Medical artificial intelligence ethics: A systematic review of empirical studies
Background Artificial intelligence (AI) technologies are transforming medicine and
healthcare. Scholars and practitioners have debated the philosophical, ethical, legal, and …
healthcare. Scholars and practitioners have debated the philosophical, ethical, legal, and …
[HTML][HTML] Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma
J Homolak - Croatian Medical Journal, 2023 - ncbi.nlm.nih.gov
The release of ChatGPT, the latest large (175-billion-parameter) language model by San
Francisco-based company OpenAI, prompted many to think about the exciting (and …
Francisco-based company OpenAI, prompted many to think about the exciting (and …
Artificial intelligence bias in medical system designs: A systematic review
Inherent bias in the artificial intelligence (AI)-model brings inaccuracies and variabilities
during clinical deployment of the model. It is challenging to recognize the source of bias in AI …
during clinical deployment of the model. It is challenging to recognize the source of bias in AI …
Detecting shortcut learning for fair medical AI using shortcut testing
Abstract Machine learning (ML) holds great promise for improving healthcare, but it is critical
to ensure that its use will not propagate or amplify health disparities. An important step is to …
to ensure that its use will not propagate or amplify health disparities. An important step is to …
Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation
Background Comorbidity, frailty, and decreased cognitive function lead to a higher risk of
death in elderly patients (more than 65 years of age) during acute medical events. Early and …
death in elderly patients (more than 65 years of age) during acute medical events. Early and …
A framework to identify ethical concerns with ML-guided care workflows: a case study of mortality prediction to guide advance care planning
Objective Identifying ethical concerns with ML applications to healthcare (ML-HCA) before
problems arise is now a stated goal of ML design oversight groups and regulatory agencies …
problems arise is now a stated goal of ML design oversight groups and regulatory agencies …
Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study
Summary Background Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2
Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal …
Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal …
[PDF][PDF] Black box or open science? A study on reproducibility in AI development papers
F Königstorfer, A Haberl, D Kowald… - Proceedings of The …, 2024 - domkowald.github.io
Abstract The surge in Artificial Intelligence (AI) research has spurred significant
breakthroughs across various fields. However, AI is known for its Black Box character and …
breakthroughs across various fields. However, AI is known for its Black Box character and …
Longitudinal assessment of demographic representativeness in the Medical Imaging and Data Resource Center open data commons
Purpose The Medical Imaging and Data Resource Center (MIDRC) open data commons
was launched to accelerate the development of artificial intelligence (AI) algorithms to help …
was launched to accelerate the development of artificial intelligence (AI) algorithms to help …
[HTML][HTML] Challenges in and Opportunities for Electronic Health Record-Based Data Analysis and Interpretation
MK Kim, C Rouphael, J McMichael, N Welch… - Gut and …, 2024 - ncbi.nlm.nih.gov
Electronic health records (EHRs) have been increasingly adopted in clinical practices
across the United States, providing a primary source of data for clinical research, particularly …
across the United States, providing a primary source of data for clinical research, particularly …