Artificial Intelligence in Healthcare - What are the benefits?

Learn about how Artificial Intelligence In Healthcare is being implemented
The Benefits of Artificial Intelligence in Healthcare

Artificial Intelligence (AI) has the potential to have a transformative impact on the healthcare industry.

One-third of medical practitioners are already using artificial intelligence or AI in their practices (Healthitanalytics), and many believe there is ample reason to think this advanced technology can help address diagnostic errors, the largest cause of malpractice claims.

The idea of enhancing artificial intelligence in healthcare is nothing new. A Stanford article published in 1996 predicted the likelihood of death from AIDS from a data set of HIV patients much more accurately with AI technology than other methods used at that time.

A recent medical survey by the doctor’s company, the nation’s largest physician-owned medical malpractice insurer found that 53% of physicians are optimistic about the prospects of AI in medicine 35% are using AI in their practices. 66% believe that AI will lead to faster diagnosis 66% believe that AI will lead to a more accurate diagnosis.

Artificial Intelligence (AI) is basically the ability of computers and machines to use features generally associated with intelligence and humans such as learning problem-solving and reasoning to process data in the context of medicine. This means artificial intelligence in healthcare can be used to help doctors recognize and diagnose diseases much faster. Artificial Intelligence and the cost of healthcare economic experts claim that AI will help lower the cost of healthcare as its ability to detect problems more efficiently and accurately most of the time and speed up the development of potentially life-saving drugs will save you time and money.

Even though AI will require a significant amount of investment to be deployed in the field, the application of AI can potentially create $150bn annual savings in the US healthcare economy by 2026, according to Accenture.

By 2021, the AI health market is expected to reach $6.6bn, increasing from $600m in 2014 and representing a compound annual growth rate of 40% (verdict).

Some of the major present and future ways artificial intelligence helps in healthcare include:

  • Robot-assisted surgery
  • Virtual nurses
  • Symptom checking and triage
  • Treatment plans
  • Medication management
  • Precision medicine
  • Health monitoring
  • Healthcare system analysis

In addition to automating diagnosis, Artificial intelligence in healthcare can assist with prevention, forecasting the spread of diseases at the macro level as well as calculating the probability that an individual may contract a condition. This can drive positive health outcomes and assist providers with logistics and planning.

Robot-assisted surgery (providing guidance based on records and real-time data) is considered to be the AI healthcare application with the greatest expected financial benefit.

Virtual nursing (remotely assessing a patient’s symptoms) can reduce unnecessary hospital visits and save time for staff.

Artificial intelligence in healthcare can help with the day-to-day heavy lifting in healthcare provision.

Robotic Process Automation (RPA) can assist with a wide range of tasks in healthcare, including:

  • Patient appointment requests
  • Patient registration
  • Claims review and payment integrity complaints
  • Denials and appeals
  • Data entry
  • Provider credentialing, provider data management and network management
  • Billing, finance and accounting tasks
  • Care coordination, case management, and remote monitoring

RPA can reportedly drive savings of 20%-50% in the healthcare industry and, according to ISG’s Automation Index, provides double the productivity boost of IT outsourcing.

Robot Process Automation (RPA) in healthcare can save money and reduce opportunities for patient harm. Other benefits include:

  • Fluid and error-free collection and communication of data
  • Automation of routine, time-consuming tasks like claims review
  • Enhanced tracking and documentation of decision making
  • Scalability
  • Unburdened staff can prioritize tasks requiring creativity and judgment

AI and machine learning can assist the healthcare industry across a diverse range of tasks including:

  • Back office functions
  • Repetitive physical work
  • Communication with patients
  • Monitoring and diagnosis of conditions
  • Practical treatment of patients, up to and including surgery

The potential benefits from Artificial intelligence in healthcare include assistance with case triage, enhanced image scanning, and segmentation, supported decision making, integration and improvement of workflow, disease risk prediction, patient appointment, and treatment tracking.


New technology is expected to function perfectly, but AI may deliver worse outcomes than human practitioners if:

  • Given insufficient training data
  • Misused
  • IT malfunctions

Because of this, it is important that healthcare professionals deploy Artificial intelligence in healthcare appropriately and can monitor how decisions are reached.

To lessen any risks such as unexplainable results, unclear lines of accountability, physicians must seek training and the use of AI and adhere to the standards provided by the device companies.

Training will also enable doctors to fully and clearly articulate potential harms to patients in order to obtain true informed consent.

Hospitals and other practices are also key to ensuring proper development, implementation, and monitoring of protocols and best practices for use of Artificial intelligence in healthcare.

AI augments the skills and expertise of medical staff by automating repetitive tasks, ensuring they are completed quickly and consistently. This will leave doctors and nurses free to spend more time with patients and to do the things that AI may not yet be able to do, such as tackling unexpected real-world problems.

Rather than approaching AI as a broader digital transformation project, healthcare providers, who may be at a relatively low technological starting point, can pursue targeted deployments geared to specific patient outcomes. By focusing efforts on these tightly-defined goals, managers can then onboard AI far more quickly.

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