A Machine Learning in Healthcare SWOT Analysis

 





Photo by National Cancer Institute on Unsplash
Machine Learning is making sweeping improvements to many industries, healthcare being one of them.  The impact ML can have on a sector or industry is as unique as the industry itself.

  • Pharmacology is able to refine products by miniscule amounts to ensure quality and safety. The layer of coating that covers pill tablets is being reevaluated for accuracy by ML's capacity to find novel patterns in vast amounts of data.

  • Resource management in hospitals is able to track inventory in real-time, giving physicians updates on what supplies are available. This is also helping in streamline the patient intake process.

  • Radiologists are using AI to help identify chronic pulmonary hypertension faster using ML to find new pattern indicators.

  • Personalized medicine, or precision medicine, is using ML to create treatment plans specific to a patient with increasing accuracy using predictive analysis. 

So where does this leave business managers?

The time to begin integrating AI into healthcare is now to ensure that your firm is able to keep up with competition and provide the best possible care to patients. Analyzing the strengths, weaknesses, opportunities, and threats of a business decision is a standard procedure known as a SWOT Analysis. It studies the internal and external environment to address problems before they occur and to make best use of possibilities.

SWOT Analysis of Machine Learning in Healthcare

Strengths

  • Increase workplace productivity
  • Increases collaboration across industry
  • Increasing amount of AI expertise
  • Strengthens quality of care provided

Weaknesses

  • High cost of product development
  • Few sample algorithms available
  • Lack of public trust
  • Lack of expertise in applying ML specifically for healthcare-related purposes

Opportunities

  • Increasing investment interest in ML and AI
  • Willingness to collaborate
  • Increasing R&D costs make ML a sensible next step to decrease costs
  • High need for ML's predicative analysis for insight into planning for future events.
  • Need to tackle more complex, rare, and chronic diseases
    Photo by CDC on Unsplash

Threats (Risks)

  • Strict regulatory guidelines make for slow progress
  • Questions of data integrity
  • Questions of accountability 

We are all waiting to see what happens next, but there is no time like the present to look to the future!







Make your own analysis, check out these resources


By Dr Mark Roberts, AI Consultant and AI Lead at Tessella
 & Dr Sam Genway, Principal AI Solutions Engineer at Tessella
Drug Discovery World
Oct 19, 2019

By Iolanda Bulgaru, Healthcare Weekly
May 12, 2020

Medicalfuturist.com
March 3, 2020


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