AI-MARRVEL (AIM) uses #MachineLearning to significantly improve diagnostic accuracy for genetic disorders, outperforming state-of-the-art methods.

Read the groundbreaking article by Dongxue Mao, Ph.D., et al.: nejm.ai/3WfeyQ5

#AI #PrecisionMedicine #HealthTech …

Datasets, Benchmarks, and Protocols: “Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying” by Ali Soroush, MD, MS et al.: nejm.ai/3w7JKGn

#ArtificialIntelligence

It was really great for me and Andrew Beam to chat with Adam Rodman and/or his digital simulacrum about LLMs, diagnostic reasoning, and the history of medicine for NEJM AI Grand Rounds. Episode coming soon!

AI-MARRVEL is an #ArtificialIntelligence system for genetic diagnosis that improves diagnostic accuracy, surpassing state-of-the-art benchmarked methods. Read the Original Article by Dongxue Mao, PhD, et al.: nejm.ai/3WfeyQ5

The performance of four histopathology slide search engines varied in reliability and accuracy across three patient cases. The authors of a Case Study recommend essential improvements to facilitate the clinical adoption of these engines. Read the study: nejm.ai/4aSjVcs

Could an AI second opinion service improve medical diagnosis and reduce medical errors **today**? We believe the answer is yes with proper oversight.

Adam Rodman and I reflect on the urgency in our new STAT essay:

statnews.com/2024/05/03/ai-…

Patients are taking AI into their own hands for diagnosis and treatment, while the medical establishment cautiously tests it.

This 'DIY' approach needs study and guidance, according to a new perspective in NEJM AI.

Read More Here: ai.nejm.org/doi/full/10.10…

#PatientPortal …

𝐖𝐡𝐚𝐭 𝐀𝐈 𝐢𝐧 𝐇𝐞𝐚𝐥𝐭𝐡 𝐂𝐚𝐫𝐞 𝐂𝐚𝐧 𝐋𝐞𝐚𝐫𝐧 𝐟𝐫𝐨𝐦 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐕𝐞𝐡𝐢𝐜𝐥𝐞𝐬

Thought-provoking analogy between healthcare AI and levels of automation for vehicles by Drs. Justin Norden and Nirav Shah in NEJM Catalyst

👉🏼catalyst.nejm.org/doi/full/10.10…

Recommendations for analysis of pathology image search algorithms and a road map for clinical adoption are provided in a new editorial by Drs. Richard J. Chen and Faisal Mahmood (@AI4Pathology): nejm.ai/4b7zhtl

Who is currently working to help patients develop best AI practices? carey goldberg addresses this question in a new Perspective: nejm.ai/3xKovuO

A multiyear effort indicates that AI-generated feedback and guidance to providers on five key elements of their clinical notes is having a positive impact at NYU Langone Health.

Dr. Daphne Koller outlines the challenges of using AI to identify new therapeutic hypotheses. She discusses “biology risk” and strategies insitro is deploying to increase the probability of success of drug discovery. Listen to the full episode: nejm.ai/ep17

New study reveals limitations of large language models (LLMs) in medical coding.

Key findings:
Tokenization algorithms lead to mismatches between billing codes & descriptions
GPT-4 had highest exact match rate, but only 45.9% for ICD-9-CM
LLMs often generate codes with…

Tokenization algorithms may be to blame when generative large language models inconsistently match medical billing codes to their preferred code descriptions. Learn more in a new Datasets, Benchmarks, and Protocols article by Ali Soroush, MD, MS et al.: nejm.ai/3w7JKGn

Perspective: Who’s Training Whom? by Jonathan H. Chen, M.D., Ph.D. (Jonathan H Chen MD PhD) nejm.ai/3Un9Oq4

Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying - NEJM AI

🔬 They evaluated GPT-3.5, GPT-4, Gemini Pro, and Llama2-70b Chat.
‼️ Conclusion: #LLMs are not appropriate for use in medical coding tasks

buff.ly/3UBBFDp

How to diagnose rare diseases more accurately?
Use #AI
As shown with a new tool ai.marrvel.org
ai.nejm.org/doi/full/10.10…
Dongxue Mao Zhandong Liu Chaozhong Liu
Linhua (Alex) Wang sasidhar pasupuleti Rami Al-Ouran and colleagues BCMHouston NEJM AI