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Will 2024 Be The Year AI-Assisted Clinical Decision Support For Doctors Finally Happens?

Mar 13, 2024 - forbes.com
The article discusses the potential of Generative AI (GenAI) in the healthcare industry, particularly in Clinical Decision Support (CDS). Despite the slower adoption of GenAI in healthcare compared to other sectors, there have been significant strides in AI-powered administrative assistance. However, there are challenges such as the "black box" models that offer no insights into how the output is generated, and the inconsistency of general AI chatbots. The article suggests that 2024 could be a pivotal year for the progress of CDS in AI-powered healthcare, with the introduction of domain-specific models that are tailored to specific industries or subsectors.

The article also emphasizes the need for regulatory advancements to ensure the ethical use of AI in healthcare. It argues that without clear compliance boundaries and parameters, healthcare providers will continue using unsanctioned AI tools, which could impede their efficiency and care delivery. The article concludes by suggesting that the next year should focus on expanding AI-based CDS use through pilot projects, rigorous testing, and commercialization across various healthcare facilities.

Key takeaways:

  • The healthcare industry has been slower in adopting generative AI (GenAI) compared to other sectors, despite its potential to improve patient care and administrative efficiency.
  • AI-assisted clinical decision support (CDS) is being utilized by doctors, despite some predicting it is still several years away from being reliable for patient diagnosis and treatment plans.
  • There are ongoing issues with GenAI, including 'black box' models that offer no insights into how the output is generated, and the inability of general AI models to distinguish between factual and non-factual information.
  • The year 2024 could be pivotal for AI-powered healthcare, with the potential for increased integrations, in-field testing and pilot projects, but this progress depends on regulatory advancements and the development of domain-specific AI models.
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