How internet of things and artificial intelligence is changing the face of rural healthcare

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This will alert our moderators to take action. Nifty 18, Macrotech Developers Ltd. Market Watch. ET NOW. Auto Auto News. Auto Components. Products Durables. Energy Power. Services Advertising. The Economic Times daily newspaper is available online now. Read Today's Paper. How Internet of Things and Artificial Intelligence is changing the face of rural healthcare. Rate Story. Font Size Abc Small. Abc Medium. Abc Large. Nevertheless, medical errors continue to be the third leading cause of death in the United States and accounts for ten percent of all mortalities in the country.

Additionally, the cost of healthcare remains high and Furthermore, attention needs to be steered away from merely treating acute illnesses and focused on preventative medicine that improves quality of life and keeps individuals and families out of the hospital. Solutions are plentiful and the recent global health crisis has shed light on the importance of investing heavily in technology. Telehealth has emerged as an essential tool in remotely diagnosing, monitoring, and triaging patients for COVID at a fraction of what an emergency room visit may cost.

And this can be done conveniently from the confines of the home environment without making the dreaded visit to the hospital. This innovation is inspired by Artificial Intelligence and in the era of Alexa and Google Home, it is refreshing to envision what the future has in store for healthcare. But what is Artificial intelligence? We are in the era of the Internet of things IoT and Artificial intelligence may define this century. This form of technology is what makes it possible to stream TV shows on Netflix while commanding Siri to provide you with the most current weather report.

This process focuses on building computer algorithms that can methodically learn, improve, and make decisions through experience without necessarily being programmed.

Furthermore, machine learning has key elements which include deep learning. Deep learning utilizes networks that are built on the premise of the neural network and neural activities in the brain.

In , McCulloch and Pitts took the initiative and began the early stages of the development of the perceptron, which represents the most fundamental unit of deep neural networks Kawaguchi, By imitating the functioning capacity of the human brain, it utilizes artificial neural networks to learn and develop solutions to complex and challenging problems that are beyond the limitations of the human brain.

This piece of ingenuity is present in various forms of technology and brings into reality ideas such as robotic surgery. There is a plethora of instances of this level of technology in healthcare. An example is the federally funded Undiagnosed Disease Network, that operates with private medical universities like Harvard and Stanford University to diagnose and treat rare diseases.

Developers at Google Health utilized deep learning to create a model capable of diagnosing breast cancer after analyzing an enormous amount of data from the United States and the United Kingdom. This study involved information gathered from over 28, women across both countries. Surprisingly, the system was able to learn and detect breast cancer with 5. Based on the outcomes indicated, fully integrating this system into clinical practice has enormous potential in reducing misdiagnosis and medical errors as most of the breast cancers identified by the Artificial intelligence model in the Stanford University study were invasive.

In areas with poorly equipped health care systems, AI can play an important role in bringing affordable healthcare to the doorsteps of individuals. Today, smart phones have the capacity to be equipped with electrocardiogram and ultrasound functionalities that can be utilized in impoverished areas for diagnostic purposes.

However, what are the implications for medical providers? With so much optimism, it is easy to imagine a future where machines will completely replace providers and our hospitals equipped with devices that can diagnose and recommend treatments within hours or maybe minutes. While a fully automated healthcare future may not be available yet, it is imperative to recognize the essence of embracing artificial intelligence as an important member of the healthcare interdisciplinary team.

This important tool will be useful in streamlining processes so that providers can function more efficiently and effectively.

As healthcare costs are reduced, clinicians will be afforded valuable time to be present for the patient. There are grave implications if providers are not equipped with the educational curricula necessary to spearhead the incorporation of Artificial Intelligence in the healthcare system.

Additionally, healthcare providers need to adapt in a dynamic healthcare system to harness the full practicability of Artificial Intelligence. It is important to allay the fears of providers that are slow to adjust and concerned about the prospects of artificial intelligence completely replacing them.

For now, providers who are uncertain about the prospects of Artificial Intelligence taking over the role of their practice can be at ease and reap the benefits of a harmonious relationship that can only benefit the patient.

Our world is evolving, and the novel coronavirus continues to alter the future of healthcare as we know it. The post-coronavirus era provides an opportunity to focus on closing the healthcare disparities that exist and limiting the human-factor in medical errors. It provides an opportunity to fully harness the potential of artificial intelligence to solve complex medical mysteries that plague humanity and increase the efficiency of breast cancer screening.

It provides an avenue to fully embrace artificial intelligence as an integral part of the healthcare team. Read the Latest Edition. Abbasi, J. Diagnostic Error s. Blease, C. Journal of medical Internet research , 21 3 , e What is Artificial Intelligence?

How Does AI Work? General Electric. GE Healthcare. Kawaguchi, K. The University of Texas at El Paso. Koontz, C. Doctors, data, and diseases: How AI is transforming health care. Venture Beat.

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Faster change to treatments based on faster diagnosis of changing conditions can ultimately deliver a lower cost of care, enhanced quality of care and improvement in patient engagement. Beyond the patient, the IoT is also at the forefront of improving inventory management. Using RFID technology, hospitals and equipment rental companies can manage and track their inventories of medical equipment, as well as other supplies such as drugs and disposables. Again, this strategy will result in lower inventory carrying costs and overall variable costs across healthcare bodies, as well as reducing waste, as many un-returned or misplaced items simply end up in landfill.

Advances in connectivity and a growing demand for at-home and out-patient care is also driving the use of devices that can remotely monitor a patient. Private medial insurers are already using two-way smartphone apps to connect customers with GPs for initial consultation and diagnosis, as a lower-cost option to avoid an unnecessary in-person consultation for a minor ailment.

The next steps for this is to be able to share vitals with the healthcare practitioner in real-time, enabling multi-faceted telehealth services using inexpensive devices to interface users together and transmit the data from connected devices such as pacemakers, monitors, ECG machines and other devices. Furthermore, this ability to share vital health data in real-time can also be used by healthcare insurers to make more informed risk assessments when determining insurance premiums and incentivising customers to be healthier.

With so much healthcare data already digital, and more being added thanks to the growing use of connected devices and AI systems to inform decision-making, there are obvious user and organisational concerns about data security. In particular, whether data can be intercepted in-flight, and whether devices can be hacked and manipulated, creating an immediate life-threatening risk to the patient.

As we are in the infancy of digital healthcare, single industry-wide standards have yet to be established. There is not a consistent communication protocol like EDI or HL7 that is governing these devices and the information they collect and broadcast, yet. In addition to this, the collection of data will require tighter regulation and uniform processes, else data is at risk of being retained in silos, limiting its value and potentially exposing the healthcare provider or other organisation to regulatory penalties.

For example, the forthcoming European Union General Data Protection Regulation GDPR carries significant financial penalties for the misuse of data, as well as for failure to find and purge data on request. The proliferation of IoT and other data driving digital healthcare initiatives could fall foul of this if is not integrated into a single place, or at least a smaller, more manageable number of silos. Ultimately, the feed of IoT medical data into healthcare bodies and treatment processes is not enough on its own to effect meaningful change and to lower the cost of healthcare delivery.

In order to verify the data from IoT devices such as wearables, this fresh stream of real-time and near real-time data needs to be interpreted alongside other sources. These include historical patient record data, drug trial data, lifestyle and lifespan data for the given region and other environmental and societal variables. This is in order to gain the clearest view of the patient and the potential for future healthcare needs, as well as current issues.

Home Health Tech. By Sangita Singh, EVP, Healthcare and Life Sciences, Infosys The pace of digital change in the healthcare sector extends right the way across the spectrum — from the development of new medicines and treatments to the frontline delivery of emergency and outpatient care. Combining AI and the IoT in healthcare Critical to the digital transformation of modern healthcare has been the rise of the internet of things IoT , in regard to healthcare delivery and monitoring.

Keeping control of inventory Beyond the patient, the IoT is also at the forefront of improving inventory management. Remote data and accurate assessment Advances in connectivity and a growing demand for at-home and out-patient care is also driving the use of devices that can remotely monitor a patient.

Overcoming security concerns With so much healthcare data already digital, and more being added thanks to the growing use of connected devices and AI systems to inform decision-making, there are obvious user and organisational concerns about data security. Artificial intelligence AI is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning ML , deep learning DL , conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences.

Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things IoMT emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care POC devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management.

The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article.