Digital Health and Artificial Intelligence 2024-2034: Trends, Opportunities, and Outlook
Telehealth, telemedicine, remote patient monitoring, consumer health wearables, digital therapeutics, & artificial intelligence in medical diagnostics
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Over the past decade, digital health and artificial intelligence (AI) have changed the delivery of healthcare. With more health data collected today than ever before, companies around the world are developing digital technologies, including AI, to leverage this wealth of information and deliver disruptive solutions for overburdened healthcare systems around the world.
This IDTechEx report on the digital health market provides an overview of the range of digital health technologies that have emerged over the past decade. It covers sectors including telehealth, remote patient monitoring (RPM), consumer health wearables, digital health apps, digital therapeutics, and the use of AI and machine learning in imaging diagnostics. With over five years of coverage in digital health and related topics such as wearable technologies, purchase of this IDTechEx report includes access to over 135 company profiles, a 10-year market forecast of RPM hardware, and a 40-year outlook for the adoption of AI and ML in computer aided diagnostics.
Healthcare is a convoluted and complex field, and digital health is no exception. Significant portions of the digital health market involve technologies and services that enable healthcare to be delivered outside of traditional clinical settings. This focus of digital health was particularly relevant during the COVID-19 pandemic - the need to deliver healthcare to people staying safe in their homes enabled the acceleration of telehealth uptake by over five years. In general, the decentralization of healthcare has been an ongoing trend even prior to the pandemic as a means to alleviate pressure on hospitals and clinics. Escalating healthcare costs, shrinking profit margins, and aging populations suffering from chronic health conditions are just several issues that digital health addresses for a multitude of stakeholders including patients, providers, and payers.
Telehealth and remote patient monitoring support patients with chronic health conditions
An increasingly tough burden faced by healthcare systems around the world today is the treatment of patients with chronic health conditions. Spanning both mental and physical health conditions, careful management of chronic conditions not only increases the quality of life for patients, but also reduces costly and harmful emergency hospitalizations. In this report, IDTechEx details the key contributions of telehealth, including and beyond the management of chronic health conditions. A deeper dive into RPM technologies is also provided, with a 10-year forecast across 19 types of connected medical devices that can be readily applied to RPM programs.
Wearable technology offers continuous monitoring, early diagnosis, and preventative healthcare
A contributor to remote patient monitoring programs is the advancement of wearable technologies. In addition to medical devices now becoming wearable, wearable consumer electronics are also moving towards healthcare applications. Over the past five years, consumer smartwatches have offered increasing health monitoring functionality, with several market leading products having received FDA clearance and CE marking for their diagnostic capabilities. These diagnostics have the potential to catch silent diseases such as hypertension and atrial fibrillation, leading to early intervention and the ability to provide preventative healthcare. Included in this report are roadmaps covering both wearable technology and wearable sensors in health monitoring.
The rise of software as a medical device
Over the past ten years, software has also emerged as a medical device, with key firsts such as the first digital therapeutic and first video game being approved by the FDA. An overview of digital health apps and digital therapeutics is provided in this report, as well as a deeper look into the role of digital health apps in diabetes management, one of the earliest adopters of digital health technology. This report also covers the rise of artificial intelligence in aiding physicians triage patients through analysis of medical images and includes a market penetration forecast covering 20 years from 2020 to 2040 over 13 different therapeutic areas.
Key aspects
This report provides a detailed overview of the digital health ecosystem including insights into the key trends, opportunities, and outlooks for several aspects of digital health, including:
- Trends in telehealth and telemedicine
- Key applications of remote patient monitoring, including a 10-year market forecast covering 19 categories of medical devices
- Health monitoring capabilities, consumer health wearables and roadmap for wearable health sensing
- Overview of digital therapeutics, including a deep dive into the range and adoption of digital health apps in diabetes management
- The potential of AI in medical imaging diagnostics, including a 20-year market penetration forecast covering 13 therapeutic areas
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| 1. | EXECUTIVE SUMMARY & CONCLUSIONS |
| 1.1. | The scope of digital health and AI in healthcare |
| 1.2. | Changing demographics require healthcare reforms |
| 1.3. | Factors encouraging the rise of digital health |
| 1.4. | Effective Use of Resources Enabling Cost Efficiency |
| 1.5. | The Future of Healthcare is Consumer-Led |
| 1.6. | Why telemedicine? |
| 1.7. | Driving the uptake of telemedicine |
| 1.8. | The Future for Telehealth and Telemedicine |
| 1.9. | Benefits of remote patient monitoring |
| 1.10. | RPM for hypertension (high blood pressure) |
| 1.11. | RPM of diabetes and prediabetes |
| 1.12. | Market Outlook: Hardware for Remote Patient Monitoring |
| 1.13. | From connected to wearable |
| 1.14. | Roadmap of wearable sensor technology segmented by key biometrics for health monitoring |
| 1.15. | Roadmap of wearable sensor technology segmented by key biometrics for health monitoring |
| 1.16. | Outlook and conclusions for wrist-worn wearables |
| 1.17. | Software as a medical device (SaMD) and Digital Therapeutics (DTx) |
| 1.18. | Mental health is a key focus for DTx |
| 1.19. | Outlook for digital therapeutics |
| 1.20. | Image recognition AI in medical imaging |
| 1.21. | Drivers & constraints of AI in medical imaging |
| 1.22. | Image recognition AI: market penetration 2020-2040 |
| 1.23. | Remaining challenges for AI in medical imaging |
| 1.24. | Global challenges in implementing digital health |
| 2. | INTRODUCTION |
| 2.1. | The scope of digital health and AI in healthcare |
| 2.2. | Digital health definitions |
| 2.3. | Digital health definitions |
| 2.4. | Changing demographics require healthcare reforms |
| 2.5. | Global healthcare spending is rising |
| 2.6. | Effective Use of Resources Enabling Cost Efficiency |
| 2.7. | Factors encouraging the rise of digital health |
| 2.8. | Health sensing at the point-of-care |
| 2.9. | From connected to wearable |
| 2.10. | Wellness and Prevention |
| 2.11. | Broadband access is a determinant of health |
| 2.12. | Role of venture capital |
| 2.13. | The impact of COVID-19 on digital health |
| 2.14. | Global challenges in implementing digital health |
| 3. | TELEHEALTH & TELEMEDICINE |
| 3.1. | Overview |
| 3.1.1. | Defining telehealth and telemedicine |
| 3.1.2. | Telehealth encompasses a range of services |
| 3.1.3. | There are numerous types of telemedicine |
| 3.1.4. | Use cases for telehealth and telemedicine |
| 3.1.5. | Benefits of telehealth and telemedicine |
| 3.1.6. | Challenges in telehealth and telemedicine |
| 3.1.7. | Is telehealth a cost-effective solution? |
| 3.1.8. | Why telemedicine? |
| 3.1.9. | The growing network of care |
| 3.1.10. | The rise of telemental health |
| 3.1.11. | Digital physical therapy |
| 3.1.12. | Doctors require better ways of communication |
| 3.1.13. | Driving the uptake of telemedicine |
| 3.1.14. | US telehealth reimbursement |
| 3.1.15. | Commercial progress |
| 3.1.16. | The outlook for telehealth and telemedicine |
| 3.2. | Remote Patient Monitoring |
| 3.2.1. | Remote patient monitoring |
| 3.2.2. | Benefits of remote patient monitoring |
| 3.2.3. | Components of a remote monitoring system |
| 3.2.4. | Common measurements and applications |
| 3.2.5. | Livongo by Teladoc Health |
| 3.2.6. | New form factors in vital sign monitoring |
| 3.2.7. | RPM for cardiovascular diseases |
| 3.2.8. | RPM for hypertension (high blood pressure) |
| 3.2.9. | Optical measurement techniques - cuff-less blood pressure monitoring |
| 3.2.10. | RPM for atrial fibrillation & other arrythmias |
| 3.2.11. | Detection: AliveCor ECG monitoring |
| 3.2.12. | Diagnosis: electronic skin patches and mobile cardiac telemetry |
| 3.2.13. | RPM of diabetes and prediabetes |
| 3.2.14. | RPM technologies for other chronic diseases |
| 3.2.15. | RPM reimbursement in the US |
| 3.2.16. | RPM in clinical trials |
| 3.2.17. | Remote monitoring of the elderly |
| 3.2.18. | Market Outlook: Hardware for Remote Patient Monitoring |
| 3.2.19. | Market Forecast 2024-2034: RPM Hardware |
| 3.2.20. | The outlook for remote patient monitoring |
| 4. | CONSUMER HEALTH WEARABLES |
| 4.1. | The Future of Healthcare is Consumer-Led |
| 4.2. | From connected to wearable |
| 4.3. | How can technology be made "wearable"? |
| 4.4. | The applications of wearables to health and wellness are wide ranging |
| 4.5. | What does wearable technology do? |
| 4.6. | What are the value proposition for wearables in consumer healthcare? |
| 4.7. | Drivers for wearable adoption and innovation in consumer health |
| 4.8. | Wearables: from wellness tools to medical devices |
| 4.9. | Wearable technology roadmap, segmented by function |
| 4.10. | Roadmap of wearable sensor technology segmented by key biometrics for health monitoring |
| 4.11. | Roadmap of wearable sensor technology segmented by key biometrics for health monitoring |
| 4.12. | Roadmap of market trends for wrist-worn wearables broken down by sector (consumer, sport, medical and enterprise) |
| 4.13. | Outlook and conclusions for wrist-worn wearables |
| 4.14. | (Early) Detection of Atrial Fibrillation |
| 4.15. | Smartwatches for AF detection |
| 4.16. | Wearables for health insurance |
| 4.17. | Wrist worn technology: Key players segmented by application |
| 4.18. | Factions of the healthcare market leverages the popularity of wrist-worn devices for 'BYOD' business models |
| 4.19. | Glucose, lactose and hydration monitoring for the consumer market |
| 4.20. | Consumer CGMs driven by metabolic health market |
| 4.21. | Wearable actuators as tools for the biohacking and longevity community |
| 4.22. | Future trends in consumer health wearable |
| 4.23. | How could AI add even more value to wearable data? |
| 5. | DIGITAL HEALTH APPS AND DIGITAL THERAPEUTICS |
| 5.1. | Overview |
| 5.1.1. | Digital therapeutics: app-based healthcare |
| 5.1.2. | Digital Therapeutics Alliance |
| 5.1.3. | Mental health is a key focus for DTx |
| 5.1.4. | Pear Therapeutics |
| 5.1.5. | Akili Interactive: EndeavorRx |
| 5.1.6. | Difficulties in realizing the potential of digital therapeutics |
| 5.2. | Diabetes Management Apps |
| 5.2.1. | Introduction to diabetes |
| 5.2.2. | Diabetes management ecosystem |
| 5.2.3. | The scope of digital health in diabetes management |
| 5.2.4. | Diabetes is an early adopter of digital health initiatives |
| 5.2.5. | Diabetes digital health landscape* |
| 5.2.6. | Diabetes Apps |
| 5.2.7. | Digital health is driven by increasing device connectivity |
| 5.2.8. | CGM integration with mobile apps |
| 5.2.9. | Ecosystem of a digital health program for diabetes |
| 5.2.10. | Telehealth programs in diabetes management |
| 5.2.11. | DTx for Diabetes: BlueStar and BlueStar Rx |
| 5.2.12. | Diabetes lifestyle apps: focal points |
| 5.2.13. | Addressing prediabetes via digital health |
| 5.2.14. | The outlook for digital health in diabetes |
| 6. | IMAGE RECOGNITION ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING |
| 6.1. | Image recognition AI in medical imaging |
| 6.2. | Drivers & constraints of AI in medical imaging |
| 6.3. | Benefits of using AI in medical imaging |
| 6.4. | Deep learning makes automated image recognition possible |
| 6.5. | Workings of CNNs: how are images processed? |
| 6.6. | AI in radiology dominates AI and ML-enabled medical devices |
| 6.7. | Image recognition AI: performance comparison by application |
| 6.8. | Image recognition AI for cancer detection: Key players |
| 6.9. | Cancer detection AI: state of development and market readiness |
| 6.10. | Cancer detection AI: conclusions and outlook |
| 6.11. | Cancer detection AI: conclusions and outlook (2) |
| 6.12. | Image recognition AI for cardiovascular disease: key players |
| 6.13. | CVD detection AI: conclusions and outlook |
| 6.14. | Image recognition AI for retinal disease: key players |
| 6.15. | Retinal diseases detection AI: conclusions and outlook |
| 6.16. | Retinal diseases detection AI: conclusions and outlook (2) |
| 6.17. | Image recognition AI: market penetration 2020-2040 |
| 6.18. | Business models: subscription vs pay per use |
| 6.19. | Remaining challenges |
| 6.20. | Opportunities for technological improvements |
| 7. | REGULATION OF DIGITAL HEALTH |
| 7.1. | Introduction |
| 7.2. | Telemedicine |
| 7.3. | Medical devices |
| 7.4. | Security risks for medical devices |
| 7.5. | Digital health software |
| 7.6. | Digital tools not under FDA review |
| 8. | COMPANY PROFILES |
| 8.1. | Abbott Diabetes Care |
| 8.2. | Afon Technology |
| 8.3. | Aktiia |
| 8.4. | Alertgy |
| 8.5. | AliveCor |
| 8.5.1. | AliveCor |
| 8.6. | AmCad BioMed |
| 8.6.1. | AmCad BioMed |
| 8.7. | Artelus |
| 8.8. | Arterys |
| 8.9. | Biobeat Technologies |
| 8.10. | BioIntelliSense |
| 8.11. | Bioness |
| 8.12. | Biosency |
| 8.13. | Biospectal |
| 8.14. | Bittium |
| 8.14.1. | Bittium |
| 8.15. | Blue Spark Technologies |
| 8.16. | Blumio |
| 8.17. | BodiGuide |
| 8.17.1. | BodiGuide |
| 8.18. | Bonbouton |
| 8.19. | Borsam |
| 8.20. | Brainomix |
| 8.21. | Breezometer |
| 8.22. | CardiacSense |
| 8.22.1. | CardiacSense |
| 8.23. | Cardiomo |
| 8.23.1. | Cardiomo |
| 8.24. | CarePredict |
| 8.24.1. | CarePredict |
| 8.25. | CASIS |
| 8.25.1. | CASIS |
| 8.26. | CEA Leti (Quantum Technologies) |
| 8.27. | Cerathrive |
| 8.28. | Cerca Magnetics |
| 8.29. | Chronolife |
| 8.29.1. | Chronolife |
| 8.29.2. | Chronolife |
| 8.30. | CIONIC |
| 8.31. | Circle Cardiovascular Imaging |
| 8.32. | CorTechs Labs |
| 8.33. | Cortrium |
| 8.34. | Cosinuss |
| 8.34.1. | Cosinuss |
| 8.34.2. | Cosinuss |
| 8.35. | CureMetrix |
| 8.36. | Dexcom |
| 8.36.1. | DexCom Inc |
| 8.36.2. | Dexcom |
| 8.37. | Diabeloop |
| 8.37.1. | DiabeLoop |
| 8.38. | Diabnext |
| 8.39. | DiaMonTech |
| 8.40. | Digital Diagnostics (formerly known as IDx) |
| 8.41. | DMS Service |
| 8.42. | EarlySense |
| 8.43. | EarSwitch |
| 8.44. | Enlitic |
| 8.45. | Epicore Biosystems |
| 8.45.1. | Epicore Biosystems |
| 8.46. | EPS BioTechnology Corp / EasyMax Diabetes Care |
| 8.47. | FibriCheck |
| 8.48. | Fitscript |
| 8.49. | GlucoRx |
| 8.50. | GlucoRx (BioXensor) |
| 8.51. | Health Care Originals |
| 8.52. | HeartFlow |
| 8.53. | Icometrix |
| 8.54. | IDUN Technologies |
| 8.55. | iHealth |
| 8.55.1. | iHealth Labs |
| 8.56. | Infervision |
| 8.57. | Infinovo |
| 8.58. | Intrasense |
| 8.59. | iRhythm |
| 8.60. | Isansys |
| 8.60.1. | Isansys Lifecare |
| 8.61. | iSchemaView |
| 8.62. | Kenzen |
| 8.63. | Know Labs |
| 8.64. | Life Science Technology |
| 8.65. | LifeSignals |
| 8.66. | Lunit |
| 8.67. | Medtronic Diabetes |
| 8.68. | Medtrum Technologies |
| 8.69. | MetaOptima |
| 8.70. | Mezoo |
| 8.70.1. | Mezoo |
| 8.71. | Miiskin |
| 8.72. | Monit |
| 8.73. | Nanowear |
| 8.74. | NeoSoft |
| 8.75. | Nomadeec |
| 8.76. | Omron |
| 8.76.1. | Omron |
| 8.76.2. | Omron |
| 8.77. | Optellum |
| 8.78. | Optimize Health |
| 8.79. | Orpyx |
| 8.80. | Oviva |
| 8.81. | Owlet |
| 8.81.1. | Owlet |
| 8.82. | Oxitone |
| 8.83. | Oxitone Medical |
| 8.84. | Philips: BioTelemetry |
| 8.84.1. | Philips |
| 8.85. | PKVitality |
| 8.85.1. | PKvitality |
| 8.86. | Pr3vent |
| 8.87. | Preventice Solutions |
| 8.88. | Propeller Health |
| 8.89. | Proteus Digital Health |
| 8.90. | Qardio |
| 8.91. | QT Medical |
| 8.91.1. | QT Medical |
| 8.92. | Quad Industries |
| 8.92.1. | Quad Industries |
| 8.93. | Quantib |
| 8.94. | Qure.ai |
| 8.95. | Raiing Medical Company |
| 8.96. | Remedee Labs |
| 8.96.1. | Remedee Labs |
| 8.97. | Respiri |
| 8.98. | Rhaeos Inc |
| 8.99. | Rogalife |
| 8.100. | Rooti |
| 8.101. | RSP Systems |
| 8.101.1. | RSP Systems |
| 8.102. | ScottCare Cardiovascular Solution |
| 8.103. | Seers Technology |
| 8.103.1. | Seers Technology |
| 8.104. | SemanticMD |
| 8.105. | Sensoria |
| 8.106. | Sigknow |
| 8.106.1. | Sigknow |
| 8.107. | SigTuple |
| 8.108. | SkinVision |
| 8.109. | Smardii |
| 8.110. | SNAQ |
| 8.111. | Somatix |
| 8.112. | Spire |
| 8.113. | SteadySense |
| 8.114. | SWMedical |
| 8.115. | The Surgical Company |
| 8.116. | Therapixel |
| 8.117. | Tidepool |
| 8.118. | toSense |
| 8.119. | Tytocare |
| 8.119.1. | Tyto Care |
| 8.119.2. | Tytocare |
| 8.120. | UnitedHealthcare |
| 8.121. | Valencell |
| 8.121.1. | Valencell |
| 8.121.2. | Valencell |
| 8.122. | Vitality |
| 8.123. | Vivalink |
| 8.124. | Viz.ai |
| 8.125. | VUNO |
| 8.126. | Walk With Path |
| 8.127. | Withings (U-Scan) |
| 8.127.1. | Withings |
| 8.127.2. | Withings Health Solutions |
| 8.127.3. | Withings Health Solutions |
| 8.128. | Xandar Kardian |
| 8.129. | Ypsomed |
| 8.130. | Zebra |
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Digital health and AI in healthcare driven by the exponential growth of health data generation
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| Companies | Over 135 |
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