For a long time, we’ve been reading about the role of data annotation in machine learning and Artificial Intelligence (AI) modules. We know that quality data annotation is an inevitable aspect that invariably influences the outcomes produced by these systems.
However, what is the different annotation techniques used in the healthcare AI space? For an industry that is so complex, vast, and crucial, what measures and procedures do data annotation experts take to tag, implement and follow to tag critical healthcare data from a myriad of sources?
Well, this is exactly what we will explore in this post today. From the basic understanding of the different types of data annotation techniques, we are going to unlock level 2 and explore the different annotation techniques used in diverse AI use cases.
Data Annotation for different AI use cases
Let’s start with the basics first. Chatbots or conversational bots are proving to be highly efficient wings for clinical management, mHealth, and more. From helping patients book appointments for their diagnosis and healthcare consultation to assisting them in processing their symptoms and vitals for signs of diseases and concerns, chatbots are turning out to be great companions for both patients and healthcare providers.
For chatbots to provide accurate results, they have to process millions of bytes of data. One wrong diagnosis or recommendation could prove to be harmful to patients and their surroundings. For instance, if an AI-powered app designed to give results on Covid-19 preliminary assessment gives wrong results, it would result in contagion. That’s why adequate AI training has to happen before the product or solution is taken live.
For training purposes, experts generally use techniques like entity recognition and sentiment analysis.
Digital Imaging Annotation
While the diagnostic process is digital with the help of sophisticated systems and devices, the inferences of results are still predominantly human-centric. This exposes the results to misinterpretation, or even overlooking crucial concerns.
Now, AI modules can eliminate all such instances and can detect even the most minute anomalies or concerns from MRI, CT scan, and X-Ray reports. Apart from accurate results, AI systems can provide results swiftly as well.
Besides conventional scans, thermal imaging is also being used for the early detection of concerns like breast cancer. IR rays emitted by tumors are studied for further symptoms and reported accordingly.
For these complex purposes, data annotation veterans deploy mechanisms like tagging of existing MRI, CT scan and X-Ray reports, and thermal imaging data. AI modules then learn from these annotated datasets to train autonomously.
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Drug Development & Treatment
One of the most recent examples of drug development through AI modules is the formulation of vaccines for Covid-19. Within months of the outbreak, researchers and healthcare providers were able to crack the code for Covid-19 vaccines. This is predominantly due to AI and machine learning algorithms and their ability to simulate drug and chemical interactions, learn from tons of healthcare journals, published papers, research documents, scholarly articles, and more for drug discovery.
Insights that could have never come under the radar of humans (considering the volume of datasets being used for drug discovery and clinical trials) are easily matched and analyzed by AI modules for instant inferences and results. This enables healthcare professionals to fast-track trials, conduct rigorous tests and forward their findings for appropriate approvals.
Apart from drug discovery, AI modules are also assisting clinicians to recommend personalized drugs that would influence their dosage and timings based on their underlying conditions, biological responses, and more.
For patients suffering from autoimmune diseases, neurological concerns, and chronic ailments, multiple drugs are prescribed. This could mean a reaction among drugs. With personalized drug recommendations, healthcare providers can take a more informed decision with respect to prescribing medications.
For all these to happen, annotators work on tagging NLP data, data from data radiology, digital images, EHRs, claims data provided by insurance companies, data collected and compiled by wearable devices, and more.
Patient Monitoring & Care
The crucial road to recovery commences only after the surgery or diagnosis. It’s on the patient to take ownership of their health’s recovery and overall well-being. Thanks to AI-powered solutions, this is gradually becoming seamless.
Patients who have undergone treatments for cancer or those suffering from mental health concerns are increasingly finding conversational bots helpful. From post-discharge queries to helping patients navigate through emotional breakdowns, chatbots are arriving as ultimate companions and assistants. An AI organization called Northwell Health also shared a report that close to 96% of its patients demonstrated optimized patient engagement with such chatbots.
Annotation techniques in this boil down to tagging text and audio data from health records, data from clinical trials, conversation, and intent analyses, digital imaging and documents, and more.
Use cases like these are setting benchmarking standards for AI training and annotation methodologies. These also serve as road maps for all unique data annotation challenges that arise in the future because of the onset of newer use cases and solutions.
However, that shouldn’t stop you from venturing into the development of AI for healthcare. If you’re just starting out and are looking for adequate and quality AI training data, get in touch with us today. We’re always anticipating newer challenges and staying a step ahead of the curve.