If you want to get into your doctor’s bad books, turn up to your next appointment having preemptively diagnosed yourself via Google. If there’s one thing that annoys healthcare professionals, it’s patients thinking that computers can do their job as well as them. Some physicians even have signages in their waiting rooms stating that.
Algorithms aren’t going to replace our doctor any time soon. However, there’s a lot of machine learning in healthcare that can help him diagnose you faster and more efficiently. Moreover, they can also devise an efficient treatment regime.
AI and machine learning in healthcare are poising to help healthcare professionals improve the quality of services. This is to enable more and more people to access care and reduce costs. Machine learning applications can help in accessing and interpreting huge amounts of patient data from across the world. Furthermore, these applications can identify genetic predispositions to certain diseases, suggest the most efficient treatments, and reduce the risk of medical error. And that’s not counting the myriad of machine learning tools that keep medical institutions to run smoothly on a day-to-day basis.
Let’s take a look at some of the current applications of machine learning in healthcare.
Machine Learning – Powered Virtual Assistants
To some extent, medical labor shortages do not affect a few countries across the world. That include the ones with considerable resources. Shortages in personnel lead to huge pressure on available healthcare workers. This can entail burnout on the part of medical professionals and in turn, a less-than-positive patient experience.
One of the most exciting types of machine learning is natural language processing (NLP). They are using this technology to develop virtual assistants and machine learning chatbots. It primarily aimed at the healthcare sector. Don’t panic! No one’s suggesting that your future medical consultations will take place with a robot. Chatbots simply manage the initial exchange between a healthcare provider. Moreover, they help to give their patient a later, more in-depth in-person exchange.
Chatbots can furthermore help doctors make an accurate diagnosis by asking patients a series of questions and making recommendations for appropriate courses of action. The more questions and answers the chatbots go through, the more they will learn which will provide them accurate answers. That’s the very basis of machine learning.
Algorithms aren’t going to replace an honest conversation with your doctor at any time soon. What they do is enhance in-person meetings and conversations. They are doing this by giving physicians time to focus on delivering a better standard of care.
Health Monitoring With Wearables
One application of machine learning in healthcare that’s probably already familiar to you is wearables. You most likely already possess a wearable healthcare device in one form or another – nowadays, huge parts of the population are using the healthcare and fitness trackers made available by manufacturers such as Apple or Fitbit. With varying levels of sophistication, the wearables in question gather data about the user and then interpret it to be able to suggest a personalized fitness regime or diet.
That’s just one of the potential applications of AI and machine learning-powered wearables, another one being health monitoring. The Apple Watch Series 4 possesses an EKG sensor that allows its users to determine if their heart rhythm is normal or in Atrial Fibrillation (AF). The KardiaAI software analysis library marketed by the Californian company AliveCor recently got FDA approval for their EKG processing and analysis algorithms.
Other major companies are showing increasing interest in machine learning in healthcare wearables, with Johnson & Johnson currently developing a heart monitoring collaboration with Apple.
Clinical trials and research are a fundamental aspect of developing new drugs. Moreover, machine learning has many potential applications in the field. Clinical trials cost vast amounts of money and often take many years to complete. With the recruitment of eligible patients, a huge undertaking before the trial even gets underway. Companies such as Antidote offer clinical trial matching platforms. They use machine learning to connect the right patients to the right studies by drawing from a wide pool of data points.
Machine learning can also be used to streamline trial workflows, ensure real-time monitoring of trial participants, mitigate risk, and improve team collaboration. Another Californian company, Trials.AI, offers a cloud-based Study Optimizer platform that uses algorithms trained on data points from past clinical trials to identify and mitigate risk. Along with this, they make recommendations for trial optimization. Other machine learning-based healthcare applications include apps that enable researchers to monitor and boost patient engagement. Doing this, it can help to prevent dropouts and nonadherence.
Predicting Outbreaks And Epidemics
Predicting outbreaks and epidemics is of crucial importance in developing world countries and places where medical infrastructure and education is lacking. Being able to reliably predict infectious disease dynamics is extremely valuable to public health organizations that can then intervene in a timely fashion and prevent or monitor outbreaks accordingly. Machine learning tools can be used to accurately predict and monitor epidemics around the world. They are doing it by collating geography, climate, and population distribution data as well as information from satellites, social media activity, news updates, and outbreak reports.
There are already several very interesting projects afoot in this particular field of machine learning applications. AIME (AI in Medical Epidemiology) has created a Dengue Outbreak Prediction platform that uses AI and machine learning to impact the spread of the disease. They hope to be able to apply their findings to Ebola and Zika sometime in the future. Microsoft is currently working on Premonition. This is a research project that aims to create an early warning system for Zika, Ebola, Chikungunya, and MERS pandemics.
A few significant challenges still exist when it comes to machine learning applications in healthcare. Data governance is a major one, with the privacy of individuals a major concern. Data silos still pose a major problem which involves fragmented electronic records. Moreover, there’s the need to recruit the right data science talent. Machine learning in healthcare has a promising future ahead of it. Consequently, better decision-making, improved efficiency, and optimized innovation are everyday realities.