COVID-19 episode has created a crisis in healthcare systems across the globe, but every crisis presents an opportunity. Now is the time to accelerate the use of AI in healthcare than ever before.
Governments across the world need to collaborate on universal public health policies and break down the barriers to succeed in this fight against future pandemics, and economic instability. The adage is appropriate at this time “think big, start small, execute fast” should be the approach to accelerate AI in Healthcare.
Let’s look at the three broad well-known categories - Prevention, Diagnosis, and Cure. Here are a few AI examples in Healthcare to think about and can act as the foundation to build from.
Health records: myhealth portal (here) from the Singapore government has been the game-changer for the government’s response to the COVID-19 crisis, the model can be expanded to cover larger countries.
DNA based prediction: Ancestry, 23andme are popular options when it comes to disease prediction and the models are improving every day with the addition of more data points. Integrated into the healthcare system, provides personalized prediction and lifestyle adjustments for individuals in the high-risk category.
DNA based nutrition: Gaining popularity in some countries where personalized nutrition is recommended based on DNA analysis, one popular company within this space is Genopalate, Inc, (here). Similar to the DNA based prediction, this option provides personalized recommendations to make lifestyle adjustments for food intake for high-risk individuals.
Networked world health record data: For prevention and better management, there is a need for governments to collaborate on the safe sharing of anonymized health data to support AI in predicting diseases. There is a lot of work to be done here, although it is not impossible either.
Smart medical devices: Technology and computational power have increased multi-fold over the past few years and dramatic improvements in machine learning technology can be extended to AI-based symptoms and cure checkers that leverage algorithms to diagnose and eventually treat illnesses. Deploying AI at scale during regular health screenings, health systems can detect various diseases in the earliest stages to develop early treatment options. Smart medical tests with blood-based diagnosis is another important category that will go through mass-market adoption over the next decade or so. AI-powered inferencing at edge devices at the point of care will be able to inference based on certain markers available in the blood to diagnose the early onset of the disease using carefully curated machine learning models.
Speed of Clinical research & trials: Clinicals research and trials take a long time today, with significant prolonged investments needed. AI/ML can provide predictive models to identify the best candidates or even run parallel clinical research on several candidates at a low cost. Best of technology will also help significantly improve accuracy and recommend ways for clinical researchers to reduce the errors in clinical trials.
Robotic automation: Recently, due to the impact caused by COVID-19 infections, several hospitals in Asian regions adopted the use of Robotic nurses to interact with infected patients and reduce the exposure of healthcare workers. The best in cutting-edge technology with a human touch can make the world of difference to perform frequent check-ins and medical care settings in the patient care setting. The cost of care can be significantly reduced while deploying investments into other areas of research and development.
Crowdsourced data: Currently, researchers already have access to a tremendous amount of healthcare data. With strategic data partnerships with public and private sectors, the ability to help accumulate, analyze and provide normalized datasets will be more beneficial than ever before. Public Health data will significantly improve the march towards better treatments due to the volume of data analyzed. Drug combinations used in specific diseases and conditions and their side effects could be surfaced faster and more efficiently to healthcare providers. With the advent of IoT, drug distribution and management for personal treatment could be improved to avoid cross-contamination or drug overdose.
AI in medical surgery: Medical surgery has seen vast advances over the centuries, the important aspect to highlight is waiting times for surgeries are growing across the globe. The use of AI in performing precision medical surgeries could pave the way for reduced wait times and reduce avoidable errors. Da Vinci Surgical System is the most commonly used medical surgery robot and it was introduced in 2005.
Personalized treatment options: Hyper personalized treatment options are now possible with machine learning and several companies have made significant progress in the area of providing numerous treatment plans that first analyze the patient’s medical history. And with the advanced analysis along with healthcare data becomes available, thus providing more data for these algorithms — personalized treatment will get even better when it comes to creating focused recovery paths for individuals.