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Found 16 papers, 5 papers with code
Best Practices for Large Language Models in Radiology
At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights.
Almanac Copilot: Towards Autonomous Electronic Health Record Navigation
Clinicians spend large amounts of time on clinical documentation, and inefficiencies impact quality of care and increase clinician burnout.
Assessing The Potential Of Mid-Sized Language Models For Clinical QA
Large language models, such as GPT-4 and Med-PaLM, have shown impressive performance on clinical tasks; however, they require access to compute, are closed-source, and cannot be deployed on device.
BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text
Models such as GPT-4 and Med-PaLM 2 have demonstrated impressive performance on a wide variety of biomedical NLP tasks.
RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models
Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of interest within medical images is crucial but manually laborious and prone to interobserver and intraobserver biases.
Towards Reliable Dermatology Evaluation Benchmarks
Benchmark datasets for digital dermatology unwittingly contain inaccuracies that reduce trust in model performance estimates.
Large language models in medicine: the potentials and pitfalls
Large language models (LLMs) have been applied to tasks in healthcare, ranging from medical exam questions to responding to patient questions.
Augmenting medical image classifiers with synthetic data from latent diffusion models
While hundreds of artificial intelligence (AI) algorithms are now approved or cleared by the US Food and Drugs Administration (FDA), many studies have shown inconsistent generalization or latent bias, particularly for underrepresented populations.
SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained model debugging and analysis
To provide a medical dataset densely annotated by domain experts with annotations useful across multiple disease processes, we developed SkinCon: a skin disease dataset densely annotated by dermatologists.
Development and Clinical Evaluation of an AI Support Tool for Improving Telemedicine Photo Quality
To address this issue, we developed TrueImage 2. 0, an artificial intelligence (AI) model for assessing patient photo quality for telemedicine and providing real-time feedback to patients for photo quality improvement.
Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm
As a start towards increasing transparency, AI researchers have appropriated the use of the Fitzpatrick skin type (FST) from a measure of patient photosensitivity to a measure for estimating skin tone in algorithmic audits of computer vision applications including facial recognition and dermatology diagnosis.
Disparities in Dermatology AI Performance on a Diverse, Curated Clinical Image Set
To ascertain potential biases in algorithm performance in this context, we curated the Diverse Dermatology Images (DDI) dataset-the first publicly available, expertly curated, and pathologically confirmed image dataset with diverse skin tones.
Disparities in Dermatology AI: Assessments Using Diverse Clinical Images
AI diagnostic tools may aid in early skin cancer detection; however most models have not been assessed on images of diverse skin tones or uncommon diseases.
Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions
In decision support applications of AI, the AI algorithm's output is framed as a suggestion to a human user.
Towards Realization of Augmented Intelligence in Dermatology: Advances and Future Directions
Artificial intelligence (AI) algorithms using deep learning have advanced the classification of skin disease images; however these algorithms have been mostly applied "in silico" and not validated clinically.
TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos
These promising results suggest that our solution is feasible and can improve the quality of teledermatology care.
