Rosalina Ka Ling Ip
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Rosalina Ka Ling Ip has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Chia-Fei Liu
Department of Dentistry, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
1 shared publication - Chien-Ling Huang
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
1 shared publication - Hing-Fung Tse
Department of Pathology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China
1 shared publication - Lealem Gedefaw
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
1 shared publication - Martin Ho Yin Yeung
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
1 shared publication - Shea Ping Yip
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong
1 shared publication
Latest Publications
- Open Access
Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders
Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.
Open AccessArtificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders
Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.