How Has Machine Learning Changed The Healthcare Industry?

Machine learning or ML has changed the healthcare industry drastically. It has facilitated new methods of handling healthcare data, changed patient care management, and organized administrative operations. ML healthcare development solutions have helped manage terabits of healthcare data that earlier required human efforts and it was a time-consuming task too.

Machine learning is a subdivision of AI (Artificial Intelligence) that primarily focuses on the use of algorithms to understand things from the data without the need for programming. With a few inputs, ML can perform a lot of human activities.

Let us learn about how machine learning medical applications have changed the healthcare sector, what are its use cases and the challenges that obstruct the acceptance of ML.

Machine Learning Applications In The Healthcare Industry

ML has the potential to bring in some visible enhancements to the healthcare system. The technology has helped in various healthcare departments like rare diseases, tumors, pathologies, etc.

ML-enabled systems can even defeat some of the tasks performed by humans.

Let us have a look at the machine learning medical applications that have introduced plenty of benefits for healthcare professionals and patients.

Diagnosis Identification

A major part of AI healthcare application development companies is focussing on building solutions that help focus on diagnostics. ML has the efficiency to assess data in seconds related to thousands of old patients. ML can help clinicians determine the disease accurately to enhance treatment quality.

Moreover, ML can also assess data to determine the patient’s condition like previous tests, CT scans, health screenings to help healthcare professionals come up with an accurate diagnosis and promote improved healthcare

For example, EKO - is a machine learning application in healthcare wherein sensors and ML algorithms defeat the doctors at determining heart issues.

Medical Record Management

It is not easy to manage healthcare records as it takes a lot of time. But, with ML-based healthcare mobile app development solutions, this issue can be resolved without any hassles.

• NLP – Natural language processing another subdivision of AI help reduce the burden of physicians performing a lot of tasks. NLP catches human dialogs from patients and converts them into text so that doctors do not have to enter the notes manually
• MedInReal offers virtual assistants for doctors to help them automate a lot of repetitive tasks.

Medical Image Analysis

Earlier, radiologists had to spend hours together studying CT images to find out the abnormalities like tumors, etc. Well, deep learning a subdivision of ML has helped radiologists compare the CT scans to countless similar cases to identify the abnormality.

A popular cloud-enabled medical imaging platform Arterys utilize ML algorithms to draft and compare images of blood flow which helps with cardiac analysis in as short as 6 minutes.

Disease Prediction

It is one of the most interesting ML contributions in healthcare is disease prediction.

ML helps study the patients’ health details to identify the connections between different symptoms in patients with a pre-assumed health ailment. The connections found can help predict possible health results before the occurrence of any health issue and help doctors understand the root cause of the disease.

Some the diseases like liver disease, cancer, diabetes, etc when detected in their early stages can help save patients’ lives by providing preventive medicine.

For example, IBM Watson Genomics.

Mental Health Tracking

This is one of the effective use cases of ML in healthcare. It is about knowing and forecasting mental health problems. This helps psychiatrists identify people who are suffering from mental health issues.

Drug Development & Discovery

Well, deep learning in healthcare can enable the drug discovery and development process.

ML-supported techniques can help evaluate biological activity, distribution, metabolism, excretion characteristics, etc

For example, druGAN is used to produce fresh molecular fingerprints & drug designs integrating essential features based on pre-set anticancer drug properties.

Clinical Trials

ML can enhance and make the drug development process easier along with clinical trials. Scheduling clinical trials is time-consuming. But, ML can speed up every phase of clinical trials and offer precise results. It helps scientists pick the suitable candidates for clinical trials and evaluate the real-time data offered during a trial, identify errors, etc

Robotic Surgery

It is still quite new and we cannot discuss robots performing surgery at the moment. But, they can help doctors to administer surgical devices and conduct some tasks.

The use of ML in assessing surgical skills, suturing automation, enhancing robotic surgical equipment, etc is already a success.

For example, STAR – smart tissue autonomous robot.

Predict Epidemics

ML in healthcare helps predict and monitor epidemic outbreaks. Not just this, ML also helps reduce the worst epidemic results by detecting it early.

ML-enabled chatbots assist group the patients into specific groups based on specific symptoms and suggest doctors diagnose and review to make further decisions.

Personalized Treatment

ML helps healthcare get into preventive mode rather than the reactive mode of healthcare by offering personalized treatment options. ML can work to figure out the patients who may have health issues.

For example – IBM Watson Oncology

Patient Engagement

ML increases the patients’ involvement in the treatment program to bring the best results. ML can fetch accurate patient data and send out messaging alerts that cause patients’ actions at certain moments.

For example, wearable non-invasive sensors

What Are The Challenges To ML Acceptance In The Healthcare Sector?

ML has proved to be quite helpful in several cases. Still, its acceptance needs a lot of hard work. Let us have a look at the possible challenges in acceptance of ML in healthcare :

Diverseness of Data

The acceptance of ML and AI can introduce enhancements to almost all the departments in the healthcare sector. Still, for precise predictions, ML should be backed by premium and organized data.

The flaws in healthcare data can result in incorrect predictions which in turn can affect the decisions made in a clinical setup.

Today, the major obstacle is the diverseness of data in the healthcare sector which can obstruct the acceptance of ML.

Lack of Relevant Resources

Yet another obstruction that can hinder the acceptance of ML in the healthcare sector that is the lack of skilled data modelers and engineers.

Provider Resistance

One of the major challenges to using ML in healthcare is defeating provider resistance. Medical organizations need to upgrade or replace their legacy systems with modern ones to introduce ML into the systems. This needs essential resources that may not be available at times just like during the corona pandemic

Future of Machine Learning In The Healthcare Industry

Despite all the challenges mentioned above, we cannot ignore the fact that AI and ML have a promising future ahead. The stats reflect that :

• AI in the international market has reached $6.7 billion and is predicted to reach an annual growth rate of nearly 41.8% between 2021 and 2028.
• The factor that contributes to the growth of ML in healthcare is the growing number of start-ups entering this sector.
• Looking at the cost-effective module that ML offers, healthcare can save a lot of money.

Conclusion

Machine learning application in healthcare has changed the treatment and diagnosis to a large extent. No doubt, the use of this technology has already shown amazing results in the early diagnosis of life-threatening diseases like cancer, etc.

If you want to be part of this ML-enabled healthcare industry to generate profits, you can invest in white label healthcare app development by hiring an expert mobile app development company having expertise in developing ML-enabled healthcare apps.

X-Byte Enterprise Solutions, a trusted mobile app development service in the USA can assist you to integrate ML in healthcare apps and can get impeccable mobile app development solutions that help you offer technology-driven healthcare services. Get in touch with us to discuss your project requirements.