10 Machine Learning Applications in Healthcare

Healthcare is a valuable resource for people worldwide that is continuously evolving. In other words, companies are looking for ways to improve healthcare.

Machine learning (ML) will soon lessen the burden for healthcare facilities to handle the continuous waves of patients. As a result, many applications are being developed and launched to make ML useful in healthcare.

Let’s look at 10 ways that ML can benefit healthcare facilities and organizations.

Top 10 Machine Learning Applications in Healthcare

1. Discovering and Manufacturing Drugs

“Machine learning applications can be useful in drug discovery,” says Elias Walker, a health writer at 1 Day 2 write and Write my X. “Pharmaceutical companies can use machine learning to discover new medicines, and manufacture them once they’re approved.”

Companies like Microsoft have already dabbled in using machine learning for biomedicine purposes.

2. Disease and Diagnosis Identification

People suffering from diseases want solutions – accurate ones. Now, with ML, more accurate disease and diagnosis is possible.

IBM has been working on  utilizing machine learning for oncology  with the hopes that medical imaging solutions will help clinicians better care for their patients.

3. Drug Personalization

While one drug may effective for some people, that same drug might not be work for others. That’s where personalized treatments come in; they pair individual health with predictive analytics, thanks to ML.

2bPrecise is at the forefront of searching for innovative ways develop drugs more personalized for individual patients. From multiple treatment options to deep dives into how prescriptions are labelled, 2bPrecise wants use of ML-based personalization to grow in the medical field.

4. Diagnosing with Medical Imaging

Medical imaging is essential for diagnosing many patients’ diseases. Machine learning can take medical imaging to the next level.

Initiatives like Microsoft’s Project InnerEye are looking to use machine learning to create better medical imaging. With ML growing more accessible to companies, InnerEye will allow for more data streams from varied medical imagery.

5. Health Records Made Smart

Healthcare institutions must maintain up-to-date health records; this can be a tedious and costly process. But with advent of ML keeping health records is more efficient time and money-wise.

Pangaea wants to make health record keeping easier for healthcare institutions. With ML-based data vendors, Pangaea ensures that health records are better accessed by medical professionals and individuals.

6. Medical Behavioral Modification

Behavioral modification is essential for preventive medicine.

For example, Somatix – a B2B2C-based data analytics company – had released an ML-based health app to help users better recognize gestures that detect and understand unconscious behaviors so that the necessary changes in behavior can be made.

7. Improved Radiotherapy

Radiotherapy (or radiation therapy) is often used in cancer treatments. However, this kind of treatment may sometimes be doing more harm than good for patients.

Google’s initiative DeepMind Health is currently helping medical researchers use machine learning to improve radiation treatment by detecting the difference between healthy and cancerous tissues.

8. Smart Collecting of Crowdsourced Data

Crowdsourcing in the medical field allows researchers and practitioners to access consent-based information. This live health data allows companies to improve medications and other treatments as time progresses.

Apple’s ResearchKit has users access interactive ML-based apps which use facial recognition to assist in treating diseases like Asperger’s and Parkinson’s.

9. Smart Clinical Trials and Research

With the need for clinical trials and research growing, there’s a greater need for smart enabled approaches.

SCIRENT works with biotech, pharma and medical device companies on better managing and executing clinical trials and research with machine learning applications. With ML, predictive analytics allow companies to find the right clinical trial candidates.

10. Predicting the Next Outbreak

“The COVID-19 pandemic has claimed many lives,” says Harvey Russell, a writer at Brit Student and Next Coursework. “Now, there’s a desperate need for ways to predict another outbreak so that another COVID-19-like disaster doesn’t happen again.”

ML-based resources like ProMED act as professional and medical forums that update subscribers and visitors to the site on any infectious activities in the world. By checking in with emerging and evolving diseases in real-time, people can be better informed should there be another outbreak.

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