Monday, July 15, 2024

Generative AI in Healthcare:  5 Ways It Is In Use Today

Artificial Intelligence has transformed several industries rapidly. Generative AI quickly followed and gained traction in the past few years as well. For healthcare, it comes with the disparate sources of unstructured data that powers generative AI as per this McKinsey report.  Gen-AI depends on unstructured data which it organizes and analyses to create content sets such as reports, charts and recordings. Apart from these, there are some unique use cases of generative AI that healthcare professionals must consider.

Potential applications of generative AI in healthcare

1. Enhancing medical imaging and diagnostics

Generative AI algorithms like variational autoencoders (VAEs) and generative adversarial networks (GANs) have boosted medical image analysis remarkably. These algorithms can generate synthetic medical images resembling real patient data. They helped significantly in the training and validation of machine-learning models. They have the skill to enhance limited datasets by generating additional samples thereby enhancing the reliability and accuracy of image-based diagnoses. 

Uses of Gen-AI in Medical Imaging:

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  • Radiology
  • Pathology
  • Surgical Planning
  • Improved accuracy of diagnostics

NVIDIA Clara and Zebra Medical Vision are already using AI algorithms for medical imaging analysis.

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2. Medical research and knowledge generation

Models based on generative AI in healthcare can enable medical research by generating synthetic data adhering to certain constraints and specific characteristics.

Synthetic data would address privacy concerns related to sensitive patient information. Simultaneously, they should enable researchers to gather valuable insights and formulate fresh hypotheses.

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Uses of Gen-AI in Medical Research:

  • Identify new drug targets
  • Identify genes & proteins associated with specific diseases
  • Supporting decision making with more accurate research

3. Facilitating the discovery and development of drugs

Discovering and developing new medicines or drugs is time-consuming, complex, and expensive. Generative AI on the other hand can expedite this process significantly by generative virtual molecules and compounds with required properties.

To explore the vast chemical space, researchers can use generative models to enable the identification of novel drug candidates. These models derive knowledge from existing datasets including known drug structures and associated properties. The learning is then used to generate new molecules with the right characteristics.

Uses of Gen-AI in Drug Discovery:

  • Identify new drug targets
  • Accelerate processes such as identifying drug candidates and testing efficacy

Creating virtual compounds

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4. Offer 24/7 patient support and follow-up

Generative AI can be used to enhance patient support and follow-up. Through the utilization of natural language processing, this technology can transmit customized reminders and offer responses to patients’ queries. Thereby, guaranteeing they obtain the essential guidance and information they require. Needless to say, such AI tools for hospitals would become a must soon enough.

Uses of Gen-AI in Patient Care:

  • Analyzing patient history to offer personalised treatment
  • Predicting patient outcomes
  • Providing patient-specific recommendations that can help healthcare providers take more informed decisions

5. Personalized medicine and treatment

Generative AI can revolutionize personalized medicine as well. It could tailor custom treatment plans based on the patient data.

Generative AI models could analyze a huge amount of patient information including genetic profiles, electronic health records, and clinical outcomes to come up with personalized treatment recommendations.

These models can recognize patterns, predict the progression of the disease, and estimate patient responses to intervention. All these shall enable healthcare providers to make informed decisions and offer a treatment strategy accordingly.

Mayo Clinic has already created a deep learning algorithm for enhanced treatment. It is able to predict any risk of complications as well as generate personalized treatment plans after a study of the risks associated.

Suggested read: Tackling healthcare’s biggest burdens with generative AI

Despite all these developments, generative AI in healthcare is still in its nascent stages because there is still the gap of larger data sets needed for accuracy. Additionally, while the algorithm comes with a result, a lot depends on how the results are interpreted. This will need significant work in healthcare AI tools that can be implemented safely and comfortably.

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Dipanita Bhowmick
Dipanita Bhowmick
Dipanita Bhowmick: I am a content writer with 13+ years of experience in various genres, allowing me to adapt my writing style to diverse topics and audiences. Alongside my passion for creating engaging content, I have a deep interest in esoteric knowledge, constantly exploring the mystical and unconventional realms for inspiration along with spiritual and personal growth.

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