Med-Flamingo: a Multimodal Medical Few-shot Learner - Discussion, Acknowledgments, and References

by The FewShot Prompting Publication June 19th, 2024

Too Long; Didn't Read

This is an early proof-of-concept for a medical multimodal few-shot learner. We expect to see significant improvements with increased model and data scale, more thoroughly cleaned data, as well as with alignment to human preference via instruction tuning. In all VLMs we analyzed, hallucinations were observed. It is possible that the model occasionally outputs low-quality generations.

featured image - Med-Flamingo: a Multimodal Medical Few-shot Learner - Discussion, Acknowledgments, and References

Authors:

(1) Michael Moor, Department of Computer Science, Stanford University, Stanford, USA and these authors contributed equally to this work;

(2) Qian Huang, Department of Computer Science, Stanford University, Stanford, USA and these authors contributed equally to this work;

(3) Shirley Wu, Department of Computer Science, Stanford University, Stanford, USA;

(4) Michihiro Yasunaga, Department of Computer Science, Stanford University, Stanford, USA;

(5) Cyril Zakka, Department of Cardiothoracic Surgery, Stanford Medicine, Stanford, USA;

(6) Yash Dalmia, Department of Computer Science, Stanford University, Stanford, USA;

(7) Eduardo Pontes Reis, Hospital Israelita Albert Einstein, Sao Paulo, Brazil;

(8) Pranav Rajpurkar, Department of Biomedical Informatics, Harvard Medical School, Boston, USA;

(9) Jure Leskovec, Department of Computer Science, Stanford University, Stanford, USA.

Table of Links

Abstract and 1 Introduction

6 Discussion, Acknowledgments, and References

A Appendix

6 DISCUSSION

In this paper, we presented Med-Flamingo, the first medically adapted multimodal few-shot learner. While this is an early proof-of-concept for a medical multimodal few-shot learner, we expect to see significant improvements with increased model and data scale, more thoroughly cleaned data, as well as with alignment to human preference via instruction tuning or explicit optimization for preferences.

We expect that the rise of multimodal medical few-shot learners will lead to exciting opportunities with regard to model explainability (via rationale generation) as well as grounding the model in verified sources (via multimodal retrieval to augment the few-shot prompt). Thereby, our work serves as a first step towards more generalist medical AI models Moor et al. (2023).

Limitations This work demonstrates a proof-of-concept. As such, Med-Flamingo is not intended nor safe for clinical use. In all VLMs we analyzed, hallucinations were observed. Furthermore, as Med-Flamingo is a pre-trained model without further instruction or preference tuning, it is possible that the model occasionally outputs low-quality generations.

Future work It will be an exciting route for future work to further train Med-Flamingo on clinical data, high-resolution medical image datasets as well as 3D volumes and medical videos. While current general-purpose medical VLMs are pre-trained on the broad medical literature (i.e., they are only “book-smart”), also learning from diverse patient data directly will become crucial for down-stream applications.

ACKNOWLEDGMENTS

We thank Rok Sosic for his technical support in the data preprocessing.

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