Raw LLM Responses

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I think that opinion on research uses is interesting but practically, it will require much greater oversight. One issue I recall with MRI volumetric measurements was that the program incorrectly identified the region of interest in different patient (studies). This was some time ago and I can't remember the specifics but it related to including anatomically non-neural/brain tissue in these calculations. I imagine that AI has improved this feature but I suspect that you will need highly skilled clinicians/MDs who are also skilled in computer science/mathematics with postgraduate degrees to be able to truly move forward here. Another example is with actual interpretation of results where there are outlier results, particularly in research scenarios for the imaging protocols themselves (eg. in Alzheimer's and Parkinson's disease molecular/PET-CT fusion imaging). The output image itself is dependent on many factors that can distort the reference region (input data). This adds even another layer of complexity and raises questions about re/solving the primary information and bias created by confirming an erroneous result. Does AI have the intuitive ability to question the dataset and relate this to the patient from which the signal was generated and recognise the noise? No doubt that AI has a role to play and it clearly seems like another consequential moment in our history (not just medicine).
youtube AI Jobs 2023-04-26T13:3… ♥ 1
Coding Result
DimensionValue
Responsibilitycompany
Reasoningdeontological
Policyregulate
Emotionfear
Coded at2026-04-27T06:24:53.388235
Raw LLM Response
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