CC BY-NC-ND 4.0 · Yearb Med Inform 2022; 31(01): 106-115
DOI: 10.1055/s-0042-1742513
Section 1: Bioinformatics and Translational Informatics

Translational Bioinformatics to Enable Precision Medicine for All: Elevating Equity across Molecular, Clinical, and Digital Realms

Alice Tang*
1   Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
2   Graduate Program in Bioengineering, UCSF, San Francisco, CA, USA
3   School of Medicine, UCSF, San Francisco, CA, USA
Sarah Woldemariam*
1   Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
3   School of Medicine, UCSF, San Francisco, CA, USA
Jacquelyn Roger*
1   Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
4   Graduate Program in Biological and Medical Informatics, UCSF, San Francisco, CA, USA
Marina Sirota
1   Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
5   Department of Pediatrics, UCSF, San Francisco, CA, USA
› Author Affiliations


Objectives: Over the past few years, challenges from the pandemic have led to an explosion of data sharing and algorithmic development efforts in the areas of molecular measurements, clinical data, and digital health. We aim to characterize and describe recent advanced computational approaches in translational bioinformatics across these domains in the context of issues or progress related to equity and inclusion.

Methods: We conducted a literature assessment of the trends and approaches in translational bioinformatics in the past few years.

Results: We present a review of recent computational approaches across molecular, clinical, and digital realms. We discuss applications of phenotyping, disease subtype characterization, predictive modeling, biomarker discovery, and treatment selection. We consider these methods and applications through the lens of equity and inclusion in biomedicine.

Conclusion: Equity and inclusion should be incorporated at every step of translational bioinformatics projects, including project design, data collection, model creation, and clinical implementation. These considerations, coupled with the exciting breakthroughs in big data and machine learning, are pivotal to reach the goals of precision medicine for all.

* Co-first Authors

Publication History

Article published online:
04 December 2022

© 2022. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

  • References

  • 1 Butte AJ. Translational bioinformatics: coming of age. J Am Med Inform Assoc 2008 Nov-Dec;15(6):709-14.
  • 2 Nicholls SM, Poplawski R, Bull MJ, Underwood A, Chapman M, Abu-Dahab K, et al; COVID-19 Genomics UK (COG-UK) Consortium. CLIMB-COVID: continuous integration supporting decentralised sequencing for SARS-CoV-2 genomic surveillance. Genome Biol 2021 Jul 1;22(1):196.
  • 3 Bennett TD, Moffitt RA, Hajagos JG, Amor B, Anand A, Bissell MM, et al; National COVID Cohort Collaborative (N3C) Consortium. Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative. JAMA Netw Open 2021 Jul 1;4(7): e2116901.
  • 4 Gunasekeran DV, Tseng RMWW, Tham YC, Wong TY. Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies. NPJ Digit Med 2021 Feb 26;4(1):40.
  • 5 Maher B, Van Noorden R. How the COVID pandemic is changing global science collaborations. Nature 2021 Jun;594(7863):316-9.
  • 6 Sahoo D, Katkar GD, Khandelwal S, Behroozikhah M, Claire A, Castillo V, et al. AI-guided discovery of the invariant host response to viral pandemics. EBioMedicine 2021 Jun;68:103390.
  • 7 Hartl D, de Luca V, Kostikova A, Laramie J, Kennedy S, Ferrero E, et al. Translational precision medicine: an industry perspective. J Transl Med 2021 Jun 5;19(1):245.
  • 8 Leasure AC, Cohen JM. Prevalence of eczema among adults in the United States: a cross-sectional study in the All of Us research program. Arch Dermatol Res 2022 Feb 11.
  • 9 Acosta JN, Leasure AC, Both CP, Szejko N, Brown S, Torres-Lopez V, et al. Cardiovascular Health Disparities in Racial and Other Underrepresented Groups: Initial Results From the All of Us Research Program. J Am Heart Assoc 2021 Sep 7;10(17):e021724.
  • 10 Hull LE, Natarajan P. Self-rated family health history knowledge among All of Us program participants. Genet Med 2022 Apr;24(4):955-61.
  • 11 Delavar A, Radha Saseendrakumar B, Weinreb RN, Baxter SL. Racial and Ethnic Disparities in Cost-Related Barriers to Medication Adherence Among Patients With Glaucoma Enrolled in the National Institutes of Health All of Us Research Program. JAMA Ophthalmol 2022 Apr 1;140(4):354-61.
  • 12 Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019 Oct 25;366(6464):447-53.
  • 13 Lloyd-Price J, Arze C, Ananthakrishnan AN, Schirmer M, Avila-Pacheco J, Poon TW, et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 2019 May;569(7758):655-62.
  • 14 Mars RAT, Yang Y, Ward T, Houtti M, Priya S, Lekatz HR, et al. Longitudinal Multi-omics Reveals Subset-Specific Mechanisms Underlying Irritable Bowel Syndrome. Cell 2020 Sep 17;182(6):1460-1473.e17.
  • 15 Tarca AL, Pataki BÁ, Romero R, Sirota M, Guan Y, Kutum R, et al. Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth. Cell Rep Med 2021 Jun 15;2(6):100323.
  • 16 Liu MC, Oxnard GR, Klein EA, Swanton C, Seiden MV; CCGA Consortium. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann Oncol 2020 Jun;31(6):745-59.
  • 17 Hogan CA, Yang S, Garner OB, Green DA, Gomez CA, Dien Bard J, et al. Clinical Impact of Metagenomic Next-Generation Sequencing of Plasma Cell-Free DNA for the Diagnosis of Infectious Diseases: A Multicenter Retrospective Cohort Study. Clin Infect Dis 2021 Jan 27;72(2):239-45.
  • 18 Taubes A, Nova P, Zalocusky KA, Kosti I, Bicak M, Zilberter MY, et al. Experimental and real-world evidence supporting the computational repurposing of bumetanide for APOE4-related Alzheimer’s disease. Nature Aging 2021;1:932–47.
  • 19 Rodriguez S, Hug C, Todorov P, Moret N, Boswell SA, Evans K, et al. Machine learning identifies candidates for drug repurposing in Alzheimer’s disease. Nat Commun 2021 Feb 15;12(1):1033.
  • 20 Zeng B, Glicksberg BS, Newbury P, Chekalin E, Xing J, Liu K, et al. OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features. Nat Protoc 2021 Feb;16(2):728-53.
  • 21 Beigel JH, Tomashek KM, Dodd LE, Mehta AK, Zingman BS, Kalil AC, et al; ACTT-1 Study Group Members. Remdesivir for the Treatment of Covid-19 - Final Report. N Engl J Med 2020 Nov 5;383(19):1813-26.
  • 22 Morselli Gysi D, do Valle Í, Zitnik M, Ameli A, Gan X, Varol O, et al. Network medicine framework for identifying drug-repurposing opportunities for COVID-19. Proc Natl Acad Sci U S A 2021 May 11;118(19):e2025581118.
  • 23 Green ED, Gunter C, Biesecker LG, Di Francesco V, Easter CL, Feingold EA, et al. Strategic vision for improving human health at The Forefront of Genomics. Nature 2020 Oct;586(7831):683-92.
  • 24 Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet 2019 Apr;51(4):584-91
  • 25 Wojcik GL, Graff M, Nishimura KK, Tao R, Haessler J, Gignoux CR, et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 2019 Jun;570(7762):514-8.
  • 26 All of Us Research Program Investigators. The “All of Us” research program. New Engl J Med 2019;381(7):668-76.
  • 27 New Data Release: Filling Out the Pandemic Picture; 2021.
  • 28 Fox K. The Illusion of Inclusion - The “All of Us” Research Program and Indigenous Peoples’ DNA. N Engl J Med 2020 Jul 30;383(5):411-3.
  • 29 Lee SS. Obligations of the “Gift”: Reciprocity and Responsibility in Precision Medicine. Am J Bioeth 2021 Apr;21(4):57-66.
  • 30 Peterson TA, Fontil V, Koliwad SK, Patel A, Butte AJ. Quantifying Variation in Treatment Utilization for Type 2 Diabetes Across Five Major University of California Health Systems. Diabetes Care 2021 Apr;44(4):908-14.
  • 31 Catherine JP, Russell MV, Peter CH. The impact of race and socioeconomic factors on paediatric diabetes. EClinicalMedicine 2021 Nov 6;42:101186.
  • 32 Landi I, Glicksberg BS, Lee HC, Cherng S, Landi G, Danieletto M, et al. Deep representation learning of electronic health records to unlock patient stratification at scale. NPJ Digit Med 2020 Jul 17;3:96.
  • 33 Alexander N, Alexander DC, Barkhof F, Denaxas S. Identifying and evaluating clinical subtypes of Alzheimer’s disease in care electronic health records using unsupervised machine learning. BMC Med Inform Decis Mak 2021 Dec 8;21(1):343.
  • 34 Kung B, Chiang M, Perera G, Pritchard M, Stewart R. Identifying subtypes of depression in clinician-annotated text: a retrospective cohort study. Sci Rep 2021 Nov 17;11(1):22426.
  • 35 Pencina MJ, Goldstein BA, D’Agostino RB. Prediction Models - Development, Evaluation, and Clinical Application. N Engl J Med 2020 Apr 23;382(17):1583-6.
  • 36 Rattsev I, Flaks-Manov N, Jelin AC, Bai J, Taylor CO. Recurrent preterm birth risk assessment for two delivery subtypes: A multivariable analysis. J Am Med Inform Assoc 2022 Jan 12;29(2):306-20.
  • 37 Feng J, Lee J, Vesoulis ZA, Li F. Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data. NPJ Digit Med 2021 Jul 14;4(1):108.
  • 38 Zhao J, Feng Q, Wu P, Lupu RA, Wilke RA, Wells QS, et al. Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction. Sci Rep 2019 Jan 24;9(1):717.
  • 39 Osborne TF, Veigulis ZP, Arreola DM, Röösli E, Curtin CM. Automated EHR score to predict COVID-19 outcomes at US Department of Veterans Affairs. PLoS One 2020 Jul 27;15(7):e0236554.
  • 40 Lauritsen SM, Kristensen M, Olsen MV, Larsen MS, Lauritsen KM, Jørgensen MJ, et al. Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nat Commun 2020 Jul 31;11(1):3852.
  • 41 Boland MR, Davidson LM, Canelón SP, Meeker J, Penning T, Holmes JH, et al. Harnessing electronic health records to study emerging environmental disasters: a proof of concept with perfluoroalkyl substances (PFAS). NPJ Digit Med 2021 Aug 11;4(1):122.
  • 42 Song X, Yu ASL, Kellum JA, Waitman LR, Matheny ME, Simpson SQ, et al. Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction. Nat Commun 2020 Nov 9;11(1):5668.
  • 43 Tomašev N, Harris N, Baur S, Mottram A, Glorot X, Rae JW, et al. Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records. Nat Protoc 2021 Jun;16(6):2765-87.
  • 44 Ramachandran A, Kumar A, Koenig H, De Unanue A, Sung C, Walsh J, et al. Predictive Analytics for Retention in Care in an Urban HIV Clinic. Sci Rep 2020 Apr 14;10(1):6421.
  • 45 Lee CK, Samad M, Hofer I, Cannesson M, Baldi P. Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality. NPJ Digit Med 2021 Jan 8;4(1):8.
  • 46 Wouters OJ, McKee M, Luyten J. Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018. JAMA 2020 Mar 3;323(9):844-53.
  • 47 Zhou M, Zheng C, Xu R. Combining phenome-driven drug-target interaction prediction with patients’ electronic health records-based clinical corroboration toward drug discovery. Bioinformatics 2020 Jul 1;36(Suppl_1):i436-i444.
  • 48 Oskotsky T, Maric I, Tang A, Oskotsky B, Wong RJ, Aghaeepour N, et al. Mortality Risk Among Patients With COVID-19 Prescribed Selective Serotonin Reuptake Inhibitor Antidepressants. JAMA Netw Open 2021 Nov 1;4(11):e2133090.
  • 49 MacLeod AR, Peckham N, Serrancolí G, Rombach I, Hourigan P, Mandalia VI, et al. Personalised high tibial osteotomy has mechanical safety equivalent to generic device in a case-control in silico clinical trial. Commun Med (Lond) 2021 Jun 30;1:6.
  • 50 Hong JC, Spiegel DY, Havrilesky LJ, Chino JP. High-volume providers and brachytherapy practice: A Medicare provider utilization and payment analysis. Brachytherapy 2018 Nov-Dec;17(6):906-11.
  • 51 Braunlin M, Belani R, Buchanan J, Wheeling T, Kim C. Trends in the multiple myeloma treatment landscape and survival: a U.S. analysis using 2011-2019 oncology clinic electronic health record data. Leuk Lymphoma 2021 Feb;62(2):377-86.
  • 52 Hong JC, Eclov NCW, Dalal NH, Thomas SM, Stephens SJ, Malicki M, et al. System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation. J Clin Oncol 2020 Nov 1;38(31):3652-61.
  • 53 Escobar GJ, Liu VX, Schuler A, Lawson B, Greene JD, Kipnis P. Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration. N Engl J Med 2020 Nov 12;383(20):1951-60.
  • 54 Muse ED, Topol EJ. Guiding ultrasound image capture with artificial intelligence. Lancet 2020 Sep 12;396(10253):749.
  • 55 Reeves JJ, Hollandsworth HM, Torriani FJ, Taplitz R, Abeles S, Tai-Seale M, et al. Rapid response to COVID-19: health informatics support for outbreak management in an academic health system. J Am Med Inform Assoc 2020 Jun 1;27(6):853-9.
  • 56 Adler-Milstein J, Chen JH, Dhaliwal G. Next-Generation Artificial Intelligence for Diagnosis: From Predicting Diagnostic Labels to “Wayfinding”. JAMA 2021 Dec 28;326(24):2467-8.
  • 57 Carcel C, Harris K, Peters SAE, Sandset EC, Balicki G, Bushnell CD, et al. Representation of Women in Stroke Clinical Trials: A Review of 281 Trials Involving More Than 500,000 Participants. Neurology 2021 Nov 2;97(18):e1768-e1774.
  • 58 Unger JM, Hershman DL, Osarogiagbon RU, Gothwal A, Anand S, Dasari A, et al. Representativeness of Black Patients in Cancer Clinical Trials Sponsored by the National Cancer Institute Compared With Pharmaceutical Companies. JNCI Cancer Spectr. 2020 Apr 24;4(4):pkaa034.
  • 59 Awad E, Paladugu R, Jones N, Pierce JY, Scalici J, Hamilton CA, et al. Minority participation in phase 1 gynecologic oncology clinical trials: Three decades of inequity. Gynecol Oncol 2020 Jun;157(3):729-32.
  • 60 Trant AA, Walz L, Allen W, DeJesus J, Hatzis C, Silber A. Increasing accrual of minority patients in breast cancer clinical trials. Breast Cancer Res Treat 2020 Nov;184(2):499-505.
  • 61 Andrasik MP, Broder GB, Wallace SE, Chaturvedi R, Michael NL, Bock S, et al. Increasing Black, Indigenous and People of Color participation in clinical trials through community engagement and recruitment goal establishment. PLoS One 2021 Oct 19;16(10):e0258858.
  • 62 Jin X, Chandramouli C, Allocco B, Gong E, Lam CSP, Yan LL. Women’s Participation in Cardiovascular Clinical Trials From 2010 to 2017. Circulation 2020 Feb 18;141(7):540-8.
  • 63 Mendis SR, Anand S, Dasari A, Unger JM, Gothwal A, Ellis LM, et al. Female representation in clinical trials leading to FDA cancer drug approvals for gastrointestinal (GI) cancers between 2008 to 2018. J Clin Oncol 2020;38:809-809.
  • 64 Martinkova J, Quevenco FC, Karcher H, Ferrari A, Sandset EC, Szoeke C, et al. Proportion of Women and Reporting of Outcomes by Sex in Clinical Trials for Alzheimer Disease: A Systematic Review and Meta-analysis. JAMA Netw Open 2021 Sep 1;4(9):e2124124.
  • 65 Baker KE, Streed CG Jr, Durso LE. Ensuring That LGBTQI+ People Count - Collecting Data on Sexual Orientation, Gender Identity, and Intersex Status. N Engl J Med 2021 Apr 1;384(13):1184-6.
  • 66 Keuroghlian AS. Electronic health records as an equity tool for LGBTQIA+ people. Nat Med 2021 Dec;27(12):2071-3.
  • 67 Vyas DA, Eisenstein LG, Jones DS. Hidden in Plain Sight - Reconsidering the Use of Race Correction in Clinical Algorithms. N Engl J Med 2020 Aug 27;383(9):874-82.
  • 68 Ye J. The Role of Health Technology and Informatics in a Global Public Health Emergency: Practices and Implications From the COVID-19 Pandemic. JMIR Med Inform 2020 Jul 14;8(7):e19866.
  • 69 The Lancet Digital Health. Contact tracing: digital health on the frontline. Lancet Digit Health 2020 Nov;2(11):e561.
  • 70 Jewell S, Futoma J, Hannah L, Miller AC, Foti NJ, Fox EB. It’s complicated: characterizing the time-varying relationship between cell phone mobility and COVID-19 spread in the US. NPJ Digit Med 2021 Oct 27;4(1):152.
  • 71 Badr HS, Du H, Marshall M, Dong E, Squire MM, Gardner LM. Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study. Lancet Infect Dis 2020 Nov;20(11):1247-54.
  • 72 Mishra T, Wang M, Metwally AA, Bogu GK, Brooks AW, Bahmani A, et al. Pre-symptomatic detection of COVID-19 from smartwatch data. Nat Biomed Eng 2020 Dec;4(12):1208-20.
  • 73 Dash TK, Mishra S, Panda G, Satapathy SC. Detection of COVID-19 from speech signal using bio-inspired based cepstral features. Pattern Recognit 2021 Sep;117:107999.
  • 74 Verde L, De Pietro G, Sannino G. Artificial Intelligence Techniques for the Non-invasive Detection of COVID-19 Through the Analysis of Voice Signals. Arab J Sci Eng 2021 Oct 8:1-11.
  • 75 Rykov Y, Thach TQ, Bojic I, Christopoulos G, Car J. Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling. JMIR Mhealth Uhealth 2021 Oct 25;9(10):e24872.
  • 76 Cavedoni S, Chirico A, Pedroli E, Cipresso P, Riva G. Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality. Front Hum Neurosci 2020 Jul 24;14:245.
  • 77 Sieberts SK, Schaff J, Duda M, Pataki BÁ, Sun M, Snyder P, et al. Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge. NPJ Digit Med 2021 Mar 19;4(1):53.
  • 78 Robin J, Harrison JE, Kaufman LD, Rudzicz F, Simpson W, Yancheva M. Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations. Digit Biomark 2020 Oct 19;4(3):99-108.
  • 79 Park C, Mishra R, Golledge J, Najafi B. Digital Biomarkers of Physical Frailty and Frailty Phenotypes Using Sensor-Based Physical Activity and Machine Learning. Sensors (Basel) 2021 Aug 5;21(16):5289.
  • 80 Hamza M, Alsma J, Kellett J, Brabrand M, Christensen EF, Cooksley T, et al. Can vital signs recorded in patients’ homes aid decision making in emergency care? A Scoping Review. Resusc Plus 2021 Jun;6:100116.
  • 81 Mohammadzadeh N, Gholamzadeh M, Saeedi S, Rezayi S. The application of wearable smart sensors for monitoring the vital signs of patients in epidemics: a systematic literature review. J Ambient Intell Humaniz Comput 2020 Nov 13:1-15.
  • 82 Leenen JPL, Leerentveld C, van Dijk JD, van Westreenen HL, Schoonhoven L, Patijn GA. Current Evidence for Continuous Vital Signs Monitoring by Wearable Wireless Devices in Hospitalized Adults: Systematic Review. J Med Internet Res 2020 Jun 17;22(6):e18636.
  • 83 Wedlund L, Kvedar J. Innovative new model predicts glucose levels without poking or prodding. NPJ Digit Med 2021 Aug 20;4(1):126.
  • 84 van den Brink W, Bloem R, Ananth A, Kanagasabapathi T, Amelink A, Bouwman J, et al. Digital Resilience Biomarkers for Personalized Health Maintenance and Disease Prevention. Front Digit Health 2021 Jan 22;2:614670.
  • 85 Capobianco E, Meroni PL. Value of digital biomarkers in precision medicine: implications in cancer, autoimmune diseases, and COVID-19. Expert Rev Precis Med Drug Dev 2021;6(4):235-8.
  • 86 Solomon DH, Rudin RS. Digital health technologies: opportunities and challenges in rheumatology. Nat Rev Rheumatol 2020 Sep;16(9):525-35.
  • 87 Onnela JP. Opportunities and challenges in the collection and analysis of digital phenotyping data. Neuropsychopharmacology 2021 Jan;46(1):45-54.
  • 88 Nahavandi D, Alizadehsani R, Khosravi A, Acharya UR. Application of artificial intelligence in wearable devices: Opportunities and challenges. Comput Methods Programs Biomed 2022 Jan;213:106541.
  • 89 Perez-Pozuelo I, Spathis D, Clifton EA, Mascolo C. Wearables, smartphones, and artificial intelligence for digital phenotyping and health. In: Digital Health. Elsevier; 2021. p.33-54.
  • 90 Lim SL, Ong KW, Johal J, Han CY, Yap QV, Chan YH, et al. Effect of a Smartphone App on Weight Change and Metabolic Outcomes in Asian Adults With Type 2 Diabetes: A Randomized Clinical Trial. JAMA Netw Open 2021 Jun 1;4(6):e2112417.
  • 91 Lau N, O’Daffer A, Yi-Frazier JP, Rosenberg AR. Popular Evidence-Based Commercial Mental Health Apps: Analysis of Engagement, Functionality, Aesthetics, and Information Quality. JMIR Mhealth Uhealth 2021 Jul 14;9(7):e29689.
  • 92 Tucker L, Villagomez AC, Krishnamurti T. Comprehensively addressing postpartum maternal health: a content and image review of commercially available mobile health apps. BMC Pregnancy Childbirth 2021 Apr 20;21(1):311.
  • 93 Khoong EC, Olazo K, Rivadeneira NA, Thatipelli S, Barr-Walker J, Fontil V, et al. Mobile health strategies for blood pressure self-management in urban populations with digital barriers: systematic review and meta-analyses. NPJ Digit Med 2021 Jul 22;4(1):114.
  • 94 Sharma R, Singh D, Gaur P, Joshi D. Intelligent automated drug administration and therapy: future of healthcare. Drug Deliv Transl Res 2021 Oct;11(5):1878-902.
  • 95 Eckardt JN, Wendt K, Bornhäuser M, Middeke JM. Reinforcement Learning for Precision Oncology. Cancers (Basel) 2021 Sep 15;13(18):4624.
  • 96 Domin A, Spruijt-Metz D, Theisen D, Ouzzahra Y, Vögele C. Smartphone-Based Interventions for Physical Activity Promotion: Scoping Review of the Evidence Over the Last 10 Years. JMIR Mhealth Uhealth 2021 Jul 21;9(7):e24308.
  • 97 Khan ZF, Alotaibi SR. Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective. J Healthc Eng 2020 Aug 30;2020:8894694.
  • 98 Jones M, DeRuyter F, Morris J. The Digital Health Revolution and People with Disabilities: Perspective from the United States. Int J Environ Res Public Health 2020 Jan 7;17(2):381.
  • 99 Busse M, Latchem-Hastings J, Button K, Poile V, Davies F, O’ Halloran R, et al. Web-based physical activity intervention for people with progressive multiple sclerosis: application of consensus-based intervention development guidance. BMJ Open 2021 Mar 16;11(3):e045378.
  • 100 de Batlle J, Massip M, Vargiu E, Nadal N, Fuentes A, Ortega Bravo M, et al; CONNECARE-Lleida Group. Implementing Mobile Health-Enabled Integrated Care for Complex Chronic Patients: Intervention Effectiveness and Cost-Effectiveness Study. JMIR Mhealth Uhealth 2021 Jan 14;9(1):e22135.
  • 101 Menictas M, Rabbi M, Klasnja P, Murphy S. Artificial intelligence decision-making in mobile health. Biochem (Lond) 2019 Oct;41(5):20-24.
  • 102 Smith B, Magnani JW. New technologies, new disparities: The intersection of electronic health and digital health literacy. Int J Cardiol 2019 Oct 1;292:280-2.
  • 103 Noel K, Ellison B. Inclusive innovation in telehealth. NPJ Digit Med 2020 Jun 25;3:89.
  • 104 Brewer LC, Fortuna KL, Jones C, Walker R, Hayes SN, Patten CA, et al. Back to the Future: Achieving Health Equity Through Health Informatics and Digital Health. JMIR Mhealth Uhealth 2020 Jan 14;8(1):e14512.
  • 105 Marwaha JS, Kvedar JC. Cultural adaptation: a framework for addressing an often-overlooked dimension of digital health accessibility. NPJ Digit Med 2021 Oct 1;4(1):143.
  • 106 Yoon H, Jang Y, Vaughan PW, Garcia M. Older Adults’ Internet Use for Health Information: Digital Divide by Race/Ethnicity and Socioeconomic Status. J Appl Gerontol 2020 Jan;39(1):105-10.
  • 107 Liu N, Yin J, Tan SS, Ngiam KY, Teo HH. Mobile health applications for older adults: a systematic review of interface and persuasive feature design. J Am Med Inform Assoc 2021 Oct 12;28(11):2483-501.
  • 108 Hilty DM, Gentry MT, McKean AJ, Cowan KE, Lim RF, Lu FG. Telehealth for rural diverse populations: telebehavioral and cultural competencies, clinical outcomes and administrative approaches. Mhealth 2020 Apr 5;6:20.
  • 109 Knitza J, Simon D, Lambrecht A, Raab C, Tascilar K, Hagen M, et al. Mobile Health Usage, Preferences, Barriers, and eHealth Literacy in Rheumatology: Patient Survey Study. JMIR Mhealth Uhealth 2020 Aug 12;8(8):e19661.
  • 110 Crawford A, Serhal E. Digital Health Equity and COVID-19: The Innovation Curve Cannot Reinforce the Social Gradient of Health. J Med Internet Res 2020 Jun 2;22(6):e19361.
  • 111 Hoffman DA. Increasing access to care: telehealth during COVID-19. J Law Biosci 2020 Jun 16;7(1):lsaa043.
  • 112 Yi SS, Ðoàn LN, Choi JK, Wong JA, Russo R, Chin M, et al. With No Data, There’s No Equity: Addressing the Lack of Data on COVID-19 for Asian American Communities. EClinicalMedicine 2021 Oct 23;41:101165.
  • 113 Bakken S. Replication studies and diversity, equity, and inclusion strategies are critical to advance the impact of biomedical and health informatics. J Am Med Inform Assoc 2021 Aug 13;28(9):1813-4.
  • 114 Sieck CJ, Sheon A, Ancker JS, Castek J, Callahan B, Siefer A. Digital inclusion as a social determinant of health. NPJ Digit Med 2021 Mar 17;4(1):52.
  • 115 Chen IY, Pierson E, Rose S, Joshi S, Ferryman K, Ghassemi M. Ethical Machine Learning in Healthcare. Annu Rev Biomed Data Sci 2021 Jul;4:123-44.
  • 116 Mhasawade V, Zhao Y, Chunara R. Machine learning and algorithmic fairness in public and population health. Nat Mach Intell 2021; 3(8):659-66.
  • 117 Lett E, Asabor E, Beltrán S, Cannon AM, Arah OA. Conceptualizing, Contextualizing, and Operationalizing Race in Quantitative Health Sciences Research. Ann Fam Med 2022 Mar-Apr;20(2):157-63.
  • 118 Miron M, Tolan S, Gómez E, Castillo C. Addressing multiple metrics of group fairness in data-driven decision making. arXiv preprint 2020; arXiv:2003.04794.
  • 119 Park Y, Hu J, Singh M, Sylla I, Dankwa-Mullan I, Koski E, Das AK. Comparison of Methods to Reduce Bias From Clinical Prediction Models of Postpartum Depression. JAMA Netw Open 2021 Apr 1;4(4):e213909.
  • 120 Wawira Gichoya J, McCoy LG, Celi LA, Ghassemi M. Equity in essence: a call for operationalising fairness in machine learning for healthcare. BMJ Health Care Inform 2021 Apr;28(1):e100289.
  • 121 Leslie D, Mazumder A, Peppin A, Wolters MK, Hagerty A. Does “AI” stand for augmenting inequality in the era of covid-19 healthcare? BMJ 2021 Mar 15;372:n304.
  • 122 Baxter MS, White A, Lahti M, Murto T, Evans J. Machine learning in a time of COVID-19 - Can machine learning support Community Health Workers (CHWs) in low and middle income countries (LMICs) in the new normal? J Glob Health 2021 Jan 16;11:03017.
  • 123 Syeda HB, Syed M, Sexton KW, Syed S, Begum S, Syed F, et al. Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review. JMIR Med Inform 2021 Jan 11;9(1):e23811.
  • 124 Ostaszewski M, Niarakis A, Mazein A, Kuperstein I, Phair R, Orta-Resendiz A, et al; COVID-19 Disease Map Community. COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms. Mol Syst Biol 2021 Oct;17(10):e10387.
  • 125 Dunn P, Hazzard E. Technology approaches to digital health literacy. Int J Cardiol 2019 Oct 15;293:294-6.
  • 126 Kemp E, Trigg J, Beatty L, Christensen C, Dhillon HM, Maeder A, 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 2021 Feb;32 Suppl 1:104-14.
  • 127 Kuek A, Hakkennes S. Healthcare staff digital literacy levels and their attitudes towards information systems. Health Informatics J 2020 Mar;26(1):592-612.
  • 128 Zhang X, Hailu B, Tabor DC, Gold R, Sayre MH, Sim I, et al. Role of Health Information Technology in Addressing Health Disparities: Patient, Clinician, and System Perspectives. Med Care 2019 Jun;57 Suppl 6 Suppl 2(Suppl 6 2):S115-S120.
  • 129 Triana AJ, Gusdorf RE, Shah KP, Horst SN. Technology Literacy as a Barrier to Telehealth During COVID-19. Telemed J E Health 2020 Sep;26(9):1118-9.