Machine Learning In Healthcare: Challenges And Opportunities

As per a report published by the World Health Organisation, by the end of 2030, approximately 1 billion healthcare workers will be needed. However, the scarcity of skilled healthcare providers can be slightly diminished by AI-based machine learning techniques. Machine learning in healthcare can improve the efficiency of medical services and unleash time for healthcare professionals, helping them to design a more personalized approach for every patient.

We will try to navigate various opportunities and challenges of machine learning applications in healthcare to understand their role in the healthcare system. 

What Is Machine Learning?

Machine learning is a specialized model of AI, enabling systems to extract information and learn from fed data and identify patterns without much human or manual intervention. Recognized patterns and perceived data are shown to the computer systems using machine learning algorithms allowing them to use according to their own requirements. 

Machine learning revolves around various types of algorithmic models and statistical methodology to solve problems. Machine learning models and algorithms perform a variety of tasks, like email filtration, denoting categorized objects in the images, analyzing complex and large amounts of data sets and many more. 

AI and machine learning in healthcare organizations is used to gather information  from large healthcare datasets helping medical practitioners facilitate medical research and decision making ideas, improve patient care outcomes, automate daily  workflows, systematize diagnosis, surgeries and so on. Thus, machine learning algorithms in healthcare increase efficiency of several medical operations.

Why Is Machine Learning Important?

AI in healthcare is important because of its uniquely optimized models and tools that play crucial roles in maintaining accuracy, quality, and effectiveness in providing integral medical services. These machine learning models are specifically generated for performing individual healthcare tasks in shorter duration. ML tools are basically helping medical practitioners and its supportive teams to balance workflow and patients’ care, efficiently. 

• Machine learning algorithms can help to design different applications in several industries, ranging from feature recognition in security services to numerous aspects of healthcare, biomedicine, and biotechnology. Significant operational changes have taken place with the advent of AI in the healthcare and medicine industries. The ongoing advancements and upgradation in machine learning in healthcare have been beneficial for physician’s ability to perform their duties accurately.

• Artificial intelligence and machine learning is to channelize recent healthcare trends. It is also to generate disease identification and prediction models for the healthcare institutions. In the broader spectrum, the roles of machine learning in medical institutes are to provide systematic and organized structure to the electronic health records, patients data, medicine and equipment records. 

• Machine learning in the healthcare domain uses medical imaging models to identify abnormalities in the blood samples, organs, and bones and further monitoring it.  Machine learning models for healthcare are also used to generate AI robots to assist in micro to open surgeries. 

• The functioning of ML and artificial intelligence models in healthcare solely relies on fundamentals of mathematics and computer programming principles for providing improved managerial and decision-making notions and reducing several risk factors in the medical field.

Application Of Machine Learning In Healthcare

There are multiple usages and applications of ML for healthcare. Starting from online-symptom checkers,  bionic pancreas for chronic diabetics, patient apps, e-triage tools, to virtual agents, healthcare and machine learning are now sharing a bond to enhance the coherence of healthcare services. Let us check out some popular applications and usage of machine learning in medicine and healthcare.

• Medical Image Annotation for Disease Outbreak Prediction
• Accurate Diagnosis Models
• Automated Robotic Surgeries
• Drug Development and Discovery
• Risk-Identification Patterns in Patients
• Virtual Nursing
• Hospital Management Optimization Models
• Electronic Health Record (EHR) Organizer Models
• Automated Health Insurance Procedures. 
• Machine learning tools for gene-editing and other Genetic Engineering and Genomic operations.

How Does AnnotationBox Help In The Application Of AI Models In The Healthcare Sector?

In the Annotation Box, our annotators use computer graphics and artificial intelligence models to generate high-quality and effective synthetic data. This data is then used to train and test medical machine-learning models. It helps in creating unique algorithms for different AI approaches in healthcare applications. This large sample of synthetic data is then annotated to the AI healthcare application models, allowing them to learn numerous patterns and enhance the accuracy level in detecting and determining diseases and its potential treatments. 

Services that we offer to the healthcare industry are:

Image Annotation Services In the Healthcare Industry

Image annotation is a process of marking objects in medical imaging like X-RAY, MRI, CT-SCAN, and so on. We provide several segments of image annotation services for the medical field. They are:

• 3D Box labeling 
• Semantic labeling
• Medical Image Breakdown
• Patient Monitoring Systems
• Image Labeling of Blood Vessels and Cells 
• Labeling Unique Medical Conditions And Symptoms

Video Annotation Service In Healthcare

Our video annotation service for healthcare understands the growing essentiality of videography in the medical domain and how to accurately mark video frames with crucial entity-attribute models or metadata. This method of video annotation strengthens patterns of patient monitoring, analyses surgical procedures, and promotes advanced training for healthcare professionals. Our video annotation services include:

• Precise Surgical Analysis Through Video Annotated Data
• Patient Monitoring and Recovery Models
• Remote Telemedicine Video Markups
• Video Annotation Labeling For Radiological, Neurological and Ophthalmological Studies.

Audio Annotation Service In the Medical Field

Our audio annotation and audio marking services for medical approaches aid real-time healthcare applications like monitoring health conditions, facilitating diagnostic support, and identifying acoustic features within clinical data files. Sound labeling services that we offer:

• Tactical Audio Segmentation For Accurate Diagnosis
• Acoustic Feature Recognition Services
• AI-based Audio Classifications Of Health Conditions and Symptoms

Text Annotation Service In the Health Sector

Our team of text annotation experts at Annotation Box delivers a stream of text analytics support in the medicine sector through AI models. We use advanced text annotation software and tools to maintain a steadfast patient history, medical records, and NLP annotation services. Our text annotation service caters to:

• Text Segmentation
• Medical Entity Recognition Annotation
• Radiological Report Annotation
• Text Annotation of  Clinical Data and Medical Records

Content Moderation Service In the Healthcare Domain

Our experts provide rigorously optimized medical content to regulate patients with up-to-date healthcare information and advice using unique AI models and tools. It benefits patients with accurate and user-friendly information and refrains from getting misled by scattered online content. Our unique medical content moderation services include:

• Unique AI-Optimized Medical Content Moderators
• Telemedicine Content Moderation
• Filtering User Data Contents
• Monitoring Patient Feedback Contents

Opportunities And Benefits Of Machine Learning In the Healthcare Industry

The impact of AI in the medical sector is setting an outstanding example for accuracy and effectiveness. The healthcare industry has been benefiting a lot from ML approaches. Let us focus on some real-time benefits of AI models in medicine and medical care services. 

1. Improvements In Clinical Trials and Medical Research

You can use ML or deep learning in healthcare to augment the selection procedure of participants for any sort of clinical trial and analysis of the trial’s outcome. Machine learning is used in healthcare to study research findings from previous clinical trials and identify earlier side effects of drugs. These types of healthcare machine learning models in clinical trials  help to improve patient care and the safety and effectiveness of medical procedures.

2. Improvement In Trauma Care Services

Machine learning models for healthcare have improved palliative and trauma care services noticeably. The sensors and devices are fed with algorithms, and programs which are connected to the body of patients in the critical care units. It immediately transmits any kind of abnormal changes in the vitals of the patients or muscle movements to the concerned nurse or doctor. The moment they get the alert notification, they rush to attend to the patient, saving lives and a lot of time.

3. Clear Analyzation Of Biomedical Entities

Geneticists and forensic scientists rely on machine learning for creating three-dimensional illustrations of biomedical entities such as RNA sequences, DNA helixes, genomic profiles, and protein structures. 

4. Determining Medical Trends

Machine language in health care is used to identify medical trends, determine similar links and form a circumstantial inference from the data set. Healthcare industry is now using machine language to predict epidemics. Machine learning in healthcare can also depict an upcoming disease within communities by tracking habits and potential symptoms of the people. 

5. Innovation In Medicine Development

Machine learning and medicine development are now going parallely after the advent of automation in healthcare applications. Researchers are now using machine learning medical drug discovery models to identify potential molecules needed to manufacture advanced medicines for existing ailments and terminal diseases. Scientists use both machine and deep learning for inventing new vaccines,also.

6. Cost Effective Data Collection And Management Procedure

Machine learning for healthcare is used to develop advanced EHR algorithms for tracking patient records. It is used to schedule outdoor patients appointments, as well. This type of machine learning system helps the healthcare industry to cut short on miscellaneous expenditures and excess amounts of time that are spent on repetitive tasks. 

7. Enhanced Security And Privacy For Data Governance

Machine learning technologies in healthcare upgrade patients’ data security by automatically detecting and instantly responding to real-time cybersecurity threats. Medical authorities now use machine learning in healthcare to detect and protect data trespassing by decoding unnatural patterns trying to breach the system to hack patients’ information.

Medical annotation services are now widely used for adequate data labeling in the healthcare domain. The healthcare industry also uses machine learning algorithms to determine disparities within diverse datasets and clear those data.

Challenges Of ML In Healthcare

• Everything in the healthcare industry cannot be done by machine learning algorithms or AI technologies. The feeds for machine learning models in the healthcare industry must be curated in such a way where computer systems can more conveniently find matchable patterns and ideal inferences.

• Machine learning algorithms can often suffer from biased approaches as, ultimately, it is human beings who train them. Therefore, at times, ML’s implications in healthcare and life science-related fields can be life-taking. The prevailing biases in humans on issues related to medical science might creep in while training the models. The most concerning part is that these models save these notions and apply them directly to all the similar patterns of cases they detect. This affects the probable diagnosis and disease prediction, leading to even patient fatality. After all, humans have minds, and machines do not!

• Another challenge of machine learning in healthcare involves the unavailability of high-quality and large sample data needed for conducting training and testing of the ML algorithms. The large data sample sizes ensure high reliability in producing predictions. As machine and deep learning algorithms learn from these samples and data, the importance of data quality is inevitable. Moreover, the large amounts of featured data required for these deep learning models and machine learning approaches are not always available or may be based on narrow population sample distributions.  

• Implementation of machine learning and deep learning into critical healthcare operations requires vivid knowledge of medicine and hospitality, as well as a deep understanding of technologies and technical applications. On the one hand, many times, AI models come with improper incorporation of clinical approaches. On the other hand, expert clinicians and doctors do not possess sufficient understanding of the ML applications in providing tangible input and compilations.

Can AI and Machine Language Replace Medicos?

While the advancement of machine learning in the healthcare industry delivers upskilled features and possibilities, it is implausible for ML to utterly replace doctors. Instead, machine learning and healthcare have settled on to design a priceless bonding in the medical field. These medical venturists benefit thousands by allowing healthcare to enhance diagnostic and treatment potentialities and improving PROMS (Patient Outcomes Measures). Doctors with the help of machine learning can fully concentrate  on the factor of providing patients post-trauma care that requires human expertise and empathy. 

The future of AI and machine learning in the healthcare industry lies not in choosing between automated treatments and doctors or medical professionals. Rather, machine learning in healthcare involves benefiting from both humans and AI’s strengths for creating a more effective, accurate, skillful, and compassionate healthcare system.

Comparing AI-Generated Machine Learning and Deep Learning

ML in healthcare is somewhat dependent on manual inputs and often needs domain specialists to detect relevant features before feeding them to the ML models. In contrast, deep learning can directly learn from available raw data. Therefore, deep learning is precisely used in genomics and medical imaging processes.

Examples of machine language in the healthcare industry revolve around diagnostic support systems, tools for risk management, and applications for patient monitoring services.  In contrast, AI-based deep learning has devised more  advanced mechanisms and applications such as automatic detection of cancerous lacerations in the x-ray picture of breasts known as mammograms or assessment of cardiovascular risks from retinal- vessel caliber images.

Examples Of Companies Using Machine Learning In Healthcare

Most medical and healthcare conglomerates and industry giants have been using ai models to smoothen healthcare workflows. Right from surgery assistance models, medical trend denoting algorithms, drug discovery to regulating EHR of patients, companies are using automation widely. 

Some of the companies using machine learning in medical science applications are:

• DeepMind
• PathAi
• Pfizer
• IBM Watson Health
• Beta Bionics
• Tempus
• BioSymmetric
• Microsoft
• Oncora Medical
• Linus Health
• Subtle Medical
• Insitro
• Healthcare Live Health
• Ciox Health

Before Winding Up

AI-based Machine learning algorithms and deep learning models in healthcare are creating a striking impact on the medicine and biotechnology domains. Healthcare corporations are financing extensively on AI for enhancing accuracy and PROMs. The propagation of medical AI companies in the healthcare industry is acting as a testimonial to the sector’s growth.

If reports are to be believed, machine learning for healthcare 2024 to 2030 will experience a giant hike in the automated healthcare market size from the existing growth rate. 

This boom depicts the potential of automated models and the trust venture capitalists have in AI-driven innovations for the healthcare industry.