Applications of Generative AI in Healthcare: algorithmic, ethical, legal and societal considerations
- URL: http://arxiv.org/abs/2406.10632v1
- Date: Sat, 15 Jun 2024 13:28:07 GMT
- Title: Applications of Generative AI in Healthcare: algorithmic, ethical, legal and societal considerations
- Authors: Onyekachukwu R. Okonji, Kamol Yunusov, Bonnie Gordon,
- Abstract summary: Generative AI is rapidly transforming medical imaging and text analysis.
This paper explores issues of accuracy, informed consent, data privacy, and algorithmic limitations.
We aim to foster a roadmap for ethical and responsible implementation of generative AI in healthcare.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generative AI is rapidly transforming medical imaging and text analysis, offering immense potential for enhanced diagnosis and personalized care. However, this transformative technology raises crucial ethical, societal, and legal questions. This paper delves into these complexities, examining issues of accuracy, informed consent, data privacy, and algorithmic limitations in the context of generative AI's application to medical imaging and text. We explore the legal landscape surrounding liability and accountability, emphasizing the need for robust regulatory frameworks. Furthermore, we dissect the algorithmic challenges, including data biases, model limitations, and workflow integration. By critically analyzing these challenges and proposing responsible solutions, we aim to foster a roadmap for ethical and responsible implementation of generative AI in healthcare, ensuring its transformative potential serves humanity with utmost care and precision.
Related papers
- Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
Mental illness is a widespread and debilitating condition with substantial societal and personal costs.
Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder.
Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
arXiv Detail & Related papers (2025-02-01T15:10:02Z) - Addressing Intersectionality, Explainability, and Ethics in AI-Driven Diagnostics: A Rebuttal and Call for Transdiciplinary Action [0.30693357740321775]
The increasing integration of artificial intelligence into medical diagnostics necessitates a critical examination of its ethical and practical implications.
This paper calls for a framework that balances accuracy with fairness, privacy, and inclusivity to ensure AI-driven diagnostics serve diverse populations equitably and ethically.
arXiv Detail & Related papers (2025-01-15T00:00:01Z) - Implications of Artificial Intelligence on Health Data Privacy and Confidentiality [0.0]
The rapid integration of artificial intelligence in healthcare is revolutionizing medical diagnostics, personalized medicine, and operational efficiency.
However, significant challenges arise concerning patient data privacy, ethical considerations, and regulatory compliance.
This paper examines the dual impact of AI on healthcare, highlighting its transformative potential and the critical need for safeguarding sensitive health information.
arXiv Detail & Related papers (2025-01-03T05:17:23Z) - Ethical Challenges and Evolving Strategies in the Integration of Artificial Intelligence into Clinical Practice [1.0301404234578682]
We focus on five critical ethical concerns: justice and fairness, transparency, patient consent and confidentiality, accountability, and patient-centered and equitable care.
The paper explores how bias, lack of transparency, and challenges in maintaining patient trust can undermine the effectiveness and fairness of AI applications in healthcare.
arXiv Detail & Related papers (2024-11-18T00:52:22Z) - AI-Driven Healthcare: A Survey on Ensuring Fairness and Mitigating Bias [2.398440840890111]
AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions.
These advancements also introduce substantial ethical and fairness challenges.
These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups.
arXiv Detail & Related papers (2024-07-29T02:39:17Z) - Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness [47.51360338851017]
Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence.
The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information.
Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task.
arXiv Detail & Related papers (2023-11-19T03:29:45Z) - FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare [73.78776682247187]
Concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI.
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
arXiv Detail & Related papers (2023-08-11T10:49:05Z) - A Conceptual Algorithm for Applying Ethical Principles of AI to Medical Practice [5.005928809654619]
AI-powered tools are increasingly matching or exceeding specialist-level performance across multiple domains.
These systems promise to reduce disparities in care delivery across demographic, racial, and socioeconomic boundaries.
The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care.
arXiv Detail & Related papers (2023-04-23T04:14:18Z) - The Role of AI in Drug Discovery: Challenges, Opportunities, and
Strategies [97.5153823429076]
The benefits, challenges and drawbacks of AI in this field are reviewed.
The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
arXiv Detail & Related papers (2022-12-08T23:23:39Z) - The Medkit-Learn(ing) Environment: Medical Decision Modelling through
Simulation [81.72197368690031]
We present a new benchmarking suite designed specifically for medical sequential decision making.
The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
arXiv Detail & Related papers (2021-06-08T10:38:09Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.