Ethical Considerations and Challenges in the Deployment of Natural Language Processing Systems in Healthcare
Waseem Ahmed Khattak
Quaid-I-Azam University Islamabad, Pakistan, Department of Plant Sciences waseem007ustb@gmail.com
Fazle Rabbi
Australian Computer Society
https://orcid.org/0000-0002-5974-7905
Keywords: Access control, Natural Language Processing (NLP), Data privacy and security, Informed consent, Bias and fairness, Transparency and explainability
Abstract
This study examines the ethical considerations and challenges associated with the implementation of Natural Language Processing (NLP) systems in healthcare. The findings highlight key areas of concern and propose recommendations for responsible and ethical use of NLP applications. Data privacy and security emerge as crucial factors in NLP systems, given their reliance on sensitive patient data. Robust measures must be implemented to protect patient information from unauthorized access, breaches, or misuse. Compliance with relevant data protection regulations, such as HIPAA, is essential. Informed consent plays a pivotal role when utilizing NLP applications, as patient data may be processed without explicit consent. Clear guidelines and protocols are necessary to obtain informed consent, ensuring patients are well-informed about the potential benefits, risks, and implications of using NLP systems in their care. The study also highlights the presence of biases in NLP models, which can result in unfair or discriminatory outcomes. To address this, the development and deployment of NLP applications should include measures to identify and mitigate biases. Regular auditing, testing for bias, and diversifying training data can help mitigate these concerns. Transparency and explainability of NLP models are crucial for healthcare providers and patients to understand the underlying processes and ensure accountability. Efforts should be made to enhance the transparency and explainability of NLP models, enabling users to comprehend how conclusions or recommendations are generated. With the introduction of NLP applications, the issue of medical liability becomes pertinent. Establishing legal frameworks is necessary to determine accountability in cases of erroneous or harmful recommendations. Developers, healthcare providers, and users must share responsibility, and frameworks should be established accordingly. The study emphasizes the need for comprehensive ethical guidelines and regulations specific to NLP applications in healthcare. These guidelines should address data privacy, informed consent, bias mitigation, transparency, and accountability. Collaborative efforts among regulatory bodies, developers, healthcare providers, and ethicists are crucial in establishing appropriate standards. While NLP systems can automate healthcare processes, human oversight remains essential. Healthcare professionals should utilize NLP outputs as decision-making aids rather than relying solely on automated recommendations. A balance between automation and human expertise ensures responsible and accountable care provision.
