Call for Papers

Call for Papers

The Fifth Elephant Papers Reading community

Harini Anand


PWL MAY 2024: Med-PaLM M: A generalist biomedical AI system that flexibly encodes and integrates multimodal biomedical data

Submitted Feb 29, 2024

About the Paper:

  • Medicine is inherently multimodal and clinicians interpret data from a wide range of modalities when providing care, including clinical notes, laboratory tests, vital signs and observations, medical images, and genomics.
  • Since issues like clinician burnout and lack of access to quality healthcare were rising, the emergence of foundation models proved to be a turning point in addressing and solving these global problems.
  • Med-PaLM M is a large multimodal generative model that interprets biomedical data spanning clinical language, medical imaging, genomics, and more performing competently on a diverse array of tasks - all with the same set of model weights.
  • It is built by fine-tuning and aligning PaLM-E - an embodied multimodal language model developed by the researchers at Google AI using MultiMedBench, a newly curated open source biomedical benchmark containing 14 diverse tasks such as medical question answering, mammography and dermatology image interpretation, radiology report generation and summarization, and genomic variant calling.
  • The future of AI in medicine and bio is incredibly exciting! This paper dives deep into why that is the case, so do join in :)
    Link to the paper:

Key Takeaways:

  • How LLMs help scale up world-class healthcare on a global scale.
  • Understand the importance of AI in clinical workflows and how it exponentiates efficiency and utility with its multimodality.
  • Demonstrate the importance of biomedical fine-tuning and alignment.
  • Learn about zero-shot generalization to novel medical concepts and tasks, positive transfer learning across tasks, and emergent zero-shot medical reasoning.
  • Build intuition for developing large-scale generalist biomedical AI with language as a common grounding across tasks which shows the possibility of combinatorial generalization and positive task transfer.

About the Speaker:

Harini Anand is a CSE Undergrad with a keen interest in Computational Cognition. She is working at Niramai Health Analytix, a deep tech startup focusing on Breast Cancer, and is using ML frameworks as a research intern at the Indian Institute of Technology Hyderabad, to predict gene regulatory networks. She has previously worked on building cognitive tools for reducing the onset of Dementia as a student entrepreneur.

She leads the largest technical community on campus and also has imparted knowledge to peers through workshops on Machine Learning, NLP, and Data Science. She’s been admitted to top summer schools at Oxford University, London, and Massachusetts Institute of Technology which specialize in the applications of AI in Healthcare. She is a strong advocate for representation in STEM and is recognized as a High Impact APAC Ambassador for the Women In Data Science Community, an initiative by Stanford University. She is also a Harvard WE Tech Fellow.

To know more:


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