Machine Learning in Healthcare: The Future Of Healthcare Will Be Around ML 

ML In Healthcare: Application, Benefits, and App Development Cost

Machine Learning (ML) is one of the trending concepts in the field of Artificial Intelligence (AI). Driven by its automation and predictive analytics, ML technology is being used for creating intelligent software solutions that offer more accurate analysis of data.

Banking and finance for Fraud detention, Marketing & sales business for predicting market scope and user interests, Healthcare organizations for improving patient care services, and Fintech organizations for estimating stock trends, ML is widely used across diversified industries.

Be it the purpose of forecasting the market dynamics, analyzing & optimizing equipment performance, determining customer behaviors, or making deep analyses of sales data, ML-powered and AI-based mobile apps have the highest scope in the years ahead. The industries will increasingly gain a lot of operational and financial benefits from using ML’s automation and predictive potentialities.

Among all other industries, the healthcare industry is one of the top sectors that is an early adopter of AI and ML-like advanced technological innovations. Whether you are a startup or a fully developed brand in the healthcare industry, ML app development ensures streamlined business operations. Let’s start our session with the top applications of ML in the healthcare sector.

Today, in this article, we would like to discuss the top use cases of ML in healthcare, the significant benefits of ML in the healthcare industry, and how much will it cost to develop ML-based healthcare apps.

Top Use Cases Of AI/ML In Healthcare Industry

The significance of machine learning in healthcare is extending with the continuous developments in the field of AI. Speed and accuracy as the core features, and ML technology is making a buzz in the digital world. Let’s take a look at the significant applications of ML in the Healthcare industry.

Here are the best answers for How AI is used in healthcare.

1. Disease Prediction

Disease prediction is one of the top ML use cases in the healthcare industry. The use of AI and ML-based applications in the healthcare service sector is increasing for predicting life-threatening diseases and improving patient care services.

The predictive modeling feature of ML algorithms derives patterns into the patients’ health reports efficiently and predicts the disease seniority. It helps physicians make immediate and better decisions to improve the patients’ outcomes.

2. Streamlines The Process

One of the major roles of ML technology in healthcare sector is process automation. Intelligent ML applications in healthcare sector, automatically processes data (patient data analysis), minimizes manual interaction, and maintain quality and accurate information. Hence, it will improve the operational efficiencies, optimize the resources productivity, and reduce the costs.

3. Research and Drug Development

It is the best use case of ML in the healthcare sector. Incredible predictive capabilities of Artificial Intelligence and Machine Learning are making drug discovery & development, candidate scope analysis, and patient data analysis for clinical trials faster and easier.

Further, ML technology is also being used for making faster decisions in drug design and development.

Moreover, besides research works, drug manufacturing companies are also increasingly investing in ML application development for forecasting the side effects of a drug candidate. It means ML apps in drug development are used for detecting the toxicity of a medicine before the clinical trial stage and improving its quality.

4. Efficient EHR Management

The need for ML in healthcare, especially for streamlining the cloud management and accessibility of patients’ data is going to drive more opportunities for ML healthcare applications in the future.

Features-rich ML-based applications in healthcare will help healthcare service providers and physicians access the Electronic health records of patients anytime from anywhere.

While ensuring the privacy of the patient’s health records, ML applications allow physicians to access previous medical treatments and the current status of their health condition. It was proved that AI and ML applications can help doctors predict the diseases that going to affect them in the next coming 5-10 years.

Hence, ML software solutions in healthcare play a key role in saving a lot of manual time in recording patient’s manual data and improving health outcomes.

5. Improve Diagnostic Accuracy

AI and ML-based medical image processing applications offer 99% accurate analysis on blood samples, DNA sequences, and radio images. Faster but accurate data analysis and patterns recognition will helps doctors to provide the best care and diagnosis services for reducing the health risks.

6. Treatment Suggestions

The use of machine learning applications or tools in healthcare makes the diagnosis process efficient and helps doctors to find multiple treatment or medicine suggestions to improve the patients’ health conditions. Based on the previous medication history and health conditions, AI and ML applications offer personalized treatment ways that ensure potential health outcomes.

7. Medical Device Performance Analysis

Intelligent ML applications make an impact in healthcare in many ways and medical device performance monitoring and analysis is one of them. AI/ML-enabled medical devices advance the accuracy of findings and improve therapeutic efficiency, thus patient care level will be boosted.

8. Virtual Nursing Assistants

ML-based virtual nursing assistants help hospital staff monitor the conditions of multiple patients at once. It is not possible to manage or view the vital health signs of many patients at once manually, but AI and ML-based software solutions do it efficiently. Hence, it will assist the staff to send immediate alerts to the physicians and make them aware of patients’ health conditions and improve care level.

9. Robotic Surgical Procedures

With unimaginable precision and adaptiveness, ML and AI-powered surgical robots have been making a buzz in the digital healthcare industry. Well-trained AI and ML-based surgical robots infused with the capabilities of professional surgeons are involved in minimally invasive surgeries. These AI and ML-based robot-assisted surgical robots will offer surgeons and ensure better visualization to perform surgeries with very small incisions.

These are a few significant applications of ML technology in the healthcare industry. Machine Learning like advanced analytical technology will benefit in terms of saving time, reducing operational costs, streamlining medical records management operations, and overall transforming the traditional healthcare operations from front-desk record keeping to complex surgeries.

How Much Does It Cost To Develop ML-based Healthcare App?

The cost of healthcare app development depends on various factors. There are so many types of healthcare apps available in the app stores. Virtual trackers, Diet planners, fitness & wellbeing apps, telemedicine apps, database management apps, medical networking apps, ePrescription apps, insurance claiming, and billing/invoice preparing apps, etc.

Based on the type the healthcare application, the features and design complexity will vary and this impacts the final cost of a healthcare mobile app.

Further, the application development platform, UX/UI design, technology stack used for mobile application development, and team size of the app developers will impact the final cost of the healthcare application. Moreover, the region and hourly rates of top app developers (Android app developers or iPhone app developers) will decide the actual cost of healthcare app development.

On a rough estimate, the cost of a healthcare application with a minimum level of design complexity and a set of the most required features will cost somewhere around $45,000 to $88,000. However, based on all the above factors, the cost of a healthcare application development might fall in the estimated range or exceed the limit as per your app specifications.

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Final Words!

The benefits of Artificial intelligence in healthcare or Machine Learning in the healthcare industry are numerous. AI and ML applications reshape the way healthcare service providers deliver services.

As we discussed in this article, AI-powered administrative and ML-based patient care solutions will be the future of the healthcare industry. AI and ML applications automate the front-office tasks and helps doctors improve care level.

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Machine Learning (ML) is a significant application of Artificial Intelligence. As we discussed in our ML blog category, applications of machine learning in healthcare learn from previous experiences or data, identifies patterns, and forecast future events.

These predictions add value to businesses. This was the major reason behind the increased demand for ML techniques. Several industries are using ML in their multiple applications for various purposes.

It’s a globally known fact that the digital world is making data accessible in a simple way. The vast amount of data is collecting, storing, and processing easily using intelligent technologies. On top of this, increasing the computing efficiency of innovative technologies made data profitable for businesses.

In addition, processing toolkits, open-source frameworks, and immense libraries are all available at affordable prices, and thus, building and executing ML applications is growing worldwide.

Though ML is giving lucrative benefits to industries, its impact in the healthcare industry is unbelievable. Machine learning in healthcare leads to some thrilling developments in oncology. Diagnosis of cancer and providing accurate treatment at the right time will become more feasible in the coming years with ML.

Machine learning algorithms the sensitivity-level of diagnosis, and helps physicians to make the right decisions to provide value-added treatments. Therefore, ML-based machines assist healthcare service providers to offer personalized treatment experience to their patients.

Yes, this reinforcement learning can also improve employee productivity in hospitals. Of course, the applications and uses of ML in the healthcare sector are infinite. In this blog, we briefed a few major uses cases of Machine learning algorithms in healthcare. Let’s start!

#1 Disease Detection

Healthcare machines are overloaded with increasing usage. This was primarily due to a growing population and with the same proportion of increasing diseases. The machines are continuously working to identify the diseases of the patients.

However, traditional machines are not capable of doing this efficiently. But, machine learning models can detect diseases with more accuracy at less time. It’s a proven fact!

Researchers have been still working on exploring machine learning in the healthcare sector. They are continuing their innovation in developing more efficient models of machine learning. Currently, machine learning methods help doctors in the early detection of diseases before the disease reaches to severe level.

The global leading technology companies are developing neural network artificial intelligence systems for accurate detection of difficult respiratory disorders. These machines, in connection with the IoT-based sensors, can precisely analyze respiratory problems and avoid risks and hospitalizations.

Nowadays, new deep learning-based disease prediction methods efficiently identify cancer in early stages. These models are trained with the patient’s medical records to detect irregular patterns that cannot be observed by human physicians.

#2 Robotic Surgery

Today’s intelligent robots are completely altering the way of doing surgery. They are designed to perform complex surgical functions. Robots can also reduce the time of surgery.

Robots can perform cardiac, spine, orthopedic hip and knee replacement, neurology, cancerous tumor irradiation, and other significant surgeries. Robotic surgery is minimum invasive and offers 99.9% positive patient outcomes.

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AI and ML-based surgical tools can enhance the processing of medical records. Also, they can give self-reviews based on past surgical experience. Therefore, the use of robots optimizes the process of surgery and reduced errors that are may happen with physicians.

#3 Personalized Medicines

The current technology is restricted doctors to estimate the risk level of disease based on the patient’s previous medical history and genes. Using AI & ML, the researchers are finding new ways of developing further matured personalized medicine for various diseases.

Machine learning in medicine is shaking today’s healthcare sector. ML-machines can provide numerous treatment ways when they trained with the patients’ medical records. In the next coming years, we will surely observe the manifold of intelligent devices and sensors with infinite capabilities.

#4 Smart Health Records

The manual process of keeping healthcare records up-to-date is a bit complex procedure. Though technology is playing a significant part in enabling data set entry procedures, still, the process was very lazy and taking too much time to enter the training data.

Here, unsupervised learning comes into place. The major role of machine learning in healthcare is to make every process simple, complete the task in a very short span.

So, ML-based smart health records avoid manual errors and automatically update the patient’s health data. Thus, the updated learning from data is very much useful for doctors to provide treatment/medical suggestions to the patients.

#5 Research and Clinical Trials

Machine learning has been widely using in medical research and clinical trials. Clinical trials of new drugs are cost-efficient, and take years to complete and get approvals from authorities.

Many pharmacy companies are now using ML-based predictive analytics for identifying latent clinical trial candidates. Machine learning models prefer electronic records to avoid unlabeled data-related errors.

#6 Improved Radiotherapy

One of the major use cases of machine learning in healthcare is in the radiology field. ML-driven algorithms can automatically learn from massive various samples to detect the patterns.

Using machine learning, the radiologist can easily categorize regular or irregular impressions in a medical image. This scenario helps to diagnose the disease level and provide proper treatment at the right time.

#7 Disease Predictions

The power of AI and ML technologies in healthcare is incredible. The government and private healthcare service providers are deploying AI & ML-based systems for many purposes.

Among other, ML and predictive analytics benefit healthcare providers to accurately predict a wide range of diseases from malaria to chronic and infectious diseases. This capability of ML helps for those markets that have no proper medical infrastructure for identifying such kind of diseases.

#8 Drug Discovery and Manufacturing

Drug discovery was a major clinical use case of machine learning. Next-generation sequencing and precision medicine technologies are also widely using in detecting multiple ways to provide therapies for various diseases.
The use of ML in drug discovery will help to reduce the cost of introducing new drugs to the market, and also make the drug discovery process faster.

#9 Crowdsourced Data Collection

Nowadays, crowd sourcing in the medical industries is all the rage, enabling practitioners and researchers to obtain high amounts of the data uploaded by individuals depending on their own consent. There are tremendous changes in the way medicine is perceived in these live health data.

Tech giant, IBM has recently associated with Medtronic to decipher, accumulate, and make available insulin and diabetes data depending on the crowd sourced information.

As progress is made in the Internet of Things (IoT), the healthcare sector is finding new ways to use this data and to address tough-diagnosis cases and the comprehensive development of diagnostics and drugs.

#10 Machine Learning-based Behavioral Modification

Behavioral modification is a significant part of preventive medication, and since machine learning usage has expanded in the healthcare industry, a large number of startups have been growing in areas such as cancer recognition and prevention, patient diagnosis and treatment, and more.

Somatics is a B2B2C based data analytics firm that has released an ML-based applications to identify gestures we make in our day to day lives, which allow us to understand our unconscious behavior and make necessary changes.

Wrapping Up

USM Business Systems has vast experience and best practices in using AI and its technologies, deliver high-level AI and ML solutions and mobile apps. We have achieved excellence in AI and ML, but further developments in intelligent technologies are needed in this society.

Our machine learning services for healthcare have helped many clients to save their time and costs. Our Machine learning solutions enabled our customers in uplifting their offerings for this modern world.

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