Gen AI’s impact on healthcare: Cutting-edge applications (and their challenges)

by | Jul 4, 2024 | Technology

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In just a short period of time, AI has demonstrated viable capabilities in healthcare: Large language models (LLMs) can offer tumor diagnoses, provide sleep and fitness advice, scan medical images and analyze MRIs, X-rays and tissue samples. 

For all its opportunities, though, there are significant — and valid — concerns around output accuracy, transparency, integration, data privacy, ethics, bias and regulatory compliance, among others. 

“The integration of AI into healthcare is not just an evolution but a revolution that holds the promise of significantly enhancing patient care, operational efficiency and medical research,” Timothy Bates, clinical professor of cybersecurity in the College of Innovation and Technology at the University of Michigan-Flint, told VentureBeat. 

But, he emphasized, “realizing this potential requires addressing substantial challenges.”

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AI throughout the medical workflow

To start, AI can take over time-consuming, repetitive tasks such as summarizing appointments (which it has already been shown to do better than humans). The technology can also streamline administrative processes like scheduling, billing and patient management. Further, AI-driven predictive analytics can help with resource allocation. 

“Despite legitimate concerns about generative AI, in five years, healthcare providers will wonder how they ever got along without it, especially for things like transcribing clinical notes and decision support,” said Dr. Colin Banas, chief medical officer at medication management company DrFirst. 

Going beyond that, AI can improve diagnostics because it can analyze vast amounts of data quickly and accurately, said Bates. For instance, AI algorithms can analyze medical images to detect conditions such as cancer, heart disease or neurological disorders earlier and more accurately than traditional methods.

One example includes AIdoc, which is helping transform radiology by detecting anomalies in medical imaging with high accuracy.

“AI has algorithms that can detect cancer in imaging much sooner than what doctors can do now, providing for earlier, less invasive treatment and a higher chance of survival,” said …

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We want to hear from you! Take our quick AI survey and share your insights on the current state of AI, how you’re implementing it, and what you expect to see in the future. Learn More

In just a short period of time, AI has demonstrated viable capabilities in healthcare: Large language models (LLMs) can offer tumor diagnoses, provide sleep and fitness advice, scan medical images and analyze MRIs, X-rays and tissue samples. 

For all its opportunities, though, there are significant — and valid — concerns around output accuracy, transparency, integration, data privacy, ethics, bias and regulatory compliance, among others. 

“The integration of AI into healthcare is not just an evolution but a revolution that holds the promise of significantly enhancing patient care, operational efficiency and medical research,” Timothy Bates, clinical professor of cybersecurity in the College of Innovation and Technology at the University of Michigan-Flint, told VentureBeat. 

But, he emphasized, “realizing this potential requires addressing substantial challenges.”

Countdown to VB Transform 2024

Join enterprise leaders in San Francisco from July 9 to 11 for our flagship AI event. Connect with peers, explore the opportunities and challenges of Generative AI, and learn how to integrate AI applications into your industry. Register Now

AI throughout the medical workflow

To start, AI can take over time-consuming, repetitive tasks such as summarizing appointments (which it has already been shown to do better than humans). The technology can also streamline administrative processes like scheduling, billing and patient management. Further, AI-driven predictive analytics can help with resource allocation. 

“Despite legitimate concerns about generative AI, in five years, healthcare providers will wonder how they ever got along without it, especially for things like transcribing clinical notes and decision support,” said Dr. Colin Banas, chief medical officer at medication management company DrFirst. 

Going beyond that, AI can improve diagnostics because it can analyze vast amounts of data quickly and accurately, said Bates. For instance, AI algorithms can analyze medical images to detect conditions such as cancer, heart disease or neurological disorders earlier and more accurately than traditional methods.

One example includes AIdoc, which is helping transform radiology by detecting anomalies in medical imaging with high accuracy.

“AI has algorithms that can detect cancer in imaging much sooner than what doctors can do now, providing for earlier, less invasive treatment and a higher chance of survival,” said …nnDiscussion:nn” ai_name=”RocketNews AI: ” start_sentence=”Can I tell you more about this article?” text_input_placeholder=”Type ‘Yes'”]

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