AI and the future of Medical Diagnostics

mediumThis post was originally published by Srushti Vora at Medium [AI]

What is AI?
  • AI refers to sophisticated software systems that enable computers to augment, or even emulate, human intelligence and decision making.
    Beneath the heading of AI is “machine learning,” which uses algorithms to parse and learn from data and then applies this learning to provide insight and make informed recommendations. The system learns by mimicking the perception and decision making of experts.
  • The key distinction between AI and other software or computer-based technologies is that AI has the capacity to learn and improve from data and experience. Other technologies can handle complex tasks but cannot perform actions or form conclusions to augment clinicians’ and patients’ care decisions that are not specifically programmed.

AI is going to grow at a rapid pace. Are there any figures to confirm this development?

  • 39% of all the managers in the healthcare sector are planning to invest in AI.
  • 69 percent expect widespread adoption of AI in the IVD lab within the next four years.
  • 92 percent expect AI to have a significant impact on healthcare eventually.

Why AI?

  • So much is in silos across diagnostic laboratories: data, analyzers, devices, even people — divided by walls, by processes, by distance or simply by force of habit. The result can be partial visibility, limited insight and duplication of effort. Artificial intelligence (AI) and digitalization are proving to be the tools to help transcend these barriers.
  • Some of the most evasive diseases could be easier to detect with AI-supported diagnostic technology that can scan thousands of medical records and analyze data that might escape the attention of tired, overworked medical professionals.
  • AI can also improve the time-consuming process of classifying medical records.
  • AI may be an aid to interpreting ECG results. Researchers at Uppsala University and heart specialists in Brazil have developed an AI that automatically diagnoses atrial fibrillation and five other common ECG abnormalities just as effective as a cardiologist.

The Future of Artificial Intelligence and Medicine

  • It has been noted that AI-backed technologies strive to free medical professionals from time-consuming, monotonous work and are not meant to supplant the skills and empathy of humans. And hopefully the technology will complement and enhance their work.
  • Diagnostic technology is helping improve the accuracy of finding tumors. Massachusetts General Hospital researchers utilize a machine learning model for diagnosing tumors. “In this method, researchers fed information on over 600 high-risk lesions — including details about the patient’s age and race, family history, past biopsies, and pathology reports — to an artificial intelligence. In trials, that artificial intelligence was able to interpret that data so effectively that the method resulted in fewer unnecessary surgeries and specifically diagnosed more cancerous lesions,” according to Boston Magazine.
  • Artificial intelligence-based software uses neural networks designed to emulate human thought processes. AI can recognize patterns beyond defined rules and analyze significantly higher volumes of information than humans could manage.

Need of a medical professional

  • The job description of a doctor is going to change. But it goes without saying that we will continue to need them.
  • Artificial Intelligence is a support tool for making a more accurate diagnosis and being able to work more quickly.
  • However, an algorithm can only be as smart as the data it is provided with. As a result, all of the med tech manufacturers are on a quest to find validated data, validated data sequences or curated data that details how patients were treated and how their survival rate was influenced by this. This is where medical individuals are needed.

Clinical analysis

AI can impact clinical analysis by 3 various processes.

  1. Process Automation :
  • Mainly focuses on the efficiencies.
  • This type of AI relates to automation of what we can broadly call ‘back-office’ function and it is deployed through more efficient handling of digital and physical tasks using technology.
  • The simplest and least expensive to implement.

2. AI Driven Data Analysis:

  • Mainly focuses on Precision.
  • AI methods could be separated into semi or fully automated.
  • It would allow us to recognize much finer details or pick up more subtle differences.

3. AI and Insight into the data:

  • This type of AI is used to detect patterns in vast volumes of data and interpret their meaning.
  • Such AI is typically used to improve performance or decisions only a machine can do.

4. Precision Medicine

  • It is moving us from one size fits all healthcare to personalized, data-driven treatment that enables more efficient spending and improved patient outcomes.
  • It offers a path to helping people recover from illness faster — and stay healthy longer.

Can AI influence changes in testing and treatment protocols?

Advanced data-mining analytics are increasingly being used to identify often-unexpected disease associations. These insights can be helpful to evaluate ECGs to predict which patients are likely to develop an irregular heartbeat — even when doctors interpreted the test results as normal.

How to prepare for AI?

  • AI needs large sets of data from which to learn and adapt.
  • For AI to be successful, the healthcare industry must come together as a whole to move toward data sources that are: Accessible, Interoperable, Standardized, Curated
  • To best prepare for emerging AI, healthcare organizations should educate key decision makers as well as modernize their current technology.
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This post was originally published by Srushti Vora at Medium [AI]

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