Daniel S. W. Ting

Orcid: 0000-0003-2264-7174

Affiliations:
  • Duke-National University of Singapore (NUS) Medical School, Singapore


According to our database1, Daniel S. W. Ting authored at least 26 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Online presence:

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Bibliography

2024
Fine-tuning Large Language Model (LLM) Artificial Intelligence Chatbots in Ophthalmology and LLM-based evaluation using GPT-4.
CoRR, 2024

Development and Testing of a Novel Large Language Model-Based Clinical Decision Support Systems for Medication Safety in 12 Clinical Specialties.
CoRR, 2024

Development and Testing of Retrieval Augmented Generation in Large Language Models - A Case Study Report.
CoRR, 2024

Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias.
CoRR, 2024

2023
Federated and distributed learning applications for electronic health records and structured medical data: a scoping review.
J. Am. Medical Informatics Assoc., November, 2023

Big data in corneal diseases and cataract: Current applications and future directions.
Frontiers Big Data, January, 2023

A translational perspective towards clinical AI fairness.
npj Digit. Medicine, 2023

Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children.
npj Digit. Medicine, 2023

Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist.
CoRR, 2023

Towards clinical AI fairness: A translational perspective.
CoRR, 2023

2022
Shapley variable importance cloud for interpretable machine learning.
Patterns, 2022

Author Correction: Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection.
npj Digit. Medicine, 2022

A Novel Interpretable Machine Learning System to Generate Clinical Risk Scores: An Application for Predicting Early Mortality or Unplanned Readmission in A Retrospective Cohort Study.
Proceedings of the AMIA 2022, 2022

2021
Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis.
npj Digit. Medicine, 2021

Shapley variable importance clouds for interpretable machine learning.
CoRR, 2021

Partially-Supervised Learning for Vessel Segmentation in Ocular Images.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

Few-Shot Domain Adaptation with Polymorphic Transformers.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

2020
Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy.
npj Digit. Medicine, 2020

Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection.
npj Digit. Medicine, 2020

2019
Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi-ethnic study.
npj Digit. Medicine, 2019

Multi-discriminator Generative Adversarial Networks for Improved Thin Retinal Vessel Segmentation.
Proceedings of the Ophthalmic Medical Image Analysis - 6th International Workshop, 2019

Multi-Instance Multi-Scale CNN for Medical Image Classification.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

Feature Isolation for Hypothesis Testing in Retinal Imaging: An Ischemic Stroke Prediction Case Study.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Enhanced Detection of Referable Diabetic Retinopathy via DCNNs and Transfer Learning.
Proceedings of the Computer Vision - ACCV 2018 Workshops, 2018

Artificial Intelligence Using Deep Learning in Classifying Side of the Eyes and Width of Field for Retinal Fundus Photographs.
Proceedings of the Computer Vision - ACCV 2018 Workshops, 2018

Generative Adversarial Networks (GANs) for Retinal Fundus Image Synthesis.
Proceedings of the Computer Vision - ACCV 2018 Workshops, 2018


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