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How Machine Learning is Revolutionizing the Healthcare Industry

  • Evan Rogen 
machine learning in healthacre

Among all, the healthcare industry has always been a strong benefactor and has adopted
new technologies with open arms. It is getting benefitted and transformed by implementing
Artificial Intelligence and Machine Learning. The industry has already applied Big Data tools
for advanced data analytics and now Machine Learning is all set to help them improve the
process of automation & better decision-making in initial patient care and public healthcare

Applying ML in healthcare industry solutions also assists in disease detection more
accurately at a rapid speed, patient care, and individualized treatments. Machine learning in
the healthcare industry
is now a growing field of research as patient data is becoming more
readily available for professionals and health systems that are helpful for treatment.
According to Grand View Research, the market of AI and ML worldwide which is valued at
$15.4 billion in the year 2022 is projected to grow at 37.5% CAGR from 2023 to 2030.
Below article will update you on how ML-integrated healthcare IT professionals are
benefiting the industry with its outstanding features. Let’s start-

The Role of Machine Learning in the Healthcare Industry

Machine Learning can be considered a specific kind of Artificial Intelligence that enables
systems to take help from data and analyze patterns without much human involvement. ML
is used in IT solutions to help businesses with process automation & streamlining,
personalize healthcare, etc. Machine Learning can be used to program systems or
computers to make predictions & connections while finding vital insights from large-scale
data that sometimes might be missed by healthcare providers.
The main objective of this technology is to augment patient results and generate essential
medical insights that were unavailable before. In particular, ML is the most exciting area of
AI and there are a lot of firms that are leveraging ML while attaining healthcare app
development services. The technology is able to detect and treat complex diseases and

overcome the occurred challenges in the healthcare industry such as lack of quality data,
patients’ safety, data privacy concerns, and many more.

Benefits of Machine Learning in Healthcare Systems

Integrating machine learning technology in IT solutions with the help of healthcare app
developers will benefit the industry in numerous possible ways. The technology is
amalgamated to deal with large data sets, improving data sets, diagnosis & treatment, cost
reduction, etc. Let’s look at more of it:

  • Better Patience Experience
  • Improved Decision-Making
  • Enhanced Innovation
  • Automated Processes
  • Decreased Costs
  • Fewer Risks
  1. Better Patience Experience
    Machine learning applications in the healthcare industry come with virtual assistants &
    chatbots that lead to improved experiences for patients by managing and simplifying the
    approach to overall healthcare services.
  2. Improved Decision-Making
    Healthcare IT solutions are beneficial in pattern detection for large data sets. Machine
    learning helps professionals to modernize analytics and improve decision-making processes.
  3. Enhanced Innovation
    The main motive of healthcare firms and pharmaceutical companies behind ML-integrated
    healthcare app development is to get a solution to lessen the time-to-market while being
    exceptionally fast in detecting diseases and saving costs.
  4. Automated Processes
    Machine learning solutions can help to streamline the EHR processes, virtual nursing, and others. The technology also assists in automating various repetitive and routine tasks.
  1. Decreased Costs
    Machine learning algorithms are used to improve the productivity of the healthcare realm
    and manage patient records leading to cost savings and advanced resource management.
  2. Fewer Risks
    ML technologies enable predictive analysis for early recognition of crucial illnesses, reduce
    risks at the time of robot-assisted operations and analyze high-risk patients.

Popular Use-Cases of Machine Learning

From handling patient records to enhancing hospital efficiency to precise disease diagnosis,
Machine Learning technology has proven itself finely. But the potential of this technology is
beyond this, hence, the expectations are high and can be fulfilled only by taking help from a
software development company. Here are some significant use cases of Machine Learning in the healthcare industry-

Identifying & Diagnosis of Disease
Healthcare IT solutions combined with ML are helpful to detect or diagnose diseases that
need to be cured as soon as possible. It leads to providing patients with a secure way of
living their lives. There have been developed different image diagnostic tools that are
considered a part of AI-driven diagnostic procedures. Machine Learning uses the blend of
both supervised and unsupervised which helps health professionals by providing early
identification of diseases.

Robot-assisted Surgery
Operations and surgical processes need great expertise and exactitude along with
adaptability to handle every situation and a relentless approach for a long time. Though experienced and trained surgeons have all these, ML is also providing robotic assistance to accomplish these tasks. ML-powered surgical robots can perform intricate surgical procedures with side effects, excessive blood loss, or higher pain.

Improve Treatment Procedure
ML improves treatment procedures by accelerating patient contribution which leads to
better health outcomes. The use of a deep learning model helps to analyze related data
directing to drug discovery and producing new drugs used to cure diseases. These types of
healthcare machine learning could help to improve entire treatment and patient care along
with the safety and efficiency of medical processes.

Manage online Appointment Scheduling
ML-enabled healthcare IT solutions support managing billing, appointment records &
rescheduling them, giving consultations to patients, setting up reminders, and many more.
This is all done with the help of identifying clinician calendars and then, giving an
appointment rate. Benefits like medical imaging and disease diagnosis are additional
advancements provided by ML in healthcare systems.

Identify Patient Data
Machine learning examines patient data and assistances in disease identification that are
hard to detect. With the help of this advanced technology medical imaging has become
incredibly easy as the involved algorithms can handle excessive pathology and radiology
data while making them fast to process.

Ethics for Applying Machine Learning

The integration of AI and ML in healthcare practices causes some ethical considerations.
Below are some of the noted concerns related to the healthcare industry needed to be kept
in mind by healthcare professionals and experts:

Data Security & Privacy
Following the HIPAA and other similar privacy regulations ensures the patients’ data security
as they have the right to keep the data private. Misuse or leak of healthcare data can lead to
many incidents with patients. The only solution to prevent the data is to anonymize the
patient’s identity including specific data security approaches.

Algorithmic Biases
The efficiency and reliability of an AI system depend on how it is trained referring to data
interpretation and then, performing all the tasks with accuracy. Hence, AI experts must
ensure to address the risk and reduce the biases at every point. But they should consider
one fact it doesn’t negatively affect the influence of healthcare solutions.

Autonomy Issues
Machine learning can be used in monitoring elderly people with some disease or
psychological issues and making decisions for their better health. It comprises concerns like
healthy habits, the right meditation, and the required specialist. But this act will surely
impact their autonomy and limit their choices.

The Future of Machine Learning
To make it to more decades, machine learning technology is hoping towards delivering
greatly preemptive and foretelling healthcare solutions. But it will not be an easy journey,
instead of that, it is going to be a really long and intricate one requiring several stakeholders
comprising IT companies, governments, and healthcare professionals to work in sync. Some
major driving forces of market growth include enhancing demand for personalized
medicine, growing datasets of patients’ health, increasing requests for lessening care
expenses, and more. Machine Learning technology has already positively affected the
healthcare industry and there is a bright future ahead to improve medical care and results.