The line between fiction and reality is blurring. Some years ago, driverless cars and drones delivering packages at our doorsteps would have seemed like science fiction. However, these and other new technological advances continue to astound us and make our lives easier. For instance, Dag Kittlaus, who created the virtual assistant Siri, recently showcased another artificial intelligence (AI) platform, Viv, at TechCrunch Disrupt, New York. Based on the demo shown, Viv seems to be a more sophisticated and powerful a virtual assistant. Whether Viv lives up to the hype waits to be seen. However, one thing is for sure—AI and machine learning are being increasingly applied to drive personalized engagement.
This evolution of technology is especially evident in the healthcare industry, where the focus is on improving patient health and providing personalized medicines at reduced cost. Earlier this year, Frost & Sullivan predicted that AI in healthcare is poised for dramatic market expansion in the next few years at a compound annual growth rate (CAGR) of 40%. This could be due to increased computing power and advances in data analytics and machine learning. AI and machine learning are gradually helping medical workers and machines to work in harmony, leading to enhanced and precise treatment—quite a disruptive change!
Take the case of London-based Your.md, a health management app which allows users to discuss their problems. The app asks users about their symptoms and personal information, among other questions, and then provides information on their illness. The company claims its app is the number one health app in more than 40 countries. It uses data models, AI, and machine learning to deliver and improve services.
A US-based company, AiCure, uses AI on mobile devices to confirm medical ingestion in clinical trials and high-risk populations. It determines if a user is taking the prescribed drug at the right time. By using automated algorithms, the app identifies patients, the medication they are taking, and the process of medication ingestion. Tracking of adherence patterns is very important as researchers have identified non-adherence in healthcare as a critical source of healthcare funds wastage, which amounts to approximately 2.3% of the US GDP. Data analytics can play a major role in saving billions of dollars by achieving the optimal level of adherence.
Pharma companies are also applying AI and machine learning to drug development and discovery. Boston-based company Berg combines AI and Big Data to chalk out new drug compounds that can have more benefits.
The shift to include AI in patient care has already begun. But what does the road ahead look like? In the search to find a cure for chronic diseases like cancer, research organizations have already spent billions of dollars. However, it often takes years to identify a drug and introduce it in the market. Here too, AI has the potential to bring down the drug discovery cost by analyzing huge data points in days. In the past, this process used to take months and even years. Machine learning can help find signals in medical images which a human cannot effectively identify, thereby missing critical patterns. The insights gathered can also determine if a patient is at risk of developing a disease in the future. This can help doctors prescribe proactive treatment to their patients.
The potential gains and opportunities from AI are exciting, and every day new progress is being made. AI and machine learning in healthcare is going to fundamentally disrupt the healthcare industry and stakeholders should be prepared to embrace the change.
What do you think will be the most critical contribution of AI in healthcare in the next few years? Please tell us in the comments section below.