Grok and Medical Education

 

Mary Gerdt Parler:Alvarita1

@marygerdt

Please comment: A study found that two artificial intelligence programs—ChatGPT-3.5 and ChatGPT-4—scored better than humans on tests similar to neurology board examinations. However, both models consistently used confident language, even when providing incorrect answers, and the test questions didn't reflect the complexity of most neurology cases, experts said.

Grok

@grok

The study highlights the impressive capabilities of AI in the field of neurology, as the models outperformed human test takers in board-style exams. However, it also underscores the need for caution when relying on AI for medical diagnoses and treatment, as the models displayed overconfidence even when providing incorrect answers. Moreover, the study suggests that real-life clinical scenarios are more complex than the test questions, emphasizing the importance of human expertise in making accurate and nuanced assessments in neurology cases.

Mary Gerdt Parler:Alvarita1

@marygerdt

Can AI be used in ways to help access information and applicable clinical pathways while teaching Patient Clinical Assessment requiring Observation, Face to Face interviews.

Grok

@grok

Absolutely, AI can be a valuable tool in helping access information and applicable clinical pathways while teaching patient clinical assessment requiring observation and face-to-face interviews. Here are some ways AI can be used in this context:

1. AI-powered virtual patients: AI can simulate various patient scenarios and conditions, allowing students to practice and improve their clinical assessment skills in a safe, controlled environment. These virtual patients can provide real-time feedback, helping learners to identify their strengths and areas for improvement.
2. Natural Language Processing (NLP): AI can analyze and understand human language, enabling it to process and interpret patient interview data. By using NLP, AI can help identify key symptoms and concerns from patient interviews, providing valuable insights to guide further assessment and treatment.
3. Decision support systems: AI can assist in identifying relevant clinical pathways and evidence-based treatment options by analyzing patient data and medical literature. This can help ensure that patients receive the most appropriate and up-to-date care.
4. Intelligent tutoring: AI can act as a virtual tutor, providing personalized guidance and feedback to students as they practice patient assessment skills. This can help students learn at their own pace and focus on areas where they need the most improvement.
5. Predictive analytics: AI can analyze large volumes of patient data to identify patterns and trends that may be difficult for humans to detect. This can help inform clinical decision-making and improve patient outcomes by identifying potential health risks or complications early on.

By incorporating AI into the teaching of patient clinical assessment, we can enhance the learning experience, improve clinical decision-making, and ultimately provide better care for our patients.

Quote

Radiology: Artificial Intelligence

@Radiology_AI

·

Feb 8

"The #LLM Will See You Now": Performance of #ChatGPT on the Brazilian Radiology and Diagnostic Imaging and Mammography Board Examinations https://doi.org/10.1148/ryai.230568 @haritrivedimd @judywawira @emoryradiology #AI #ML #MachineLearning

Quote

JAMA

@JAMA_current

·

Feb 8

Marzyeh Ghassemi, PhD, the Herman L. F. von Helmholtz Career Development Professor at @MITEECS, examines ethical machine learning & responsible clinical implementation. Watch “AI and Clinical Practice” w/ @MarzyehGhassemi & @KbibbinsDomingo. #JAMAai @MIT https://ja.ma/3uw6IpG

Quote

Liam McCoy, MD MSc

@LiamGMcCoy

·

Feb 8

Replying to @morgancheatham

There are many steps between algorithmic performance and clinical impact that have to also be evaluated — how are clinicians trained to use it? How does it impact existing care pathways? How are errors handled? Not to mention the need for local data to properly validate!

Quote

Jose Donato Magno

@doc_jdam

·

Feb 9

ADAPT. IMBIBE. CASCADE. AI technology should not move us farther away from but rather closer to the patient. We need to muster enough courage to embrace the potential of AI to augment our clinical practice and ultimately improve patient care and outcomes. #AICardio #AIMedicine

Quote

Soroush Saghafian

@Soroush_Saghaf

·

Feb 12

My interview with @Euronews: “To ensure further suitability for implementation in clinical practice, one might need to develop #AI and #ML methods that can consider a combination of treatments and enable predicting counterfactual outcomes." https://ow.ly/ewRF50QAocz

Quote

Raffaele Di Giacomo, PhD

@sciqst

·

Feb 9

Replying to @AshuPJadhav

Enthralling topic, @AshuPJadhav! The evolution of outcome grading after brain injuries highlights both how far we've come and the challenges that still remain in neurology. It underlines the importance of precise, adaptive measures for patient recovery. For those deep-diving into…

Quote

Saritha Rai

@SarithaRai

·

Feb 8

Unlearn, an AI startup which boosts clinical research in neurology, immunology & cardiovascular trials by developing virtual copies of participants, has recieved $50M from Altimeter, Radical, Mubadala & others via @technology @business http://tinyurl.com/24be4ymu #AI

Quote

Vincent Rajkumar

@VincentRK

·

Feb 14

On ABIM MOC. Please read!!! To maintain the trust of patients we need to test the testers who test doctors @AaronGoodman33 @doctorwes @chadinabhan @DavidSteensma We must create a Board Exam to ensure that ABIM leaders are qualified Testers. Goal is to improve patient care. 1/

Mary Gerdt Parler:Alvarita1

@marygerdt

Thanks, Grok, Good Night

Grok

@grok

You're welcome, and sweet dreams! If you need anything else, don't hesitate to ask. Good night!