Promise and Challenges of Generative AI in Healthcare Information Systems

Author:

Ustymenko Stanislav1ORCID,Phadke Abhishek2ORCID

Affiliation:

1. Saint Leo University, Saint Leo, Florida, USA

2. Texas A&M University Corpus Christi, Corpus Christi, Texas, USA

Publisher

ACM

Reference34 articles.

1. Operationalising AI ethics through the agile software development lifecycle: a case study of AI-enabled mobile health applications

2. Kriti Bhattarai, Inez Y Oh, Jonathan M Sierra, Philip R Payne, Zachary B Abrams, and Albert M Lai. 2023. Leveraging GPT-4 for Identifying Clinical Phenotypes in Electronic Health Records: A Performance Comparison between GPT-4, GPT-3.5-turbo and spaCy's Rule-based & Machine Learning-based Methods. bioRxiv (2023), 2023--09.

3. Chia-Chun Chiang, Man Luo, Gina Dumkrieger, Shubham Trivedi, Yi-Chieh Chen, Chieh-Ju Chao, Todd J Schwedt, Abeed Sarker, and Imon Banerjee. 2023. A Large Language Model-Based Generative Natural Language Processing Framework Finetuned on Clinical Notes Accurately Extracts Headache Frequency from Electronic Health Records. medRxiv (2023).

4. Bharath Chintagunta, Namit Katariya, Xavier Amatriain, and Anitha Kannan. 2021. Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization. In Machine Learning for Healthcare Conference. PMLR, Virtual, 354--372.

5. Corti. [n.d.]. AI-Powered Patient Triaging. https://www.corti.ai/solutions/engage

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