Artificial Intelligence in Healthcare
The year 2019 has been an incredible year in terms of implementation and usage of Artificial Intelligence (AI). Artificial Intelligence is the ability of computer programs to perform tasks that normally require human intelligence such as decision making, high-level computations, object detection, solving complex problems, increased accuracy and so on. Since the emergence of Artificial Intelligence in the 1950s, it’s been impacting various domains, including Robotics, Digital Marketing, Finance, Natural Language Processing, Neural Networks, Gaming Industry, and even the Musical Arts. The official idea and definition of AI were first coined by Jay McCarty in 1955 at the Dartmouth Conference.
Here’s what McCarty said then about AI “Every aspect of learning or any other feature of intelligence be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve kinds of problems now reserved form humans, and improve themselves.”
Gone are the days when Artificial Intelligence was considered only science fiction. We are increasingly seeing the tangible, real-world impact of AI across various industries. AI is revolutionizing marketing in many ways. For example, when combined with Natural Language Processing, AI allows the chatbot application to understand human speech, including discerning intent. Chatbots can not only collect pre-sales from prospects but also gather hot leads.
However, the most powerful impact of Artificial Intelligence has been in the field of healthcare. AI is progressively changing the world of medical practice and is estimated to contribute over $ 15 trillion to the world economy by the year 2030. This makes it pretty evident that healthcare is using Artificial Intelligence in a more advanced manner.
The Techniques Used in AI: Machine Learning and Deep Learning
- Machine Learning is an application of Artificial Intelligence that provides machines the ability to learn from their experiences and improve themselves in terms of prediction and accuracy without being explicitly programmed.
- Deep learning is another application of Machine Learning that uses complex algorithms and deep neural nets to train a model, and allows the machine to learn feature hierarchies based on the concept of artificial neural networks.
The deep learning technique helps in early detection of cancer cells and tumours, improves the time consuming process of synthesizing new drugs, invents sophisticated medical instruments and can also predict earthquakes, create traffic alerts, social media platforms, help with google translate, filter spam and malware, and much more.
Reasons that led to Sudden Growth of AI in the Healthcare Industry
Let us narrow down to two major reasons which contributed to the AI being so impactful in the field of Healthcare. They are:
- The first reason is the easy and voluminous availability of Medical Data. In today’s world, all of us have tons and tons of medical data in the form of medical history wherein details related to our health is written down in accounts in hospitals. So basically with the availability of data, implementing AI became much easier and accurate. AI is based on technologies such as Deep Learning and Machine Learning which require tons and tons of data.
- Another important reason that led to the development of Artificial Intelligence in healthcare is the Introduction/Development of Complex Algorithms. Leveraging Machine Learning (ML) alone is not enough to handle, process and make sense of high dimensional medical data, that is, data which is very vast and has thousands and thousands of attributes. So to process and analyze data of this dimension with Machine Learning alone was a challenge but as soon as Deep Learning and Neural networks were introduced this became much easier because Deep Learning and Neural networks focus on solving complex problems that involve high dimensional data.
Use Cases of AI in Healthcare
There are many cases of how AI in Healthcare has helped patients all over the world. Some of those are discussed below:
- AI is changing the business with its applications in decision support, patient triage and image analysis. AI helps with the issue of physician burnout by collecting patient’s data via an app, text messaging, or chatbots, saving time and money for both the patient and the provider.
- Machine Learning diagnoses conditions through imaging crowd source medical data and even suggests treatments and steps in drug development. This becomes crucially important when diagnosis and treatment depends on quick and accurate interpretation of MRIs, CT scans and X-rays to identify tumors, fractures, and medical conditions.
- With advancement in Deep Learning driven by invidious powerful GPU accelerators, the healthcare industry is heading towards even more sophisticated applications, such as personalized medicine, wearable medical devices, and automated robotic surgery.
- Researchers are using deep learning to compare patients with a broader population predicting heart conditions and cancerous genetic mutations even before they occur.
- AI has made Melanoma detection easier with its ability to distinguish benign from malignant moles. The computer program effectively analyzes the patterns of various skin conditions.
Future Scope of AI in Healthcare
Although there are many proven approaches of AI in the healthcare domain, the extent of accomplishment is still at a nascent stage. It is expected that an increase in the adoption of AI, would result in transformational change in the healthcare industry.
Few of the sectors where AI will grow shortly are:
- Around 400 million people are globally suffering from diabetes as per the data from National Health surveys. The major challenge of detecting Diabetic Retinopathy (DR) in patients can be resolved with the help of AI, which enables the physicians to gather images of the patient’s retina and run these images through machines. The signs discovered can assist in enhanced early diagnosis and the prevention of the occurrence of DR among diabetic patients.
- Based on the report published by WHO, there is an estimated shortage of about 17.4 million healthcare workers and there is availability of only 4.45 skilled health professionals per 100 people globally. The use of AI-based platforms would enhance the doctor’s efficiency, deliver better results and minimize errors by providing advice on treatments based on patient’s records with similar symptoms.
- Delivering healthcare facilities in rural and underdeveloped areas is still a major challenge for the health sector. Implementation of AI can help deliver therapeutic knowledge, predictive technology, and diagnostic facilities, freeing up manpower and other health resources for deployment to rural areas.
AI has been a boon to the healthcare industry and has the ability to change the way we live our life. Although AI has innumerable applications in healthcare technologies, we are still facing obstacles in our real-life implementation. In future, AI will have a larger role to play in reducing the problems that marketers face today and will help them in increasing their revenues. With the help of AI, we can overcome the problems and increase productivity from the marketing spends.
Thus, it won’t be wrong to say that in its short existence, the healthcare industry has quickly taken advantage of Artificial Intelligence, Machine Learning, and Deep Learning. Medical applications are leveraging these technologies to accelerate and improve patient’s experience. Hence, Automation, Cloud Technology and streamlined IT have become more important than ever before. Thanks to the AI integration working smarter, we have solutions to a variety of issues for patients, hospitals and the healthcare industry.
With the advancement made so far, we expect the rapid growth of AI to continue in 2020 as companies strive to get the maximum meaning and competitive advantage from the data they capture. The ultimate goal of working on AI is to solve the majority of the problems which we humans directly can’t accomplish.