoring using smartphones. In 7th Int. Conf. Pervasive Computing Technologies for Healthcare and Workshops 145–152 (IEEE, 2013).

  116. Lu, H. et al. The jigsaw continuous sensing engine for mobile phone applications. In Proc. 8th Conf. Embedded Networked Sensor Systems 71–84 (ACM, 2010).

  117. LiKamWa, R., Liu, Y., Lane, N. D. & Zhong, L. Moodscope: building a mood sensor from smartphone usage patterns. In Proc. 11th Ann. Int. Conf. Mobile Systems, Applications, and Services 389–402 (ACM, 2013).

  118. Lane, N. et al. Bewell: a smartphone application to monitor, model and promote wellbeing. In 5th Int. ICST Conf. Pervasive Computing Technologies for Healthcare (IEEE, 2012).

  119. Lane, N. D. et al. Bewell: sensing sleep, physical activities and social interactions to promote wellbeing. Mob. Netw. Appl. 19, 345–359 (2014).

    Article  Google Scholar 

  120. Rabbi, M., Aung, M. H., Zhang, M. & Choudhury, T. MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones. In Proc. Int. Joint Conf. Pervasive and Ubiquitous Computing 707–718 (ACM, 2015).

  121. Consolvo, S. et al. Activity sensing in the wild: a field trial of UbiFit Garden. In Proc. SIGCHI Conf. Human Factors in Computing Systems (CHI ’08) 1797–1806 (ACM, 2008).

  122. Wang, R., Harari, G., Hao, P., Zhou, X. & Campbell, A. T. SmartGPA: how smartphones can assess and predict academic performance of college students. In Proc. Int. Joint Conf. Pervasive and Ubiquitous Computing 295–306 (ACM, 2015).

  123. Ben-Zeev, D., Scherer, E. A., Wang, R., Xie, H. & Campbell, A. T. Next-generation psychiatric assessment: using smartphone sensors to monitor behavior and mental health. Psychiat. Rehabil. J. 38, 218 (2015).

    Article  Google Scholar 

  124. Harari, G. M. et al. Using smartphones to collect behavioral data in psychological science: opportunities, practical considerations, and challenges. Persp. Psychol. Sci. 11, 838–854 (2016).

    Article  Google Scholar 

  125. Lindhiem, O. et al. Objective measurement of hyperactivity using mobile sensing and machine learning: pilot study. JMIR Form. Res. 6, e35803 (2022).

    Article  Google Scholar 

  126. Arakawa, R. et al. Lemurdx: using unconstrained passive sensing for an objective measurement of hyperactivity in children with no parent input. Proc. ACM Interact. Mob. Wearable Ubiq. Technol. 7, 1–23 (2023).

    Google Scholar 

  127. Adler, D. A. et al. Identifying mobile sensing indicators of stress-resilience. Proc. ACM Interact. Mob. Wearable Ubiq. Technol. 5, 1–32 (2021).

    Article  Google Scholar 

  128. Shao, Q. et al. Joey: supporting kangaroo mother care with computational fabrics. In Proc. 22nd Ann. Int. Conf. on Mobile Systems, Applications and Services 237–251 (ACM, 2024).

  129. Guo, X. et al. Exploring biomagnetism for inclusive vital sign monitoring: modeling and implementation. In Proc. 30th Ann. Int. Conf. Mobile Computing and Networking (MobiCom ’24) 93–107 (ACM, 2024).

  130. Truong, H. et al. Painometry: wearable and objective quantification system for acute postoperative pain. In Proc. 18th Int. Conf. Mobile Systems, Applications, and Services 419–433 (ACM, 2020).

  131. Zhou, H. et al. One ring to rule them all: an open source smartring platform for finger motion analytics and healthcare applications. In Proc. 8th ACM/IEEE Conf. Internet-of-Things Design and Implementation (IoTDI) 27–38 (ACM/IEEE, 2023).

  132. Zhou, H. et al. SignQuery: a natural user interface and search engine for sign languages with wearable sensors. In Proc. 29th Ann. Int. Conf. Mobile Computing and Networking (MobiCom ’23) 71 (ACM, 2023).

  133. Zhou, H., Lu, T., DeHaan, K. & Gowda, M. ASLRing: American sign language recognition with meta-learning on wearables. In 9th Int. Conf. Internet-of-Things Design and Implementation (IoTDI) 203–214 (ACM/IEEE, 2024).

  134. Yuan, K. et al. ToMoBrush: exploring dental health sensing using a sonic toothbrush. Proc. ACM Interact. Mob. Wearable Ubiq. Technol. 8, 1–27 (2024).

    Article  Google Scholar 

  135. Hu, Z., Radmehr, A., Zhang, Y., Pan, S. & Nguyen, P. IOTeeth: intra-oral teeth sensing system for dental occlusal diseases recognition. Proc. ACM Interact. Mob. Wearable Ubiq. Technol. 8, 1–29 (2024).

    Google Scholar 

  136. Tasnim Oshim, M. F., Killingback, J., Follette, D., Peng, H. & Rahman, T. MechanoBeat: monitoring interactions with everyday objects using 3D printed harmonic oscillators and ultra-wideband radar. In Proc. 33rd Ann. Symp. User Interface Software and Technology 430–444 (ACM, 2020).

  137. Iyer, V., Chan, J., Culhane, I., Mankoff, J. & Gollakota, S. Wireless analytics for 3D printed objects. In Proc. 31st Ann. Symp. User Interface Software and Technology 141–152 (ACM, 2018).

  138. L’heureux, A., Grolinger, K., Elyamany, H. F. & Capretz, M. A. Machine learning with big data: challenges and approaches. IEEE Access 5, 7776–7797 (2017).

    Article  Google Scholar 

  139. Baltrušaitis, T., Ahuja, C. & Morency, L.-P. Multimodal machine learning: a survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 41, 423–443 (2018).

    Article  Google Scholar 

  140. Rai, A., Chintalapudi, K. K., Padmanabhan, V. N. & Sen, R. Zee: zero-effort crowdsourcing for indoor localization. In Proc. 18th Ann. Int. Conf. Mobile Computing and Networking (MobiCom ’12) 293–304 (ACM, 2012).

  141. Brajdic, A. & Harle, R. Walk detection and step counting on unconstrained smartphones. In Proc. Int. Joint Conf. Pervasive and Ubiquitous Computing 225–234 (ACM, 2013).

  142. Seyyed-Kalantari, L., Zhang, H., McDermott, M. B., Chen, I. Y. & Ghassemi, M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27, 2176–2182 (2021).

    Article  Google Scholar 

  143. Burlina, P., Joshi, N., Paul, W., Pacheco, K. D. & Bressler, N. M. Addressing artificial intelligence bias in retinal diagnostics. Transl. Vis. Sci. Technol. 10, 13 (2021).

    Article  Google Scholar 

  144. Gianattasio, K. Z., Ciarleglio, A. & Power, M. C. Development of algorithmic dementia ascertainment for racial/ethnic disparities research in the US health and retirement study. Epidemiology 31, 126–133 (2020).

    Article  Google Scholar 

  145. Park, Y. et al. Comparison of methods to reduce bias from clinical prediction models of postpartum depression. JAMA Netw. Open 4, e213909 (2021).

    Article  Google Scholar 

  146. van Breugel, B., Liu, T., Oglic, D. & van der Schaar, M. Synthetic data in biomedicine via generative artificial intelligence. Nat. Rev. Bioeng. 2, 991–1004 (2024).

    Article  Google Scholar 

  147. Goodfellow, I. et al. Generative adversarial networks. Commun. ACM 63, 139–144 (2020).

    Article  Google Scholar 

  148. Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at https://doi.org/10.48550/arXiv.1312.6114 (2013).

  149. Arora, A. & Arora, A. Generative adversarial networks and synthetic patient data: current challenges and future perspectives. Future Healthc. J. 9, 190 (2022).

    Article  Google Scholar 

  150. Wan, Z., Zhang, Y. & He, H. Variational autoencoder based synthetic data generation for imbalanced learning. In Symp. Series Computational Intelligence (SSCI) 1–7 (IEEE, 2017).

  151. Li, Z., Zhu, H., Lu, Z. & Yin, M. Synthetic data generation with large language models for text classification: potential and limitations. In Proc. 2023 Conf. Empirical Methods in Natural Language Processing (ACL, 2023).

  152. Viceconti, M. et al. In silico trials: verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products. Methods 185, 120–127 (2021).

    Article  Google Scholar 

  153. Lecomte, F., Alvarez, P., Cotin, S. & Dillenseger, J.-L. Beyond respiratory models: a physics-enhanced synthetic data generation method for 2D–3D deformable registration. In Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition 2413–2421 (IEEE, 2024).

  154. Myles, P., Ordish, J. & Tucker, A. The potential synergies between synthetic data and in silico trials in relation to generating representative virtual population cohorts. Prog. Biomed. Eng. 5, 013001 (2023).

    Article  Google Scholar 

  155. Moss, A. J. Gender differences in ECG parameters and their clinical implications. Ann. Noninvasive Electrocardiol. 15, 1–2 (2010).

    Article  Google Scholar 

  156. Khane, R. S. & Surdi, A. D. Gender differences in the prevalence of electrocardiogram abnormalities in the elderly: a population survey in India. Iran. J. Med. Sci. 37, 92–99 (2012).

    Google Scholar 

  157. Nigg, B., Fisher, V. & Ronsky, J. Gait characteristics as a function of age and gender. Gait Posture 2, 213–220 (1994).

    Article  Google Scholar 

  158. McKean, K. A. et al. Gender differences exist in osteoarthritic gait. Clin. Biomech. 22, 400–409 (2007).

    Article  Google Scholar 

  159. Yu, S., Tan, T., Huang, K., Jia, K. & Wu, X. A study on gait-based gender classification. IEEE Trans. Image Process. 18, 1905–1910 (2009).

    Article  MathSciNet  Google Scholar 

  160. Allen, J. & Murray, A. Age-related changes in the characteristics of the photoplethysmographic pulse shape at various body sites. Physiol. Meas. 24, 297–307 (2003).

    Article  Google Scholar 

  161. Yousef, Q., Reaz, M. & Ali, M. A. M. The analysis of PPG morphology: investigating the effects of aging on arterial compliance. Meas. Sci. Rev. 12, 266–271 (2012).

    Article  Google Scholar 

  162. Fouzas, S., Priftis, K. N. & Anthracopoulos, M. B. Pulse oximetry in pediatric practice. Pediatrics 128, 740–752 (2011).

    Article  Google Scholar 

  163. Picone, D. S. et al. Influence of age on upper arm cuff blood pressure measurement. Hypertension 75, 844–850 (2020).

    Article  Google Scholar 

  164. Feiner, J. R., Severinghaus, J. W. & Bickler, P. E. Dark skin decreases the accuracy of pulse oximeters at low oxygen saturation: the effects of oximeter probe type and gender. Anesth. Analg. 105, S18–S23 (2007).

    Article  Google Scholar 

  165. Colvonen, P. J. Response to: investigating sources of inaccuracy in wearable optical heart rate sensors. npj Digit. Med. 4, 38 (2021).

    Article  Google Scholar 

  166. Keller, M. D., Harrison-Smith, B., Patil, C. & Arefin, M. S. Skin colour affects the accuracy of medical oxygen sensors. Nature 610, 449–451 (2022).

    Article  Google Scholar 

  167. Henry, N. R. et al. Disparities in hypoxemia detection by pulse oximetry across self-identified racial groups and associations with clinical outcomes. Crit. Care Med. 50, 204–211 (2022).

    Article  Google Scholar 

  168. Schlosshan, D. & Elliott, M. Sleep.3: clinical presentation and diagnosis of the obstructive sleep apnoea hypopnoea syndrome. Thorax 59, 347–352 (2004).

    Article  Google Scholar 

  169. Benzeghiba, M. et al. Automatic speech recognition and speech variability: a review. Speech Commun. 49, 763–786 (2007).

    Article  Google Scholar 

  170. Koenecke, A. et al. Racial disparities in automated speech recognition. Proc. Natl Acad. Sci. USA 117, 7684–7689 (2020).

    Article  Google Scholar 

  171. Hwang, W.-C., Myers, H. F., Abe-Kim, J. & Ting, J. Y. A conceptual paradigm for understanding culture’s impact on mental health: the cultural influences on mental health (CIMH) model. Clin. Psychol. Rev. 28, 211–227 (2008).

    Article  Google Scholar 

  172. Henry, C. J., Quek, R. Y. C., Kaur, B., Shyam, S. & Singh, H. K. G. A glycaemic index compendium of non-Western foods. Nutr. Diabetes 11, 2 (2021).

    Article  Google Scholar 

  173. Owen, M. et al. Diversity, equity, and inclusion in I&M: beyond the technology: wearables and the cultural compass. IEEE Instrum. Meas. Mag. 27, 38–43 (2024).

    Article  Google Scholar 

  174. Pereira, T. et al. Photoplethysmography based atrial fibrillation detection: a review. npj Digit. Med. 3, 3 (2020).

    Article  Google Scholar 

  175. Niemelä, M. J., Airaksinen, K. J. & Huikuri, H. V. Effect of beta-blockade on heart rate variability in patients with coronary artery disease. J. Am. Coll. Cardiol. 23, 1370–1377 (1994).

    Article  Google Scholar 

  176. Eichelbaum, M. Drug metabolism in thyroid disease. Clin. Pharmacokinet. 1, 339–350 (1976).

    Article  Google Scholar 

  177. van Zyl, L. T., Hasegawa, T. & Nagata, K. Effects of antidepressant treatment on heart rate variability in major depression: a quantitative review. Biopsychosoc. Med. 2, 12 (2008).

    Google Scholar 

  178. Wichniak, A., Wierzbicka, A., Walęcka, M. & Jernajczyk, W. Effects of antidepressants on sleep. Curr. Psychiatry Rep. 19, 1–7 (2017).

    Article  Google Scholar 

  179. Wang, X. & Luan, W. Research progress on digital health literacy of older adults: a scoping review. Front. Public Health 10, 906089 (2022).

    Article  Google Scholar 

  180. Kemp, E. et al. Health literacy, digital health literacy and the implementation of digital health technologies in cancer care: the need for a strategic approach. Health Promot. J. Austr. 32, 104–114 (2021).

    Article  Google Scholar 

  181. Tegegne, M. D. et al. Digital literacy level and associated factors among health professionals in a referral and teaching hospital: an implication for future digital health systems implementation. Front. Public Health 11, 1130894 (2023).

    Article  Google Scholar 

  182. Adjekum, A., Blasimme, A. & Vayena, E. Elements of trust in digital health systems: scoping review. J. Med. Internet Res. 20, e11254 (2018).

    Article  Google Scholar 

  183. Blalock, D., Gonzalez Ortiz, J. J., Frankle, J. & Guttag, J. What is the state of neural network pruning? Proc. Mach. Learn. Syst. 2, 129–146 (2020).

    Google Scholar 

  184. Guo, Y. A survey on methods and theories of quantized neural networks. Preprint at https://doi.org/10.48550/arXiv.1808.04752 (2018).

  185. Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge distillation: a survey. Int. J. Comput. Vis. 129, 1789–1819 (2021).

    Article  Google Scholar 

  186. Buciluǎ, C., Caruana, R. & Niculescu-Mizil, A. Model compression. In Proc. 12th ACM/SIGKDD Int. Conf. Knowledge Discovery and Data Mining 535–541 (ACM, 2006).

  187. Sainath, T. N., Kingsbury, B., Sindhwani, V., Arisoy, E. & Ramabhadran, B. Low-rank matrix factorization for deep neural network training with high-dimensional output targets. In Int. Conf. Acoustics, Speech and Signal Processing (ICASSP) 6655–6659 (IEEE, 2013).

  188. Elsken, T., Metzen, J. H. & Hutter, F. Neural architecture search: a survey. J. Mach. Learn. Res. 20, 1–21 (2019).

    MathSciNet  Google Scholar 

  189. Nguyen, D. C. et al. Federated learning for smart healthcare: a survey. ACM Comput. Surv. (CSUR) 55, 60 (2022).

    Google Scholar 

  190. Xu, J. et al. Federated learning for healthcare informatics. J. Healthc. Inform. Res. 5, 1–19 (2021).

    Article  Google Scholar 

  191. Choudhury, O. et al. Anonymizing data for privacy-preserving federated learning. In ECAI 2020, 24th European Conference on Artificial Intelligence (IOS Press, 2020).

  192. Iravantchi, Y., Ahuja, K., Goel, M., Harrison, C. & Sample, A. PrivacyMic: utilizing inaudible frequencies for privacy preserving daily activity recognition. In Proc. Conf. Human Factors in Computing Systems (CHI ’21) 198 (ACM, 2021).

  193. Acar, A., Aksu, H., Uluagac, A. S. & Conti, M. A survey on homomorphic encryption schemes: theory and implementation. ACM Comput. Surv. (CSUR) 51, 79 (2018).

    Google Scholar 

  194. Sagi, D., Spitzer-Shohat, S., Schuster, M., Daudi, L. & Rudolf, M. C. J. Teaching plain language to medical students: improving communication with disadvantaged patients. BMC Med. Educ. 21, 407 (2021).

    Article  Google Scholar 

  195. Pinaya, W. H. et al. Brain imaging generation with latent diffusion models. In MICCAI Worksh. Deep Generative Models 117–126 (Springer, 2022).

  196. Simonyan, K., Vedaldi, A. & Zisserman, A. Deep inside convolutional networks: visualising image classification models and saliency maps. In Workshop at International Conference on Learning Representations (ICLR, 2013).

  197. Kim, B. et al. Interpretability beyond feature attribution: quantitative testing with concept activation vectors (TCAV). In Int. Conf. Machine Learning 2668–2677 (PMLR, 2018).

  198. Krause, J. et al. Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology 125, 1264–1272 (2018).

    Article  Google Scholar 

  199. Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. In Proc. 31st Int. Conf. Neural Information Processing Systems (NIPS ’17) 4768–4777 (ACM, 2017).

  200. Lundberg, S. M. et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng. 2, 749–760 (2018).

    Article  Google Scholar 

  201. Ribeiro, M. T., Singh, S. & Guestrin, C. ‘Why should I trust you?’ Explaining the predictions of any classifier. In Proc. 22nd ACM/SIGKDD Int. Conf. Knowledge Discovery and Data Mining 1135–1144 (ACM, 2016).

  202. Lechner, N. H. An overview of cybersecurity regulations and standards for medical device software. In Central Eur. Conf. Information and Intelligent Systems 237–249 (Faculty of Organization and Informatics, 2017).

  203. Schmidt, A. Regulatory challenges in healthcare IT: ensuring compliance with HIPAA and GDPR. Acad. J. Sci. Technol. 3, 1–7 (2020).

    Google Scholar 

  204. Cohen, I. G., Gerke, S. & Kramer, D. B. Ethical and legal implications of remote monitoring of medical devices. Milbank Q. 98, 1257–1289 (2020).

    Article  Google Scholar 

  205. Alzghaibi, H. Examining healthcare practitioners’ perceptions of virtual physicians, mHealth applications, and barriers to adoption: insights for improving patient care and digital health integration. Int. J. Gen. Med. 18, 1865–1885 (2025).

    Article  Google Scholar 

  206. Guo, X., Zhang, X. & Sun, Y. The privacy–personalization paradox in mHealth services acceptance of different age groups. Electron. Commer. Res. Appl. 16, 55–65 (2016).

    Article  Google Scholar 

  207. Koelle, M., Wolf, K. & Boll, S. Beyond LED status lights-design requirements of privacy notices for body-worn cameras. In Proc. 12th Int. Conf. on Tangible, Embedded, and Embodied Interaction 177–187 (ACM, 2018).

  208. Lindegren, D., Karegar, F., Kane, B. & Pettersson, J. S. An evaluation of three designs to engage users when providing their consent on smartphones. Behav. Inf. Technol. 40, 398–414 (2021).

    Article  Google Scholar 

  209. Ayaz, M., Pasha, M. F., Alzahrani, M. Y., Budiarto, R. & Stiawan, D. The fast health interoperability resources (FHIR) standard: systematic literature review of implementations, applications, challenges and opportunities. JMIR Med. Inform. 9, e21929 (2021).

    Article  Google Scholar 

  210. Dotson, P. CPT® codes: what are they, why are they necessary, and how are they developed? Adv. Wound Care 2, 583–587 (2013).

    Article  Google Scholar 

  211. Chan, J. et al. Closed-loop wearable naloxone injector system. Sci. Rep. 11, 22663 (2021).

    Article  Google Scholar 

  212. Mitrasinovic, S. et al. Clinical and surgical applications of smart glasses. Technol. Health Care 23, 381–401 (2015).

    Article  Google Scholar 

  213. Breda, J., Springston, M., Mariakakis, A. & Patel, S. Feverphone: accessible core-body temperature sensing for fever monitoring using commodity smartphones. Proc. ACM Interact. Mob. Wearable Ubiq. Technol. 7, 1–23 (2023).

    Article  Google Scholar 

  214. Preston, J., Brown, F. W. & Hartley, B. Using telemedicine to improve health care in distant areas. Psychiat. Serv. 43, 25–32 (1992).

    Article  Google Scholar 

  215. Anawade, P. A., Sharma, D. & Gahane, S. A comprehensive review on exploring the impact of telemedicine on healthcare accessibility. Cureus 16, e55996 (2024).

    Google Scholar 

  216. Vesga, O. et al. Highly sensitive scent-detection of COVID-19 patients in vivo by trained dogs. PLoS ONE 16, e0257474 (2021).

    Article  Google Scholar 

  217. Rooney, N. J., Morant, S. & Guest, C. Investigation into the value of trained glycaemia alert dogs to clients with type I diabetes. PLoS ONE 8, e69921 (2013).

    Article  Google Scholar 

  218. Cornu, J.-N., Cancel-Tassin, G., Ondet, V., Girardet, C. & Cussenot, O. Olfactory detection of prostate cancer by dogs sniffing urine: a step forward in early diagnosis. Eur. Urol. 59, 197–201 (2011).

    Article  Google Scholar 

  219. Willis, C. M. et al. Olfactory detection of human bladder cancer by dogs: proof of principle study. BMJ 329, 712 (2004).

    Article  Google Scholar 

  220. Strauch, M. et al. More than apples and oranges — detecting cancer with a fruit fly’s antenna. Sci. Rep. 4, 3576 (2014).

    Article  Google Scholar 

  221. Qin, C. et al. Artificial olfactory biohybrid system: an evolving sense of smell. Adv. Sci. 10, 2204726 (2023).

    Article  Google Scholar 

  222. Sakar, B. E. et al. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J. Biomed. Health Inform. 17, 828–834 (2013).

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

    Justin Chan & Mayank Goel

  2. Paul G. Allen School of Computer Science, University of Washington, Seattle, WA, USA

    Shyamnath Gollakota

  3. Department of Information Science, Cornell Tech, New York, NY, USA

    Rajalakshmi Nandakumar

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Contributions

J.C., R.N. and S.G. researched data for the manuscript and contributed to discussion of its contents. J.C. and R.N. wrote the article. M.G. reviewed the manuscript. All authors edited the manuscript.

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Correspondence to Shyamnath Gollakota.

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Competing interests

The authors declare the following competing interests: S.G. and J.C. are co-founders of Wavely Diagnostics, Inc. The remaining authors declare no competing interests.

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Related links

Action plan: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device

Active sonar technology for smartphones: https://www.accessdata.fda.gov/cdrh_docs/pdf21/K211387.pdf

Android API: https://developer.android.com/guide/practices/compatibility

Blood oxygen: https://support.ouraring.com/hc/en-us/articles/7328398760851-Blood-Oxygen-Sensing-SpO2

Blood pressure: https://www.samsung.com/ae/support/apps-services/how-to-measure-blood-pressure-using-a-galaxy-watch/

Body composition: https://www.samsung.com/uk/support/mobile-devices/measure-your-body-composition-with-the-galaxy-watch-series/

BreatheEasy: https://www.accessdata.fda.gov/cdrh_docs/pdf21/K211387.pdf

Capture breathing rate by tracking chest movements from the front-facing camera: https://blog.google/technology/health/take-pulse-health-and-wellness-your-phone/

Car crash detection: https://store.google.com/intl/en/ideas/articles/car-crash-detection/

Coarse-grained location estimates: https://mapscaping.com/how-accurate-is-gps/

Digital Wellbeing app: https://blog.google/products/pixel/health-ai-better-sleep/

ECG app: https://support.apple.com/en-us/120278

Exposure notifications: https://about.google/company-info/commitments/

Fall detection: https://support.apple.com/en-us/108896

FDA has approved mobile medical systems that leverage device sensors: https://web.archive.org/web/20220308170430/https:/www.fda.gov/medical-devices/device-software-functions-including-mobile-medical-applications/examples-premarket-submissions-include-mmas-cleared-or-approved-fda

FDA-granted smartphone app for body-temperature monitoring: https://blog.google/products/pixel/google-thermometer-app-body-temperature/

Google Fit app for Pixel phones: https://blog.google/technology/health/take-pulse-health-and-wellness-your-phone/

Guidance for mobile medical applications: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/policy-device-software-functions-and-mobile-medical-applications

Hear the World: https://www.hear-the-world.com/

Hearing-screening devices: https://www.e3diagnostics.com/products/otoacoustic-emissions---oae/grason-stadler-corti

Heart rate: https://support.ouraring.com/hc/en-us/articles/4410656562579-Heart-Rate-Graph

Irregular heart rhythm: https://support.apple.com/en-us/120276

Irregular heart rhythms: https://store.google.com/magazine/fitbit_irregular_rhythm?pli=1&hl=en-US

Loss of pulse detection: https://blog.google/feed/pixel-watch-3-loss-of-pulse-detection-fda/

Measure colour changes caused by blood moving through the fingertip to compute heart rate: https://blog.google/technology/health/take-pulse-health-and-wellness-your-phone/

Medical device academy: https://medicaldeviceacademy.com/510k-cost/

Minuteful Kidney: https://blog.healthy.io/company-news/minuteful-kidney-receives-fda-clearance/

Mobile medical apps: https://web.archive.org/web/20220308170430/https://www.fda.gov/medical-devices/device-software-functions-including-mobile-medical-applications/examples-premarket-submissions-include-mmas-cleared-or-approved-fda

NOVID: https://www.novid.org/

Predict the start of the menstrual period: https://ouraring.com/womens-health

SENSORA digital stethoscope: https://www.ekohealth.com/blogs/newsroom/eko-announces-new-category-iii-cpt-code

Sleep sensing algorithms: https://blog.google/products/google-nest/new-nest-hub-soli/

SleepScore app: https://apps.apple.com/us/app/sleepscore/id1364781299

Software as a medical device: https://www.fda.gov/medical-devices/software-medical-device-samd/what-are-examples-software-medical-device

Sonar-based breathing monitors: https://www.mobihealthnews.com/news/sound-life-sciences-scores-fda-510k-smart-device-respiratory-monitoring-app

The SleepScore app: https://apps.apple.com/us/app/sleepscore/id1364781299

TraceTogether: https://github.com/opentrace-community/opentrace-calibration/blob/master/Trial%20Methodologies.md

Wristbands: https://www.empatica.com/embrace2/

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Chan, J., Goel, M., Gollakota, S. et al. Mobile medical systems for equitable healthcare. Nat Rev Bioeng (2025). https://doi.org/10.1038/s44222-025-00330-5

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  • DOI: https://doi.org/10.1038/s44222-025-00330-5

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