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ChatGPT provides inconsistent risk-stratification of patients with atraumatic chest pain
ChatGPT-4 is a large language model with promising healthcare applications. However, its ability to analyze complex clinical data and provide consistent results is poorly known. Compared to validated tools, this study evaluated ChatGPT-4's risk stratification of simulated patients with acute nontraumatic chest pain.Three datasets of simulated case studies were created: one based on the TIMI score variables, another on HEART score variables, and a third comprising 44 randomized variables related to non-traumatic chest pain presentations. ChatGPT-4 independently scored each dataset five times. Its risk scores were compared to calculated TIMI and HEART scores. A model trained on 44 clinical variables was evaluated for consistency.ChatGPT-4 showed a high correlation with TIMI and HEART scores (r = 0.898 and 0.928, respectively), but the distribution of individual risk assessments was broad. ChatGPT-4 gave a different risk 45-48% of the time for a fixed TIMI or HEART score. On the 44-variable model, a majority of the five ChatGPT-4 models agreed on a diagnosis category only 56% of the time, and risk scores were poorly correlated (r = 0.605).While ChatGPT-4 correlates closely with established risk stratification tools regarding mean scores, its inconsistency when presented with identical patient data on separate occasions raises concerns about its reliability. The findings suggest that while large language models like ChatGPT-4 hold promise for healthcare applications, further refinement and customization are necessary, particularly in the clinical risk assessment of atraumatic chest pain patients
Label-driven weakly-supervised learning for multimodal deformable image registration
Spatially aligning medical images from different modalities remains a
challenging task, especially for intraoperative applications that require fast
and robust algorithms. We propose a weakly-supervised, label-driven formulation
for learning 3D voxel correspondence from higher-level label correspondence,
thereby bypassing classical intensity-based image similarity measures. During
training, a convolutional neural network is optimised by outputting a dense
displacement field (DDF) that warps a set of available anatomical labels from
the moving image to match their corresponding counterparts in the fixed image.
These label pairs, including solid organs, ducts, vessels, point landmarks and
other ad hoc structures, are only required at training time and can be
spatially aligned by minimising a cross-entropy function of the warped moving
label and the fixed label. During inference, the trained network takes a new
image pair to predict an optimal DDF, resulting in a fully-automatic,
label-free, real-time and deformable registration. For interventional
applications where large global transformation prevails, we also propose a
neural network architecture to jointly optimise the global- and local
displacements. Experiment results are presented based on cross-validating
registrations of 111 pairs of T2-weighted magnetic resonance images and 3D
transrectal ultrasound images from prostate cancer patients with a total of
over 4000 anatomical labels, yielding a median target registration error of 4.2
mm on landmark centroids and a median Dice of 0.88 on prostate glands.Comment: Accepted to ISBI 201
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