DIAGNOSTICS

Artificial Intelligence or AI’s application fields are rapidly expanding. This technology is already being utilized to enhance cybersecurity, manufacturing, education, and logistical operations. However, does AI have anything to offer healthcare?

According to a recent paper published in the Future Healthcare Journal, artificial intelligence will increasingly be used in the healthcare field, particularly for diagnostic and treatment recommendation tasks, patient engagement and adherence, and administrative tasks performed by the healthcare workforce.

Correct illness diagnosis requires years of medical expertise. Even yet, diagnostics is frequently a lengthy and time-consuming operation. In many fields, demand for knowledge considerably outstrips supply. This puts clinicians under pressure and often results in the delay of life-saving patient diagnosis.

Machine Learning algorithms – notably Deep Learning algorithms – have lately made significant progress in automatically identifying diseases, lowering the cost and increasing the accessibility of diagnostics.

How machines acquire diagnostic abilities

Machine Learning algorithms can be trained to recognize patterns in the same manner that doctors do. A critical distinction is that algorithms require many concrete examples – many thousands – to learn. Additionally, these examples must be meticulously scanned because machines cannot read between the lines in textbooks.

So, Machine Learning is particularly helpful in areas where the diagnostic information a doctor examines is already digitized, such as:

• Detecting lung cancer or strokes based on CT scans
• Assessing the risk of sudden cardiac death or other heart diseases based on electrocardiograms and cardiac MRI images
• Classifying skin lesions in skin images
• Finding indicators of diabetic retinopathy in eye images

Whereas machine learning outperforms humans in raw computational capacity, what does this mean for physicians in the diagnostic field? In reality, while AI will continue to evolve and will undoubtedly play an increasingly prominent role in tasks such as visual analysis, it’s difficult to envision a time when human physicians might be considered even close to obsolete in diagnostics.

While verified AI algorithms may eventually take over routine diagnosis of non-life threatening ailments, the human component in care delivery cannot be understated. For instance, very few individuals would prefer to get a cancer diagnosis via an impersonal computer readout over a lengthy meeting with a sympathetic human specialist who can quickly answer their questions and explore their alternatives. Even in pattern recognition, a diagnostic strength of machine learning, people benefit from additional context regarding the patient’s background and lifestyle, stress levels, and recent activities.

Indeed, with the latest advancements in deep learning and neural networks, the most sophisticated AIs are effectively mimicking the human brain’s learning process, but with all the advantages of inexhaustible memory and none of the limits of human systems or complacency. After all, when a human doctor makes a mistake, it may take months or even years for it to become apparent, and in many cases, the diagnosing physician is unaware of their blunder.

Regional Outlook (Revenue, USD Billion, 2021 – 2027)

• North America
  • The U.S.
  • Canada
  • Mexico
• Europe
  • Germany
  • The U.K.
• Asia Pacific
  • China
  • Japan
  • India
• South America
  • Brazil
• Middle East and Africa (MEA)

Competitors

• diagnostics.ai
• Google Health/Deep Mind
• IBM Watson Health
• Oncora Medical
• Cloud MedX Health
• Babylon Health
• Corti
• Butterfly Network
• Arterys
• Caption Health
• Enlitic

Topics this report will cover:

• Market size from 2019 to 2027
• Revenue Forecast from 2020 to 2027
• Market segmentation and active players
• Market trends and opportunities
• Competitive landscape 
• Regional analysis 
• Related Industries 

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