KEY HIGHLIGHTS
- MedLM Launch: Google announces MedLM, a family of generative AI models fine-tuned for the medical industry, aiming to assist healthcare workers in their tasks.
- Models for Varied Tasks: MedLM offers two models – one for complex tasks and another for scalable applications, recognizing the need for task-specific model selection.
- Customer Implementation: HCA Healthcare, a for-profit facility operator, is piloting MedLM for drafting patient notes, while BenchSci integrates it into its biomarker identification and ranking system.
- Competition in Healthcare AI: Google, Microsoft, and Amazon race to dominate the growing healthcare AI market, with Amazon launching AWS HealthScribe and Microsoft piloting AI-powered healthcare products.
- Historical Challenges in AI Healthcare: Despite advancements, the use of AI in healthcare has faced skepticism and challenges, citing examples like Babylon Health’s scrutiny and IBM’s sale of its Watson Health division.
- Concerns and Risks: The World Health Organization warns about risks associated with generative AI in healthcare, including potential inaccuracies, dissemination of misinformation, and the inadvertent leakage of sensitive health data.
- Google’s Cautionary Approach: Google emphasizes its commitment to responsible AI deployment in healthcare, focusing on ensuring safety, avoiding errors, building trust, and making benefits accessible to everyone.
Google has recently unveiled its latest venture into the healthcare sector with the introduction of MedLM, a suite of generative AI models specifically tailored for medical applications. This innovative development aims to assist healthcare professionals in their daily tasks and enhance overall efficiency within the medical industry.
Google’s MedLM: Advancing AI in Healthcare
MedLM is an evolution of Google’s existing model, Med-PaLM 2, renowned for its expert-level performance in addressing a myriad of medical examination questions. The service is currently available to Google Cloud customers in the United States, with a preview release in select international markets for those whitelisted through Vertex AI, Google’s fully managed AI development platform.
Comprising two distinct models, MedLM caters to the diverse needs of healthcare organizations. The larger model is designed for handling intricate tasks, while the smaller, fine-tunable model excels in scalability across various assignments. Yossi Matias, Google’s VP of Engineering and Research, emphasized the importance of customization, stating that the most effective model varies based on the specific use case.
In a statement provided to TechCrunch, Matias shared insights gained from pilot programs, revealing that optimal model selection depended on the nature of the task. For instance, summarizing conversations might be better suited for one model, while searching through medications could be more efficiently handled by another.
HCA Healthcare, a for-profit facility operator, has already been testing MedLM to aid physicians in drafting patient notes at emergency department hospital sites. Another participant, BenchSci, has integrated MedLM into its “evidence engine,” enhancing the identification, classification, and ranking of novel biomarkers.
Google is not alone in its pursuit of dominance in the healthcare AI market. Competitors like Microsoft and Amazon are also aggressively developing their AI solutions for healthcare applications. Amazon recently introduced AWS HealthScribe, utilizing generative AI to transcribe, summarize, and analyze patient-doctor conversations. Meanwhile, Microsoft is piloting various AI-powered healthcare products, including medical “assistant” apps built on large language models.
However, the integration of AI in healthcare has not been without skepticism and challenges. Past experiences with AI startups, such as Babylon Health and IBM’s Watson Health division, have highlighted the potential risks and pitfalls associated with relying on AI for medical diagnoses and information.
A study mentioned in the report raised concerns about the accuracy of generative models, including Google’s MedLM, in responding to healthcare-related queries. The study found discrepancies and inaccuracies, indicating that caution is essential when deploying such technology in critical healthcare scenarios.
In response to these concerns, the World Health Organization (WHO) issued a warning about the risks associated with using generative AI in healthcare. The WHO highlighted potential issues, including the generation of harmful misinformation, the disclosure of sensitive health data, and the possibility of unintended leaks of confidential medical records.
Despite these challenges, Google remains steadfast in its commitment to responsible and cautious deployment of generative AI in healthcare. Matias emphasized Google’s focus on enabling professionals with safe technology use, ensuring that the benefits of these advancements are accessible to everyone without compromising safety and ethical considerations. As the race for dominance in the healthcare AI market intensifies, the industry watches closely to see how these technological advancements will shape the future of medical practices.
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Source(s): TechCrunch
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