Subscribe for Updates
Data labeling is a vital aspect of all Artificial Intelligence (AI) models. As essential as it is, it is also extremely time-consuming. In itself, data labeling is a process whereby data tagging, annotation, processing, and classification among other tasks. Data Labelling can be divided into high-quality data labeling and low-quality data labeling. High-quality data is accurate across all data sets and is close to the ground truth. Here, ground truth refers to data that is accurately representing real-world situations. Low-quality data on the other hand can negatively affect results. It can affect the system during model training and again when the model consumes the data which then informs its future decisions. This can be both a boon and a bane specifically for Artificial Intelligence in the healthcare industry.
Initially, AI systems in healthcare were designed keeping in mind the absence of perfect data, and these systems were required to build on the expertise of the physicians. At present, according to Global Markets Insight, AI in the healthcare market is being said to surpass USD 21 billion by 2026.
High-quality data labeling is the need of the hour for AI models. However, the creation, regulation, and maintenance of such data are some of the obstacles faced by the AI industry. If the amount of good quality data is increased, better inferences can be made by the AI models. And, besides, if the amount of good quality data is high then the amount of data required is less. But getting data labeling right the first time around is difficult if not impossible. Companies tend to outsource these data labeling activities and this often leads to the creation of more errors in data collection instead of less.
Despite this, AI is making advancements in the Healthcare industry. One of the main aims of AI in healthcare is to protect patient-sensitive information. The systems used are backed by AI by using large amounts of machine learning datasets. In healthcare, AI can be used as a tool to educate patients as well as to alert them about upcoming appointments These can be programmed in the form of personal assistants like Siri or Alexa. This process of automation in healthcare fields is seen in areas from drug development to gene sequencing. The ability of AI technology to streamline drug discovery procedures also saves a lot of time.
In recent years, AI has also been used to detect and prevent the spread of cancer in its early stages. According to a survey conducted by the American Cancer Society, a large number of mammograms yielded false results. This was a source of oftentimes undue stress for women. The use of AI is enabling a review of these mammograms with an accuracy rate of 99%.
Machine learning is improving medical diagnostics, predicting outcomes, and beginning to scratch the surface of personalized care. Existing AI technologies can already predict the outbreak of diseases. This is done using both real-time information as well as historical and geographical data collected over the years. Simultaneously, AI systems are being developed that focus more on prevention, personalization, and precision of individuals, instead of making the same medical decisions based on a few similar physical characteristics among patients.
Surgical robots have been used in medical procedures for over 30 years. These robots either aid a human surgeon or carry out the operation procedure by themselves. These surgical robots are also being used in hospitals to facilitate repetitive tasks.
With AI applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. While the data being shared between institutions is increasing, there is still not enough data to generate newer insights about complex ailments. And the data that has been collected over the years is also lacking in terms of diversity, socioeconomic status, and geographies of the world. Also, there is a chance that not all algorithms, much like the humans that created them, aren’t perfect. These algorithms need to be polished to generate the best results for the patients.
The goal of AI in healthcare has never been to replace the human medical team but to rather enhance their abilities to perform and serve people better by automating repetitive and often labor-intensive tasks.
ADVIT by Automaton AI is one such tool that can help regulate such medical data. ADVIT is a full-stack data labeling tool. It is a complete solution to all data labeling problems. And it can be customized according to use cases. With its high rate of accuracy, Advit is both extremely efficient and time-saving in the way that it works. Machine learning models can scan a patient’s medical reports and compare them with millions of similar reports which were previously assessed, in a matter of seconds. The model then offers a suggestive diagnosis for the patient’s medical conditions. Adit is also being used in the dental industry to detect root canals and dental caries. All of this is done quickly and efficiently, thereby saving a lot of time.
The use of Artificial Intelligence in the healthcare industry is promising. Harnessing patient data can lead to better diagnosis, help detect and possibly prevent diseases before they manifest, and support independent living for the elderly, amongst many other things. The advances in AI are likely to challenge the existing medical regulatory models and improve the quality of healthcare services the world over.