Technological advances have heavily influenced and shaped the healthcare industry in the last few years. Healthcare facilities and companies now leverage technology to deliver more effective products, offer better treatment plans and ensure timely interventions.
Advances such as machine learning are also being increasingly incorporated into healthcare technology. 54% of the U.S. healthcare leaders expect machine learning to be widespread by 2023. Experts believe that machine learning promises to ensure better patient data processing, trim down pre-treatment waiting time and help in the creation of tailored treatment plans for individual patients.
While these remain the future benefits of machine learning in healthcare, following are some key machine learning applications that are already in use within the industry.
Early and Accurate Diagnosis of Diseases
Diagnosis is the most crucial part of a medical treatment plan. This is why healthcare providers go to great lengths to get it right. Machine learning is helping in this by enabling early and accurate diagnosis of diseases that are otherwise hard to diagnose. Techniques such as genome sequencing and genetic data are being used to pin down such diseases and initiate treatments early on.
Faster Processing of Patient Records
Processing electronic medical (EMR) records of the patients can be a timely and expensive process. It can also create delays in the start of treatment for many patients. Machine learning makes EMR processing quick and cost-effective, ultimately trimming down the costs of care for the patients.
Drug Discovery and Development
A vital clinical application of machine learning is in early-stage drug discovery and development. Many diseases have multiple factors that must be taken into account when prescribing the right drugs. Initiatives such as the Project Hanover by Microsoft are making it possible for drug manufacturers to research personalized drugs tailored to the specific circumstances of a patient.
Better Management of Health Information
Health information, including clinical data, needs to reach multiple stakeholders in a clinical setting. The timely and accurate delivery of this information aids in proper care and timely treatment. While there have conventionally been several bottlenecks in this flow of information, machine learning has enabled many health providers to make the process more seamless and quick. This also streamlines clinical workflows and improves overall efficiency.
Medical Imaging and Diagnostics
Medical imaging is another healthcare area where machine learning is enabling significant advances. With machine learning, pathologists and other healthcare professionals are able to make an accurate and quick diagnosis in reviewing medical imaging. As machine learning becomes more integrated into medical imaging, we can expect more detailed automated analysis from such diagnostic procedures. Microsoft’s Project InnerEye is a primary example in this case. It uses machine learning to identify tumours and differentiate them from healthy body anatomy.
Healthcare providers increasingly realize the importance of personalized medicine. Any given disease manifests differently in different patients. This is why many healthcare professionals now consider multiple treatment options for a given condition. Machine learning tools like IBM Watson Oncology help generate these options. These options are generated by considering patient history and relevant factors such as local weather, dietary habits, family history and genetic makeup.
Costly research & development (R&D) is one of the key reasons why many drugs are expensive. Machine learning now helps drug manufacturers reduce R & D costs. This enables them to create drugs that are better researched, manufactured sooner and cost less.
Smart Health Records
The management and processing of health records is an incredibly cumbersome process. It takes time, money and significant effort. This is another area where machine learning is ushering in a change. With faster machines, improved bandwidth and advanced ML algorithms, it is possible to classify documents and speed up data entry and storage within the healthcare industry.
Robotic surgery is a more advanced form of machine learning in a healthcare setting. Still, in its infancy, robotic surgery is taking on smaller tasks which do not require the advanced skills of a surgeon. This helps a surgeon avoid fatigue, speed up surgery and mitigate errors on routine procedures related to surgeries.
Behavioural Modification and Diseases Prevention
Behavioural modification is the implementation of changes and behaviours best suited to prevent diseases. With machine learning, it is possible to read human gestures on a daily basis and suggest optimal modifications. Public healthcare entities today gain more headway with such ML-based modification data.
Crowdsourced Health Data
With the pervasiveness of technology in our lives, it is now possible to crowdsource health data. This data is often voluntarily submitted by people across the world. Public and private healthcare entities today use machine learning to explore this data and chart health strategies, identify disease outbreaks and gain a deeper insight into genetic similarities and differences across geographical boundaries.
Tailored Daily Healthcare Plans
As machine learning becomes more readily available, it is becoming possible for healthcare providers to create tailored plans for individuals. These plans are delivered through daily doses via a variety of smart devices. Such tailored plans can play a critical role in disease prevention, diagnoses and treatment at a personal level.
Choosing the Right Doctor
A relatively simpler but equally important application of machine learning is in matching patients with doctors. Several platforms help patients find the right doctors by matching the two across various data touchpoints. This helps patients quickly reach doctors who are more likely to help them effectively.
Clinical Trial Possibilities
Patient data can be used to determine areas of potential clinical trials and studies. While this was once nearly impossible due to the sheer effort needed to sift through the data, it is now possible with machine learning.
Disease Outbreak Prediction
Healthcare scientists and researchers today have access to endless data. This data is dug from social media, patient records, real-time imagery, breaking news and more. With machine learning support, this data is used to accurately predict the outbreak of various diseases like malaria.
Radiotherapy is another area in which machine learning is improving diagnosis. Conventional radiotherapy is notoriously inadequate in identifying and categorizing all objects such as lesions and cancerous growths. With machine learning, it is possible for radiotherapy equipment to learn and grow so that it can better identify and categorize these objects over time.
Identifying Healthcare Lapses
Healthcare lapses cost lives, time and money. This is particularly so for lapses and gaps in public health systems. Machine learning is today leveraged to identify these gaps and bridge them, making healthcare provision more effective.
The use of machine learning in the healthcare industry is still in its initial phases. The vast range of applications listed above is but the tip of the iceberg. The future of ML-based healthcare solutions and innovations is very promising. And the pace of machine learning adoption across the healthcare industry is astonishing. This trend may finally usher in the era of customized and personalized treatments for individual patients in the near future.