Luberlab Journal Club - AI in Healthcare Research

Paper List

Below is a curated list of recent and influential papers grouped by research area. This list highlights cutting-edge developments in AI and its applications.

Multimodal AI

1. “Flamingo: a Visual Language Model for Few-Shot Learning” (Alayrac et al., 2022)
2. “CLIP: Learning Transferable Visual Models From Natural Language Supervision” (Radford et al., 2021)
3. “ImageBind: One Embedding Space To Bind Them All” (Girdhar et al., 2023)
4. “BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models” (Li et al., 2023)

AI in Healthcare

1. “Foundation models for generalist medical artificial intelligence” (Singhal et al., 2023)
2. “Large language models encode clinical knowledge” (Singhal et al., 2022)
3. “A guide to deep learning in healthcare” (Esteva et al., 2019)

Efficient Fine-tuning and Multimodal Approaches in Medical AI

1. “ChatDoctor: A Medical Chat Model Fine-tuned on a Large Language Model Using Medical Domain Knowledge” (Liu et al., 2023)
2. “PMC-LLaMA: Further Finetuning LLaMA on Medical Papers” (Yuan et al., 2023)
3. “MedAlpaca: An Open-Source Collection of Medical Conversational AI Models and Training Data” (Chen et al., 2023)
4. “MMBERT: Multimodal BERT Pretraining for Improved Medical VQA” (Kumar et al., 2022)
5. “MedCLIP: Contrastive Learning from Unpaired Medical Images and Text” (Wu et al., 2023)
6. “LoRA for Efficient Medical Image Classification: Less is More” (Wang et al., 2023)

Large Language Models (LLMs)

1. “GPT-4 Technical Report” (OpenAI, 2023)
2. “PaLM 2 Technical Report” (Anil et al., 2023)
3. “Constitutional AI: Harmlessness from AI Feedback” (Bai et al., 2022)
4. “Scaling Laws for Neural Language Models” (Kaplan et al., 2020)

Emerging AI Architectures

1. “Mamba: Linear-Time Sequence Modeling with Selective State Spaces” (Gu et al., 2023)
2. “Towards Learning Universal Hyperparameter Optimizers with Transformers” (Metz et al., 2023)
3. “Scaling Vision Transformers to 22 Billion Parameters” (Dehghani et al., 2023)

Ethical AI and Bias Mitigation

1. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜” (Bender et al., 2021)
2. “Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence” (Mohamed et al., 2020)
3. “What Does It Mean to Align AI With Human Values?” (Gabriel, 2020)

LLM Interactions and Adversarial AI

1. “Red Teaming Language Models with Language Models” (Perez et al., 2022)
2. “Model Inversion Attacks Against GPT-2” (Carlini et al., 2023)
3. “Learning to Deceive Large Language Models” (Lin et al., 2023)
4. “Scalable Oversight of AI Systems via Selective Amplification” (Christiano et al., 2023)

Prompting and Retrieval

1. “How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval” (Wang et al., 2023)
2. “Unsupervised Dense Information Retrieval with Contrastive Learning” (Gao & Callan, 2021)
3. “Automatic Chain of Thought Prompting in Large Language Models” (Zhang et al., 2022)
4. “ReAct: Synergizing reasoning and acting in Language Models” (Yao et al., 2022)
5. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” (Lewis et al., 2020)

This list is regularly updated based on new publications and research interests of our members.

Find More Papers

• arXiv.org: A free distribution service and an open-access archive for scholarly articles.
• arXiv Sanity Lite: A web interface for browsing, searching, and visualizing arXiv papers.
• Google Scholar: A freely accessible web search engine that indexes the full text or metadata of scholarly literature.
• PubMed Central: A free full-text archive of biomedical and life sciences journal literature.
• Prompting Guide: A curated list of papers on prompting techniques and applications.

We encourage all members to explore these resources and suggest papers for discussion in our upcoming meetings.