Global PIQA: Evaluating Physical Commonsense Reasoning Across 100+ Languages and Cultures
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2510.24081
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AI/ML, NLP, CV, IoT, Generative AI
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Kameshr
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19 days ago
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#4 opened about 2 months ago
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#4 opened about 2 months ago
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#2 opened about 2 months ago
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#1 opened 2 months ago
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abinayam
published
a
model
2 months ago
Sri-Vigneshwar-DJ
posted
an
update
2 months ago
Post
327
Do you think domain-specific embedding fine-tuners are needed?
I've been working with embeddings for marketing use cases and noticed something: most embeddings don't get marketing concepts very well. They're trained in general-purpose ways.
The Issue I'm Seeing
When I search marketing content with general embeddings:
"organic growth" returns farming articles
"conversion funnel" matches industrial equipment
"brand lift" doesn't connect to campaign effectiveness
Marketing jargon like CAC, ROAS, CTR aren't properly understood
My Question
Do you think domain-specific embeddings are needed for marketing?
Some thoughts:
Marketing has its own vocabulary and concept relationships
General models trained on Wikipedia/web crawl miss these nuances
But is fine-tuning worth the effort vs just using more retrieval tricks?
Quick Example
I fine-tuned all-mpnet-base-v2 on ~1000 marketing concept pairs and saw 15-20% better retrieval accuracy. But I'm curious:
Has anyone else tried this for marketing or other domains?
When do you think domain-specific embeddings are actually necessary vs overkill?
Are there better approaches I'm missing?
https://huggingface.co/blog/Sri-Vigneshwar-DJ/why-your-marketing-rag-system-needs-domain-specifi
I've been working with embeddings for marketing use cases and noticed something: most embeddings don't get marketing concepts very well. They're trained in general-purpose ways.
The Issue I'm Seeing
When I search marketing content with general embeddings:
"organic growth" returns farming articles
"conversion funnel" matches industrial equipment
"brand lift" doesn't connect to campaign effectiveness
Marketing jargon like CAC, ROAS, CTR aren't properly understood
My Question
Do you think domain-specific embeddings are needed for marketing?
Some thoughts:
Marketing has its own vocabulary and concept relationships
General models trained on Wikipedia/web crawl miss these nuances
But is fine-tuning worth the effort vs just using more retrieval tricks?
Quick Example
I fine-tuned all-mpnet-base-v2 on ~1000 marketing concept pairs and saw 15-20% better retrieval accuracy. But I'm curious:
Has anyone else tried this for marketing or other domains?
When do you think domain-specific embeddings are actually necessary vs overkill?
Are there better approaches I'm missing?
https://huggingface.co/blog/Sri-Vigneshwar-DJ/why-your-marketing-rag-system-needs-domain-specifi
-
- 6 replies
Sri-Vigneshwar-DJ
posted
an
update
2 months ago
Post
4427
🚀 Exciting News! We've released a Performance Marketing Expert Dataset from Hawky.ai [www.hawky.ai]
Hawky-ai
This dataset empowers AI models with cutting-edge strategies for Meta, Google Ads, and TikTok campaigns. It includes:
1. Multi-platform strategies for e-commerce, DTC, B2B, and more
2. Creative optimization and audience targeting insights
3. ROI-driven recommendations based on 2025 best practices
Sri-Vigneshwar-DJ/Performance-Marketing-Data
Hawky-ai
This dataset empowers AI models with cutting-edge strategies for Meta, Google Ads, and TikTok campaigns. It includes:
1. Multi-platform strategies for e-commerce, DTC, B2B, and more
2. Creative optimization and audience targeting insights
3. ROI-driven recommendations based on 2025 best practices
Sri-Vigneshwar-DJ/Performance-Marketing-Data
Sri-Vigneshwar-DJ
posted
an
update
2 months ago
Post
3331
🚀 Qwen3-Omni for Marketing: A Game-Changer
Just wanted to share something exciting I've been exploring—Qwen3-Omni and how it's transforming marketing workflows.
What makes it special? At Hawky.ai we are started experimenting with Qwen3 recently for Analysis and Optimization.
Unlike traditional tools that look at text, images, or audio separately, Qwen3-Omni analyzes everything together. It handles 119 languages, processes 40-minute audio sequences, and understands both images and videos—all at once.
The cool part? It's 2-3x faster than similar models thanks to its MoE architecture.
Real applications I'm seeing:
Ad Analysis: It scores video ads by combining visual elements, audio tone, and text—giving 25% better CTR predictions than single-mode tools.
Campaign Localization: Drop in one ad, get 10 localized versions with native voiceovers in under a minute. Perfect for testing across markets.
Market Research: Feed it competitor content, podcasts, or UGC videos. It extracts actionable insights like "3-second hooks boost retention by 15%" and saves about 70% of analysis time.
Quality Checks: Automatically catches lip-sync errors and audio-visual mismatches.
Full technical breakdown: https://huggingface.co/blog/Sri-Vigneshwar-DJ/hawky-aiqwen3-omni-advanced-architecture-and-marke
Has anyone else been experimenting with multimodal models for marketing? Would love to hear what you're building!
#MultimodalAI #MarTech #OpenSource
Just wanted to share something exciting I've been exploring—Qwen3-Omni and how it's transforming marketing workflows.
What makes it special? At Hawky.ai we are started experimenting with Qwen3 recently for Analysis and Optimization.
Unlike traditional tools that look at text, images, or audio separately, Qwen3-Omni analyzes everything together. It handles 119 languages, processes 40-minute audio sequences, and understands both images and videos—all at once.
The cool part? It's 2-3x faster than similar models thanks to its MoE architecture.
Real applications I'm seeing:
Ad Analysis: It scores video ads by combining visual elements, audio tone, and text—giving 25% better CTR predictions than single-mode tools.
Campaign Localization: Drop in one ad, get 10 localized versions with native voiceovers in under a minute. Perfect for testing across markets.
Market Research: Feed it competitor content, podcasts, or UGC videos. It extracts actionable insights like "3-second hooks boost retention by 15%" and saves about 70% of analysis time.
Quality Checks: Automatically catches lip-sync errors and audio-visual mismatches.
Full technical breakdown: https://huggingface.co/blog/Sri-Vigneshwar-DJ/hawky-aiqwen3-omni-advanced-architecture-and-marke
Has anyone else been experimenting with multimodal models for marketing? Would love to hear what you're building!
#MultimodalAI #MarTech #OpenSource
Sri-Vigneshwar-DJ
posted
an
update
11 months ago
Post
882
Checkout phi-4 from Microsoft, dropped a day ago... If you ❤️ the Phi series, then here is the GGUF -
Sri-Vigneshwar-DJ/phi-4-GGUF. phi-4 is a 14B highly efficient open LLM that beats much larger models at math and reasoning - check out evaluations on the Open LLM.
Technical paper - https://arxiv.org/pdf/2412.08905 ; The Data Synthesis approach is interesting
Technical paper - https://arxiv.org/pdf/2412.08905 ; The Data Synthesis approach is interesting
Sri-Vigneshwar-DJ
posted
an
update
11 months ago
Post
2116
Just sharing a thought: I started using DeepSeek V3 a lot, and an idea struck me about agents "orchestrating during inference" on a test-time compute model like DeepSeek V3 or the O1 series.
Agents (Instruction + Function Calls + Memory) execute during inference, and based on the output decision, a decision is made to scale the time to reason or perform other tasks.
Agents (Instruction + Function Calls + Memory) execute during inference, and based on the output decision, a decision is made to scale the time to reason or perform other tasks.
Sri-Vigneshwar-DJ
posted
an
update
11 months ago
Post
2374
Combining smolagents with Anthropic’s best practices simplifies building powerful AI agents:
1. Code-Based Agents: Write actions as Python code, reducing steps by 30%.
2. Prompt Chaining: Break tasks into sequential subtasks with validation gates.
3. Routing: Classify inputs and direct them to specialized handlers.
4. Fallback: Handle tasks even if classification fails.
https://huggingface.co/blog/Sri-Vigneshwar-DJ/building-effective-agents-with-anthropics-best-pra
1. Code-Based Agents: Write actions as Python code, reducing steps by 30%.
2. Prompt Chaining: Break tasks into sequential subtasks with validation gates.
3. Routing: Classify inputs and direct them to specialized handlers.
4. Fallback: Handle tasks even if classification fails.
https://huggingface.co/blog/Sri-Vigneshwar-DJ/building-effective-agents-with-anthropics-best-pra
Post
5270
Last Week in Medical AI: Top Research Papers/Models 🔥
🏅 (December 15 – December 21, 2024)
Medical LLM & Other Models
- MedMax: Mixed-Modal Biomedical Assistant
- Advanced multimodal instruction tuning
- Enhanced biomedical knowledge integration
- Comprehensive assistant capabilities
- MGH Radiology Llama 70B
- Specialized radiology focus
- State-of-the-art performance
- Enhanced report generation capabilities
- HC-LLM: Historical Radiology Reports
- Context-aware report generation
- Historical data integration
- Improved accuracy in diagnostics
Frameworks & Methods
- ReflecTool: Reflection-Aware Clinical Agents
- Process-Supervised Clinical Notes
- Federated Learning with RAG
- Query Pipeline Optimization
Benchmarks & Evaluations
- Multi-OphthaLingua
- Multilingual ophthalmology benchmark
- Focus on LMICs healthcare
- Bias assessment framework
- ACE-M3 Evaluation Framework
- Multimodal medical model testing
- Comprehensive capability assessment
- Standardized evaluation metrics
LLM Applications
- Patient-Friendly Video Reports
- Medical Video QA Systems
- Gene Ontology Annotation
- Healthcare Recommendations
Special Focus: Medical Ethics & AI
- Clinical Trust Impact Study
- Mental Health AI Challenges
- Hospital Monitoring Ethics
- Radiology AI Integration
Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well!
- Full thread in detail:
https://x.com/OpenlifesciAI/status/1870504774162063760
- Youtube Link: youtu.be/SbFp4fnuxjo
- Spotify: https://t.co/QPmdrXuWP9
🏅 (December 15 – December 21, 2024)
Medical LLM & Other Models
- MedMax: Mixed-Modal Biomedical Assistant
- Advanced multimodal instruction tuning
- Enhanced biomedical knowledge integration
- Comprehensive assistant capabilities
- MGH Radiology Llama 70B
- Specialized radiology focus
- State-of-the-art performance
- Enhanced report generation capabilities
- HC-LLM: Historical Radiology Reports
- Context-aware report generation
- Historical data integration
- Improved accuracy in diagnostics
Frameworks & Methods
- ReflecTool: Reflection-Aware Clinical Agents
- Process-Supervised Clinical Notes
- Federated Learning with RAG
- Query Pipeline Optimization
Benchmarks & Evaluations
- Multi-OphthaLingua
- Multilingual ophthalmology benchmark
- Focus on LMICs healthcare
- Bias assessment framework
- ACE-M3 Evaluation Framework
- Multimodal medical model testing
- Comprehensive capability assessment
- Standardized evaluation metrics
LLM Applications
- Patient-Friendly Video Reports
- Medical Video QA Systems
- Gene Ontology Annotation
- Healthcare Recommendations
Special Focus: Medical Ethics & AI
- Clinical Trust Impact Study
- Mental Health AI Challenges
- Hospital Monitoring Ethics
- Radiology AI Integration
Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well!
- Full thread in detail:
https://x.com/OpenlifesciAI/status/1870504774162063760
- Youtube Link: youtu.be/SbFp4fnuxjo
- Spotify: https://t.co/QPmdrXuWP9
- 1 reply
Post
3504
Last Week in Medical AI: Top Research Papers/Models 🔥
🏅 (December 7 – December 14, 2024)
Medical LLM & Other Models
- PediaBench: Chinese Pediatric LLM
- Comprehensive pediatric dataset
- Advanced benchmarking platform
- Chinese healthcare innovation
- BiMediX: Bilingual Medical LLM
- Multilingual medical expertise
- Diverse medical knowledge integration
- Cross-cultural healthcare insights
- MMedPO: Vision-Language Medical LLM
- Clinical multimodal optimization
- Advanced medical image understanding
- Precision healthcare modeling
Frameworks and Methodologies
- TOP-Training: Medical Q&A Framework
- Hybrid RAG: Secure Medical Data Management
- Zero-Shot ATC Clinical Coding
- Chest X-Ray Diagnosis Architecture
- Medical Imaging AI Democratization
Benchmarks & Evaluations
- KorMedMCQA: Korean Healthcare Licensing Benchmark
- Large Language Model Medical Tasks
- Clinical T5 Model Performance Study
- Radiology Report Quality Assessment
- Genomic Analysis Benchmarking
Medical LLM Applications
- BRAD: Digital Biology Language Model
- TCM-FTP: Herbal Prescription Prediction
- LLaSA: Activity Analysis via Sensors
- Emergency Department Visit Predictions
- Neurodegenerative Disease AI Diagnosis
- Kidney Disease Explainable AI Model
Ethical AI & Privacy
- Privacy-Preserving LLM Mechanisms
- AI-Driven Digital Organism Modeling
- Biomedical Research Automation
- Multimodality in Medical Practice
Full thread in detail: https://x.com/OpenlifesciAI/status/1867999825721242101
🏅 (December 7 – December 14, 2024)
Medical LLM & Other Models
- PediaBench: Chinese Pediatric LLM
- Comprehensive pediatric dataset
- Advanced benchmarking platform
- Chinese healthcare innovation
- BiMediX: Bilingual Medical LLM
- Multilingual medical expertise
- Diverse medical knowledge integration
- Cross-cultural healthcare insights
- MMedPO: Vision-Language Medical LLM
- Clinical multimodal optimization
- Advanced medical image understanding
- Precision healthcare modeling
Frameworks and Methodologies
- TOP-Training: Medical Q&A Framework
- Hybrid RAG: Secure Medical Data Management
- Zero-Shot ATC Clinical Coding
- Chest X-Ray Diagnosis Architecture
- Medical Imaging AI Democratization
Benchmarks & Evaluations
- KorMedMCQA: Korean Healthcare Licensing Benchmark
- Large Language Model Medical Tasks
- Clinical T5 Model Performance Study
- Radiology Report Quality Assessment
- Genomic Analysis Benchmarking
Medical LLM Applications
- BRAD: Digital Biology Language Model
- TCM-FTP: Herbal Prescription Prediction
- LLaSA: Activity Analysis via Sensors
- Emergency Department Visit Predictions
- Neurodegenerative Disease AI Diagnosis
- Kidney Disease Explainable AI Model
Ethical AI & Privacy
- Privacy-Preserving LLM Mechanisms
- AI-Driven Digital Organism Modeling
- Biomedical Research Automation
- Multimodality in Medical Practice
Full thread in detail: https://x.com/OpenlifesciAI/status/1867999825721242101
- 4 replies
Kameshr
authored
a
paper
12 months ago
Post
2083
Last Week in Medical AI: Top Research Papers/Models 🔥
🏅 (December 2 – December 7, 2024)
Medical LLM & Models
- Block MedCare: Blockchain AI & IoT
- LLMs4Life: Biomedical Ontology Learning
- LLaMA II for Multimodal Diagnosis
- Compact LLM for EHR Privacy
Frameworks & Methods
- RARE: Retrieval-Augmented Reasoning
- STORM: Strategies for Rare Events
- TransFair: Fair Disease Classification
- PePR: Performance Per Resource
- Medical LLM Best Practices
LLM Applications
- Medchain: LLMs in Clinical Practice
- Query Nursing Note Summarization
- CLINICSUM: Patient Conversation Summaries
- Text Embeddings for Classifiers
LLM Benchmarks
- Polish Medical Exams Transfer
- Single-Cell Omics Annotation
- LLMs in Precision Medicine
- Low-Resource Healthcare Challenges
Other Models
- LLM Chatbot Hallucinations
- Multi-stage Chest X-ray Diagnosis
- EchoONE: Echocardiography AI
- Radiology Report Grounding
Ethics & Fairness
- Privacy in Medical Imaging
- Demographic Fairness in AI
Datasets
- LLM Scientific Knowledge Extraction
- Biomedical Knowledge Review
Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well!
- Full thread in detail: https://x.com/OpenlifesciAI/status/1865584829057929303
- Youtube Link: https://youtu.be/SwawtIFy-BI
- Spotify: https://open.spotify.com/episode/17Cxk0NLfKhiRWPoykzjem
🏅 (December 2 – December 7, 2024)
Medical LLM & Models
- Block MedCare: Blockchain AI & IoT
- LLMs4Life: Biomedical Ontology Learning
- LLaMA II for Multimodal Diagnosis
- Compact LLM for EHR Privacy
Frameworks & Methods
- RARE: Retrieval-Augmented Reasoning
- STORM: Strategies for Rare Events
- TransFair: Fair Disease Classification
- PePR: Performance Per Resource
- Medical LLM Best Practices
LLM Applications
- Medchain: LLMs in Clinical Practice
- Query Nursing Note Summarization
- CLINICSUM: Patient Conversation Summaries
- Text Embeddings for Classifiers
LLM Benchmarks
- Polish Medical Exams Transfer
- Single-Cell Omics Annotation
- LLMs in Precision Medicine
- Low-Resource Healthcare Challenges
Other Models
- LLM Chatbot Hallucinations
- Multi-stage Chest X-ray Diagnosis
- EchoONE: Echocardiography AI
- Radiology Report Grounding
Ethics & Fairness
- Privacy in Medical Imaging
- Demographic Fairness in AI
Datasets
- LLM Scientific Knowledge Extraction
- Biomedical Knowledge Review
Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well!
- Full thread in detail: https://x.com/OpenlifesciAI/status/1865584829057929303
- Youtube Link: https://youtu.be/SwawtIFy-BI
- Spotify: https://open.spotify.com/episode/17Cxk0NLfKhiRWPoykzjem
Post
3389
Last Week in Medical AI: Top Research Papers/Models 🔥
(November 2 -November 9, 2024)
🏅 Medical AI Paper of the Week:
Exploring Large Language Models for Specialist-level Oncology Care
Medical LLM & Other Models:
- GSCo: Generalist-Specialist AI Collaboration
- PediatricsGPT: Chinese Pediatric Assistant
- MEG: Knowledge-Enhanced Medical QA
- AutoProteinEngine: Multimodal Protein LLM
Frameworks and Methodologies:
- BrainSegFounder: 3D Neuroimage Analysis
- PASSION: Sub-Saharan Dermatology Dataset
- SAM for Lung X-ray Segmentation
- Label Critic: Data-First Approach
- Medprompt Runtime Strategies
Medical LLM Applications:
- CataractBot: Patient Support System
- CheX-GPT: X-ray Report Enhancement
- CardioAI: Cancer Cardiotoxicity Monitor
- HealthQ: Healthcare Conversation Chain
- PRObot: Diabetic Retinopathy Assistant
Medical LLMs & Benchmarks:
- MediQ: Clinical Reasoning Benchmark
- Touchstone: Segmentation Evaluation
- Medical LLM Adaptation Progress
- Fine-Tuning Medical QA Strategies
AI in Healthcare Ethics:
- Healthcare Robotics with LLMs
- XAI in Clinical Practice
- Precision Rehabilitation Framework
- Multimodal AI Challenges
Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well!
- Full Thread: https://x.com/OpenlifesciAI/status/1855207141302473090
- YouTube: https://youtu.be/ad0uTnYuTo8
- Spotify: https://open.spotify.com/episode/6s39t1UJZk1i10szuXP2qN
(November 2 -November 9, 2024)
🏅 Medical AI Paper of the Week:
Exploring Large Language Models for Specialist-level Oncology Care
Medical LLM & Other Models:
- GSCo: Generalist-Specialist AI Collaboration
- PediatricsGPT: Chinese Pediatric Assistant
- MEG: Knowledge-Enhanced Medical QA
- AutoProteinEngine: Multimodal Protein LLM
Frameworks and Methodologies:
- BrainSegFounder: 3D Neuroimage Analysis
- PASSION: Sub-Saharan Dermatology Dataset
- SAM for Lung X-ray Segmentation
- Label Critic: Data-First Approach
- Medprompt Runtime Strategies
Medical LLM Applications:
- CataractBot: Patient Support System
- CheX-GPT: X-ray Report Enhancement
- CardioAI: Cancer Cardiotoxicity Monitor
- HealthQ: Healthcare Conversation Chain
- PRObot: Diabetic Retinopathy Assistant
Medical LLMs & Benchmarks:
- MediQ: Clinical Reasoning Benchmark
- Touchstone: Segmentation Evaluation
- Medical LLM Adaptation Progress
- Fine-Tuning Medical QA Strategies
AI in Healthcare Ethics:
- Healthcare Robotics with LLMs
- XAI in Clinical Practice
- Precision Rehabilitation Framework
- Multimodal AI Challenges
Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well!
- Full Thread: https://x.com/OpenlifesciAI/status/1855207141302473090
- YouTube: https://youtu.be/ad0uTnYuTo8
- Spotify: https://open.spotify.com/episode/6s39t1UJZk1i10szuXP2qN
Post
2843
Last Week in Medical AI: Top Research Papers/Models 🔥
🏅 (October 26 - November 2, 2024)
🏅 Medical AI Paper of the Week:
Google Presents MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making
Medical LLM & Other Models:
- Matchmaker: Schema Matching with LLMs
- UltraMedical: Specialized Biomedical Models
- ZALM3: Vision-Language Medical Dialogue
- EchoFM: Echocardiogram Foundation Model
Frameworks and Methodologies:
- FEDKIM: Federated Medical Knowledge Injection
- Flex-MoE: Flexible Modality Combination
- MAISI: Synthetic Medical Imaging
- Cough-E: Edge Privacy Detection
- MassSpecGym: Molecule Identification
Medical LLM Applications:
- DiaMond: Multi-Modal Dementia Diagnosis
- LLM-Forest: Health Data Imputation
- Medical Multimodal Visual Grounding
- Clinical Evidence Synthesis with LLMs
Medical LLMs & Benchmarks:
- Histopathology Models Beyond H&E
- LLMs in Mental Health Counseling
- Medical Dataset Reuse Analysis
AI in Healthcare Ethics:
- LLMs in Medical Education
- Medical Exam Question Generation
- Clinical Knowledge Graph Integration
Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well!
- Full Thread: https://x.com/OpenlifesciAI/status/1852685220912464066
- YouTube: https://youtu.be/3O3xjaMCXHI
- Spotify: https://open.spotify.com/episode/05trbTbtVZcfI7ycA5Z3Tt?si=706b74626f714aa1
🏅 (October 26 - November 2, 2024)
🏅 Medical AI Paper of the Week:
Google Presents MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making
Medical LLM & Other Models:
- Matchmaker: Schema Matching with LLMs
- UltraMedical: Specialized Biomedical Models
- ZALM3: Vision-Language Medical Dialogue
- EchoFM: Echocardiogram Foundation Model
Frameworks and Methodologies:
- FEDKIM: Federated Medical Knowledge Injection
- Flex-MoE: Flexible Modality Combination
- MAISI: Synthetic Medical Imaging
- Cough-E: Edge Privacy Detection
- MassSpecGym: Molecule Identification
Medical LLM Applications:
- DiaMond: Multi-Modal Dementia Diagnosis
- LLM-Forest: Health Data Imputation
- Medical Multimodal Visual Grounding
- Clinical Evidence Synthesis with LLMs
Medical LLMs & Benchmarks:
- Histopathology Models Beyond H&E
- LLMs in Mental Health Counseling
- Medical Dataset Reuse Analysis
AI in Healthcare Ethics:
- LLMs in Medical Education
- Medical Exam Question Generation
- Clinical Knowledge Graph Integration
Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well!
- Full Thread: https://x.com/OpenlifesciAI/status/1852685220912464066
- YouTube: https://youtu.be/3O3xjaMCXHI
- Spotify: https://open.spotify.com/episode/05trbTbtVZcfI7ycA5Z3Tt?si=706b74626f714aa1