Honestly, we are living in the future we all dreamt of a couple of years back. If accurately predicting an occurrence or event was one of our primary intentions with technology decades ago, we are actually in that point of time where this idea is becoming a reality.
Today, devices as commercial as Apple Watches accurately predict heart attacks and cardiac concerns and alert users in advance so they could take precautions or get in touch with their doctors. Despite a viral disease devastating the plant, it is completely because of technology and its advancements that we have been able to quickly crack and develop the vaccine for it.
The healthcare industry is immensely benefited by technology – especially Artificial Intelligence. In this post, we will explore in detail how AI is shaping the future of health tech, its benefits, and the limitations associated with implementing AI effectively across hospitals, diagnostic centers, and other healthcare centers.
How relevant is AI to Healthcare?
The point of AI is to perform in a way a human could never. Advanced systems of today can perform exceptional computations really fast, enabling researchers and healthcare experts to leverage the potential of technology for research and development purposes. Besides, AI also has prescriptive and predictive capabilities, which can allow stakeholders to make decisions that are accurate, relevant, and most effective.
However, AI is a very generic term. To get a clear understanding of how relevant AI is, let’s break it down into different wings and understand the relevance of each with diverse healthcare segments.
Machine Learning, Deep Learning and Neural Networks
The act of making machines learn and the process of execution of tasks autonomously, machine learning and its allied technologies can be used to run simulations of drug combinations and in delivering precision healthcare treatments.
From predicting the onset of a hereditary disease in individuals to giving accurate results on the effectiveness of drugs in a human body, machine learning, deep learning, and neural networks can be deployed to work on concepts and topics that are currently beyond human reach.
Abbreviated as Natural Language Processing, this has everything to do with the processing of speech and text. AI modules are used to process and analyze speech and text for sentiments, translations, speech-to-text, and vice versa, and more. One of the standout ways NLP is relevant in healthcare is it can curate and process bulks of unstructured healthcare data such as reports, journals, EHRs, and even scientific papers and visualize inferences.
What sounds more like a deployment in warehouses and factories is actually incorporated in healthcare centers as well. Advanced physical robots are assisting surgeons of today in conducting precision-heavy invasive surgeries. Surgeries in sensitive organs of the human body such as the spinal cord, prostate, neck, and brain are conducted with the help of physical robots today.
RPA stands for Robotic Process Automation, where some of the most redundant tasks in healthcare centers and hospitals are automated for execution. This could be as simple as sending out appointment notifications or reminders to customers or as complex as updating patient billing or extracting data from unstructured sources.
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AI-centric Use Cases in Healthcare
To give you a simple idea of how swiftly healthcare chains are implementing AI into their systems and workflows, understand that the market value of AI in healthcare is anticipated to grow at a compounded rate of 41.8% within the next 7 years. The market value stood at around $6.7bn in the year 2020.
This only goes to show that the use cases of artificial intelligence in healthcare is only increasing. But what are they? Let’s find out.
- AI is used in the development of an interface between machines and the human brain. In terms of healthcare, this system is aimed at improving the quality of life of patients suffering from stroke, ALS, locked-in syndrome, or other irreversible neurological disorders. With such systems or assistive devices, patients can respond and communicate better.
- The current radiology tools require the need for a physical sample for diagnosis purposes. However, with AI implementations, advanced radiology tools are being developed that could predict or process samples from biopsies and other diagnostic entities for accurate information.
- Regardless of the advancements in healthcare, there are still corners of the world that are yet to see and experience primary healthcare and its benefits. AI incorporation can help take healthcare facilities to such regions and assist in elevating the life and lifestyle of people there.
- The role of AI in oncology is crucial and at the same time phenomenal. Sophisticated machine learning algorithms can help researchers accurately predict the onset of a malignant tumor or the time a benign could turn into a malignant one. From a preventive perspective, AI is also being used in the study and development of checkpoint inhibitors. Oncology is being studied extensively with the help of AI for more data and purpose-driven decision-making for diagnosis and treatments.
- AI is also used to track and tackle the epidemic of counterfeit medications and let patients be sure of the authenticity of the medications they consume on a daily basis.
While this is an exciting phase in the evolution of healthcare, there are tons of challenges in limitations in the space. The implementation of AI is not as easy as it sounds. It is futuristic and ambitious, yes!
However, its incorporation is also intricate. There are concerns such as data interoperability, security, advanced protocols, standards and compliances, data de-identification, and more. Not just that, the challenges start from the time you decide to develop an AI-powered healthcare solution as you would need tons of healthcare data to train your AI modules in the first place.
That’s where reliable companies like ours come into the picture. We are pioneering AI training data for the development of sophisticated healthcare systems that would be used across the globe for diverse purposes. For more on how you could get your AI training data for your project, reach out to us today.