Healthcare

The Role Of Data Collection And Annotation In Healthcare

What if we told you that the next time you took a selfie, your smartphone would predict that you are likely to develop acne in the next couple of days? Sounds intriguing, right? Well, that’s where we’re all collectively heading to.

The tech world is full of ambitions. Through our ideas, innovations, and goals, we are moving ahead as a society. This is especially true with respect to the evolution of healthcare AI, where some of the most plaguing concerns are being tackled and fixed with the help of technology.

Today, we are on the brink of rolling out machine learning models that can accurately predict the onset of hereditary diseases and the time a tumor would turn cancerous. We are working on prototypes for robot surgeons and VR-enabled training centers for doctors. Even at the operational levels, we have optimized bed and patient management, remote care, dispensing of medications, and more and automated tons of redundant tasks through AI-powered systems.

As we continue to keep dreaming of better ways to deliver healthcare, let’s explore and understand some of the key aspects in the evolution of healthcare and how technology, especially data science and its wings, is helping in this phenomenal growth.

This post is dedicated to bringing out the significance of data in the development of healthcare systems and modules, some prominent use cases, and the challenges stemming from the process.

The importance of Data in Healthcare AI

Now, before we begin to understand some of the more complex use cases and implementations of AI, let’s realize that the average healthcare and fitness apps you have on your phone are powered by AI modules. They have undergone years of training to accurately analyze, prescribe and infer your data and visualize it into insights.

The Importance Of Data In Healthcare Ai It could be your mHealth app that lets you virtually get consultations from a physician or book an appointment with them or an app that retrieves results on probable health concerns based on your symptoms and well-being, AI is embedded in every healthcare application today.

Scale this requirement further and you will have advanced systems that require data from multiple sources such as computer vision, electronic health records, and more to perform complex tasks. Remember the breakthroughs in oncology we mentioned earlier, such solutions require massive volumes of contextual data to produce accurate results. For this, annotators and experts have to source data from scans and reports such as X-Rays, MRIs, CT scans, and more and annotate every single element they see on them.

Healthcare professionals have to work on identifying different concerns and cases and label them so machines could understand them better and process more accurate results. So, all results, diagnoses, and treatment plans stem from data and the precise processing of it.

With data being at the heart of healthcare, let’s acknowledge that data is paving the way for a healthier tomorrow.

Let’s discuss your AI Training Data requirement today.

AI Use Cases in Healthcare

  • While we talk about advancements in surgical procedures and instruments, current AI systems prescribe whether surgeries are required in the first place. Through meticulous processing of data, systems can simulate instances and share whether concerns could be cured through medications and lifestyle changes.
  • AI also is helping us diagnose viral diseases through genomically sequenced pathogens and profiling.
  • Virtual nurses and assistants are also being developed to assist in patient care and lending support in their recovery process. During pandemics, when patient counts are high, virtual nurses could help organizations bring down operational expenses and simultaneously offer the care patients require. These digital nurses will be trained to execute all the fundamental tasks humans are trained to do.
  • Several neurological and autoimmune diseases that can never be cured or reversed could be predicted in advance through AI and machine learning models. Dementia, Alzheimer’s, Parkinson’s, and more could be eliminated this way.
  • Personalized treatment plans and medications are also possible with AI and access to electronic health records. By knowing a patient’s health history, allergies, chemical compatibility, and more, effective medications could be recommended by machines.
  • The discovery of new drugs could be fast-tracked through simulated clinical trials as well.

Challenges involved in developing AI Solutions for Healthcare

Challenges Involved In Developing Ai Solutions For Healthcare Regardless of the industry AI is implemented in, some challenges remain prominent and universal. This is true with respect to healthcare as well. To give you a quick idea, here are some of the most common challenges that limit AI advancements in healthcare:

  • The generation of consistent healthcare data is a challenge as machine learning models rely on the availability of massive amounts of datasets to learn to process inferences and deliver results.
  • The healthcare industry is bound by several laws, compliances, and protocols to maintain privacy and confidentiality standards. Data interoperability is inevitable and at the same time tedious because of the protocols that govern the fair sharing of data among stakeholders. Organizations have to take additional measures to protect the confidentiality of their patients and users through data de-identification.
  • The availability of healthcare SMEs is also a huge challenge. Data annotation is probably defining moment that influences ultimate outcomes. Because healthcare is a highly specialized wing, data from reports and scans have to be annotated by healthcare professionals. Recruiting them is a huge challenge.

So, this is the fundamental understanding you need to have of the healthcare industry and its AI-specific implementations. As we speak, tons of advancements are happening to fix some of the challenges we discussed. Newer use cases and challenges are also cropping up simultaneously. The only major takeaway here is that data will continue to shape healthcare outcomes and if you’re developing an AI solution, we recommend sourcing data from the experts like Shaip.

The difference it makes is unparalleled.

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