Telemedicine: the pandemic driven boon in disguise for Healthcare AI


Artificial intelligence in healthcare is no longer confined to research labs. It has also enhanced several areas of telemedicine centered on broadband technology and electronic data to help and coordinate remote healthcare treatments. AI takes over the entire chain of clinical practice and patient-centered care by delivering care and sustenance models. AI may be beneficial in a variety of ways. While preventative care and telemedicine have traditionally been significant components of the healthcare business, they have become much more so in the context of the COVID-19 pandemic. Researchers are increasingly employing artificial intelligence to improve these ways of care delivery, which might lead to better patient outcomes. The wave of AI that changed it was the expansion of telehealth or telemedicine from counselling patients via video communication to managing patients remotely by using electronic data and broadcast communication technologies. Artificial intelligence in Telehealth enables long-distance health-related education, medical services, and patient health outcomes to be delivered under one roof.


Telemedicine and telehealth are concerned with technologically facilitating remote diagnostics and clinical care. It aids in the elimination of long hospital wait times and other administrative responsibilities. AI systems are enhancing health care by assisting physicians in making the best decisions for their patient’s treatment. According to Transparency Market Research, telehealth (or telemedicine) is a growing segment of the healthcare business that has slowly gained popularity and created a profitable sector. According to the market research organization, overall US sales will reach $19.5 billion in 2025, up from $6 billion in 2016.

Role of Artificial Intelligence:

AI and cloud-based technologies are driving data center digitalization, expansion, and transformation. Healthcare facilities with massive amounts of electronic health data are now supplementing healthcare services with the insights gained from it. According to the World Health Organization, telemedicine is accelerating innovation in teleradiology, telepathology, teledermatology, and telepsychiatry. 


As a result of this transition, a variety of suppliers and healthcare service providers have chosen to construct platforms to optimize their healthcare requirements for improved patient results. On-demand patient access is a critical practice of utilizing telehealth, i.e., Telehealth deployment allows patients to immediately reach the physician via the app. This lowered typical hospital waiting times and direct access to a specialized doctor, greatly increasing healthcare outcomes while lowering healthcare expenses.

AI-empowered Use cases in telehealth and telemedicine

Elder Assistance: 

With the elderly population constantly increasing, healthcare officials are beginning to brace themselves for the onslaught of issues that will surely engulf the whole business. To keep healthy, older persons require growing quantities of clinical care and social assistance, and in a system where resources are already stretched, this spike might place undue demand on care professionals. Organizations are beginning to seek the assistance of advanced Artificial Intelligence tools to better understand the requirements of older folks in order to overcome the challenges associated with an ageing population, particularly, artificial intelligence and data analytics technology.  These techniques have the potential to improve treatment for patients with Alzheimer’s disease, anticipate the risk of adverse events, and assist physicians in monitoring elders even when they are not in the doctor’s office. The usefulness of AI and data analytics technologies, on the other hand, is entirely dependent on their usability. While measuring utility can be difficult for any set of end users, it is especially tough for seniors. 

From digital chatbots to care assistance robots that conduct flawless operations. AI is helping diminish the barrier of communication and the need for the offline presence of a medical consultant to provide one on one consultation. Advances in Natural Language Understanding and Processing can be leveraged at scale to deliver pseudo one to one consultations by training language models to understand and comprehend the medical needs of the elderly people and use the analytics-driven from past records which in turn uses novel Computer Vision approaches to digitize the medical report, to suggest suitable prescriptions and consultations verified by a Doctor.

Remote Patient monitoring:

While we prefer to think of hospitals as secure havens, the unpleasant truth, especially in light of the coronavirus, is considerably grimmer. Even before Covid-19, it was discovered that the likelihood of spreading viruses and hospital acquired infections increased with the length of time patients stayed in hospitals, with hospital stays incurring a 17.6 percent risk of patient infection and increasing 1.6 percent with each additional day. The risk of infection has been heightened by Covid-19, whose acute hazard to both patients and medical staff has transformed hospitals into infection hotspots, confining high-risk patients to their homes and preventing them from obtaining necessary care. To reduce the risk of infection and provide medical personnel with accurate and dependable patient vital readings – both within and outside of the care environment – hospitals and healthcare institutions have turned to innovative remote patient monitoring equipment to follow patient vitals from a safe distance. These gadgets continually monitor a variety of patient vitals, including blood pressure, respiration rate, and heart rate, as well as motion, temperature, and sweat levels, giving medical professionals with accurate, up-to-date assessments of patient health.

This requirement is met by incorporating AI-powered patient analytics into remote patient monitoring systems, which stratify continuous patient data such that it is actionable. With these new capabilities, doctors may monitor thousands of patients simultaneously and rely on smart algorithms to triage and focus patient care to those who require it the most, optimizing hospital resources. AI-features such as early warning score systems, whose dependable algorithms are based on large data sets, aid in the early detection of potential deterioration, giving medical staff an advantage over Covid-19 and many other critical conditions and allowing them to take preventative action to improve patient outcomes.

TeleMedicine for hospitals:

Telemedicine reduces hospital waiting times, allowing hospitals to use AI and predictive data analytics technologies to rebuild their networking models in order to find experts more quickly. With telemedicine, healthcare facilities can not only identify ordinary instances, but also illnesses such as diabetic retinopathy and cancer in their early stages. 

Clinicians at the Los Angeles County Department of Health Services, for example, reduced visits to specialized care providers by more than 14,000 by introducing diabetic retinopathy telemedicine tests at its security net locations.

Every year, the incidence of cancer cases with brain blockage rises rapidly throughout the world. And, as a result of the COVID-19 epidemic, cancer patients face a significant problem with delayed therapy. While researchers in the United Kingdom and the United States used telemedicine with powerful AI and augmented reality to digitally diagnose patients. They also stated that AI chatbots and data analytics technologies aided in providing therapies and diagnosing patients at the appropriate moment.

AI-powered EHR and telehealth are expanding virtual medical meet-ups to educate patients about the full range of virtual healthcare benefits. Traditional hospital processes involve time-consuming administration activities that might be reduced by incorporating telemedicine into the core. Telehealth has advanced significantly in recent years, with AI applications now enhancing scale much beyond remote patient monitoring and administration. The industry-wide deployment of Telehealth or Telemedicine to save costs while increasing outcomes, and an overall increase in revenue.

Why Should You Prefer AI-Powered Smart Data Preparation in Building AI-enabled telemedicine and telehealth solutions?

Every computer vision, deep learning, and NLP model needs labeled training data. To get highly accurate labelled data an AI-based data annotation platform is best preferred over a manual annotation platform.

Manually annotating data can be a tedious task. Manual Data Labeling Challenges:

  1. Managing and maintaining the quality of data labelling
  2. Workforce management 
  3. Keeping a track of the cost incurred
  4. Compliance with data privacy requirements
  5. The task to ensure data security
  6. Read More on The Daunting Task of Manual Labeling in Retail and CPG and how Automation Helps

Read More: The Daunting Task of Manual Labeling in Retail and CPG and how Automation Helps

Labellerr – The Fastest and Smartest Data Annotation Platform

Labellerr provides you simple, feature-rich, affordable data annotation solution.

Why Choose Labellerr?

  1. Data Labelling at scale is an important concern for an organization since creating labels on large data sets by hand is often too slow and expensive. Labellerr solves this problem with their agile ML-Powered data annotation platform. 
  2. Work Quality and Worker productivity is difficult to track in the case of crowdsourcing and freelance data labeling services. Hence now with Labellerr’s marketplace, you can choose from our hand-picked and most trusted vendors to get data labeling tasks done.
  3. Domain and context capabilities specific to tasks are limited with workers on crowdsourcing platforms, contractors, and freelancers. 

So, if you wish your data annotation task to be automated and error-free then choose Labellerr. 


Benefits of Labellerr’s Data Annotation Platform:

  1. Label data at 10x speed using Labellerr’s ‘Auto Labeling’ feature
  2. Track work quality and worker productivity with a personalized dashboard experience.
  3. Get relieved from the hassle of reviewing each dataset, instead review only the ones having low confidence scores. 

The above is still just an indicative list, the superior benefit of adopting an automated way of labelling dataset is peace of mind and trust. The idea here is simple – let the machine do all the work for you so that your focus can be on your customers!

If you wish to try the power-packed machine learning embedded data annotation platform – Labellerr then just click here and explore.

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