Emerging Tech In the Life Sciences — What You Need to Know

Yogesh Phulke
Sr. Cloud Engineer
RCH Solutions

The Life Sciences and Healthcare industries have long been driven by a commitment to innovation. The introduction and advancement of technologies over the past 50 years has increased the pace at which science moves forward in unimaginable ways. Now, emerging technologies like artificial intelligence (aka, “AI”), among others, present even greater opportunities to push the limits of discovery.  

But for many IT teams, understanding when, where, and how these capabilities best fit within the Bio-IT ecosystem can be both challenging and frustrating. More importantly, distinguishing between the viability of these tools today and the promise they offer for tomorrow can be the deciding factor in whether or not the capabilities will actually add value to your R&D computing workflows. 

What is AI? 

Artificial intelligence refers to the use of automated algorithms to perform tasks that traditionally rely on human intelligence. From the voice-controlled Siri or Alexa to self-driving cars AI is growing rapidly. AI can be portrayed as Robots with human-like intelligence or emotions, and it can embrace anything from Google search algorithms to Facebook’s facial recognition needs.

Though the long-term goal of those working to advance the tech is to create an AI that would outperform humans at nearly every intellectual task, artificial Intelligence today can perform only relatively light-weight tasks such as data analysis, internet searches, facial recognition, and self-driving cars. 

Artificial Intelligence in Life Sciences and Healthcare

Over the last few years, the use of artificial intelligence has redefined how scientists address a disease, develop new drugs and improve patient outcomes. Computers are able to perform analysis and uncover new datasets at an infinitely quicker pace than human analysis. As a result, new and effective drugs can be made available sooner, and diseases previously deemed too difficult to take on are now gaining the attention of more research teams. 

Some ways AI is currently being used effectively within the Life Sciences and Healthcare spaces include:

  • Apple is using AI to screen children for Autism
  • IBM Watson helps match patients with the right drug trials
  • AI-assisted robotic surgery improves the efficiency of surgeons
  • AI is taking precision medicine to the next level and increasing the accuracy and prediction of outcome for patients, as well as predicting a patient’s probability of disease
  • Google Deep Mind AI is developing tools and technology capable of helping millions of patients around the world, improving patients outcomes

The Big Question: Is AI Right for Your R&D Goals?

Before you can answer that question, you have to start by asking yourself the following:

  1. Will AI change your outcome significantly versus performing the same task traditionally? 
  2. Do you want AI to step-analyze data and provide you with recommendations you can execute on your own? 
  3. Do you want technology to take actions on your behalf in pursuit of defined KPI’s?

It’s also important to be able to explain why traditional methods have failed until now and why AI is necessary for you to succeed. At RCH, we’ve supported many customers in their effort to incorporate artificial intelligence into their projects. Here are a few of the most successful use cases we’ve seen: 

  • Automating Administrative Tasks – Technology such as voice-to-text transcriptions could help the company order tests, prescribe medications and write chart notes.
  • Accelerating Drug Discovery – The use of AI in R&D has helped pharmaceutical companies streamline drug discovery as well as drug repurposing, particularly in oncology drug discovery programs.
  • Detecting Disease – AI is improving the early detection of diseases like cancer and retinopathies. The use of AI in analyses and reviews of mammograms and radiology images can help speed up the process up to 30% and with 99% accuracy.
  • Streamlining Communications Between Patients and Providers – Virtual nursing assistants are available 24×7 to monitor patients and answer questions, allowing for more regular communication and preventing unnecessary hospital visits.
  • Improving Patient Outcomes –  AI is being used to help more people stay healthy. The Internet of Medical Things (IOMT) in consumer health applications has seen significant growth in the last decade or so. Companies are now more easily tracking cardiac health, fall detection and emergency SOS.

The Flip Side of AI:

Despite all of the potential AI promises, there are a number of considerations related to data privacy and ethics associated with the use of AI, particularly as they pertain to direct patient care. Who would be held accountable for machine errors that could lead to mismanagement of care?  Would patients be informed of the extent of role AI is playing in their treatment? Would AI encourage patients to not seek advice from the medical practitioner and indulge in self-diagnosis and medication? Could the health practitioner be threatened by AI about a potential loss in authority and autonomy?  While these are just a few of the questions still swirling around the use of AI, the larger theme is this:

AI is a buzzword and while it does offer some incredibly differentiating potential, it is important to understand what it actually helps to avoid a potentially expensive investment in a technology that won’t add the value you need it to.

Looking for support for your AI initiatives?

An experienced Bio-IT partner can help you determine if AI is right for your project. At RCH, we’ve helped our customers successfully navigate and leverage an evolving technology landscape to best meet their R&D IT needs for nearly three decades. Talk to us about how we can help you, too.

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