|Role this area should play in the mHealth space|
|Artificial intelligence (AI) becomes more and more prominent in the delivery of healthcare. Mobile health is benefiting immensely from this technology as AI algorithms, sensor technology, and advanced data techniques are transforming the mobile devices into full-fledged health-management platforms. AI is being used to analysze large quantities of patient data, and increase the accuracy of disease detection. AI can enhance disease surveillance and it has the potential to improve the productivity of the healthcare professionals and to augment their knowledge and abilities to make better decisions. It has also great potential to accelerate operational processes of health institutions and reduce costs. Major areas supported by AI are Clinical Decision Support and Information management. The ubiquity of smart phones and mobile devices makes Ai powered medical decision apps accessible worldwide. These apps plays an important role in patient empowerment while enabling in the same time the professionals to make more personalized and effective decisions with positive outcomes for patients.
AI enables the mHealth to offer solutions for the following use cases:
Big data, the technology to aggregate and analyse huge amounts of data, has particular relevance to medical and health data. Healthcare Analytics has the potential in disease prevention by significantly reduce the cost of treatment. Advanced data mining and analysis techniques lead to better health outcomes by enabling healthcare professionals to better analyse a patient’s condition and self-care. AI empowers healthcare professionals to make more informed decisions and to treat patients more effectively.
|Current challenges and limitations|
|What benefit could this bring to adopters of this innovation?|
|It is widely accepted that the combination of mobile healthcare apps and artificial intelligence is the future of health care. mHealth can leverage the power of AI and Machine Learning to deliver health services for optimal, personalized and improved patient care. AI could improve availability of healthcare services, increase efficiency in the treatment process, reduce costs and increased opportunities for preventive care.
AI-powered digital health assistants integrated in chatbots and delivered through smartphones and other mobile devices can “transform mHealth apps from a simple static source of information into smart platforms for personalized pre-primary care and assisted self-care provision”.
Early adopters of AI and digital technologies will better and more successfully manage future health emergencies. Besides diagnosis and personalized medicine AI should be leveraged to optimize and streamline administrative health workers tasks and workflows.
ML coupled with wearables are opening a wide array of new possibilities for the mHealth systems. These can be further improved and expanded by incorporating new emerging technologies such as personal sensing. Additional use case that can be of interest to the aging population and increase their appetite for consuming AI powered mHealth services and apps are: detecting fall incidents, seizure episodes, emotional arousal (stress level and mood swings), and nutritional intake.
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|What is on the horizon?|
|Going forward, mHealth data will be a critical component of artificial intelligence tools and of the healthcare industry as a whole. mHealth, through mobile apps and devices will be an important source of big health and wellness data. Availability of these massive datasets could open new perspectives in the development of AI-based health and health care tools. More affordable sensors, rich real-time data, and powerful analysis tools are powering the next generation of mobile apps.
mHealth data, extracted from wearable devices and patient monitoring tools, will play a critical role in powering artificial intelligence and analytics technology in the future, according to a recent analysis from Frost & Sullivan.
“As mHealth rapidly gains traction, wearables, telehealth, social media, and patient engagement are expected to find adoption among more than half of the population in developed economies by 2025,” said Sowmya Rajagopalan, Advanced Medical Technologies Global Director.
“In the future, patient monitoring data will be combined with concurrent streams from numerous other sensors, as almost every life function will be monitored and its data captured and stored,”
The industry will also see a surge in wearables and biosensors, largely due to the rise of chronic disease and an industry shift from treatment to prevention. Continuous glucose monitors, pulse oximeters, and electrocardiogram monitors are some of the main tools that will dominate in the next few years. Smart prosthetics and smart implants, which help to measure key parameters to support monitoring and early intervention, will also likely be critical tools in the coming years.
“The patient monitoring market is expected to be worth more than $350 billion by 2025, as the focus is likely to move beyond device sales to solutions.”
The estimated increase in the global AI economy by 2022 is $3.9Tn from $1.2Tn in 2018. This increase can be attributed to machine learning tools and deep learning techniques. The spending in the healthcare industry alone is estimated to reach $36.1Bn in 2025 with a CAGR of 50.2%. It is predicted that the biggest investors in this technology would be hospitals and physicians as well as individual caregivers.
According to WEF these are 3 ways AI will change healthcare by 2030
1.- AI-powered predictive care.
“In 2030, the healthcare systems can anticipate when a person is at risk of developing a chronic disease, for example, and suggest preventative measures before they get worse. This development has been so successful that rates of diabetes, congestive heart failure and COPD (chronic obstructive heart disease), which are all strongly influenced by SDOH, are finally on the decline.”
2.- Networked hospitals, connected care to a single digital infrastructure . Locations connected through a supply demand balancing approach.
3.- Better patient and staff experiences, reduce admin burden. By learning from every patient, every diagnosis and every procedure, AI creates experiences that adapt to the professional and the patient. This not only improves health outcomes, but also reduces clinician shortages and burnout, while enabling the system to be financially sustainable.
|Artificial Intelligence, Machine Learning|