Digital tools for healthcare are rapidly taking hold in the post-pandemic era. Innovations in mobile apps, wearable sensors, and digital therapies have been approved on the fly by regulatory agencies around the world, culminating in an extremely patient-positive technology landscape, according to the Digital Health Trends 2021 report published this week by IQVIA. The number of digital health apps reached over 350,000 this year, with 90,000 new ones in 2020 alone. Moving away from the fitness tracking niche, they are becoming more versatile and already monitor varied individual health conditions, control various disease progressions, coordinate therapies, and link health plan management with insurers. The digital health market reached USD 24.7 billion in investments in 2020, a year in which disease management apps almost doubled in usage compared to 2015. The greatest emphasis of apps was on mental illness, diabetes, and cardiovascular tracking. The IQVIA study finds evidence of a positive impact on patient health with the continued use of certain apps and the growing inclusion of wearable devices and biomarkers in treatments. And, perhaps most interestingly, these devices have supported an increasing number of clinical trials and remote monitoring of patients in critical conditions. According to the report, to support clinical trials during the pandemic, 80% of companies have used telemedicine tools, while around 40% have used electronic consent applications and 30% home visits. Patient drug delivery was used by close to 25% of companies and wearable devices by less than 10%. But there is a certain virtuous circle in the use of health apps: the more people use them and the better the results obtained in various types of treatment, the greater the chance that doctors will recommend them and that new tools will be launched to cover treatment gaps. And the use of artificial intelligence in the early diagnosis of diseases has helped to give greater credibility to the technology in a world that a few years ago was skeptical of the adoption of any system. Like any machine learning system, which needs to be trained with real data before being tested and finally put into production to start predicting results, many of these apps and wearables collect several physical and even physiological variables to build a scenario that makes the prediction possible. Among devices, the most frequent collections are of heart rate, steps, distance traveled, and calories burned, derived mainly from wrist meters such as fitness trackers and smartwatches. But there is another range of variables collected through more than 300 sensors embedded in the devices, such as accelerometers, temperature meters, luminosity present in smartphones, even humidity, weight, UV light, and blood pressure. With the increasing use of AI for prevention, prediction, and health treatments, the World Health Organization recently released its first global report on Ethics and Governance of Artificial Intelligence for Health. After a group of 20 international experts analyzed for 2 years the opportunities and challenges AI presents for digital health, the organization published some public policies, principles, and recommended practices for the ethical and responsible use of AI in health, aiming at avoiding the incorrect, illegal, discriminatory and inhumane use of the technology. The WHO lists six basic ethical principles that should permeate the governance and regulation of AI in health: Protecting human autonomyPromoting human well-being and safety and the public interestEnsuring transparency, explainability and intelligibilityFostering responsibility and accountabilityEnsuring inclusiveness and equityPromoting AI that is responsive and sustainable In this sense, the use of devices to collect data and make predictions that will be analyzed by doctors is not out of principle, but care is needed so that the recommendations given by the apps are within reasonable parameters that cannot cause harm to the patient in case of any error. For example, the use of sensors has been applied to monitor functional domains by means of biological-digital markers, as in the control of neurological diseases. By tracking the use of a mobile device, they are able to capture hand and eye movements, voice tones, sleep patterns, and detect anxiety through task-altering functions and intensive use of certain apps. By gathering all this data, the AI system should be able to make recommendations to the doctor who, assessing the patient's situation, should decide on the intervention. The fact that clinical trials are increasingly being conducted with the help of digital health apps shows how intense the need for technological integration into the regulatory agencies' approval processes is. If in the past electrocardiograms were used to establish cardiac safety in the evaluation of new drugs, since 2015 glucose meters in endocrinological tests, spirometry in respiratory studies and blood pressure meters in the follow-up of cardiologic reactions, all of them in remote use, began to be applied more frequently. The IQVIA study points out as a trend during the Covid-19 pandemic the use of actigraphy to monitor activity and sleep patterns, and measurement of oxygenation, temperature, heart rate, blood pressure and even respiratory rate through wearable devices. What's next Technological advances must now come from the precision of sensors inside the instruments that are already used in digital medicine today, coupled to biofluid and drug application systems, as pointed out in a study by Flexible Electronics, which evaluated several types of equipment available in the market and others in adaptation and development phases. Precision Actigraphy is the name given to monitoring physical activity with millimetric precision, capturing the nuances of movements to detect degenerative processes such as Parkinson's or Alzheimer's disease. By measuring the speed of change in movement, angle and distance of steps, and arm swing range, the systems may be able to assess the severity of the disease and indicate more appropriate treatments. Through algorithms embedded in very sensitive microphone sensors, systems can identify specific acoustic biomarkers, which will be used to detect abnormalities in breathing, cough type, or pauses in speech, valuable information in respiratory or neurological clinical tests. Other systems embedded in smartphone cameras, for example, can detect behavioral or emotional changes through facial analysis, providing data for monitoring cognitive functions, depression, or even adverse events in cancer patients. And smaller, portable imaging devices may be used to remotely perform an ultrasound for various types of tests, or remote electroencephalography for patients with epilepsy or sleep disorders. Also in the test phase are wireless products that inject drugs into the body on demand, using acoustic waves, magnetic and electric fields, and even electromagnetic radiation to trigger mechanisms according to a predetermined schedule. A recent study in Nature Electronics has thoroughly evaluated the advantages and limitations of such devices, indicating design guidelines that can be applied in development. It remains to be seen whether the ethical principles of technology use will be added to those of cybersecurity and monitoring necessary to prevent external takeover and hacking of patients by unauthorized people.