Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization

• URL: http://arxiv.org/abs/2105.00060v1
• Date: Fri, 30 Apr 2021 19:46:31 GMT
• Title: Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization
• Authors: Michael Anis Mihdi Afnan, Cynthia Rudin, Vincent Conitzer, Julian Savulescu, Abhishek Mishra, Yanhe Liu, Masoud Afnan
• Abstract summary: We give an introduction to IVF and review the use of AI for embryo selection. We discuss concerns with the interpretation of the reported results from scientific and practical perspectives. We advocate strongly for the use of interpretable models.
• Score: 41.52637932108825
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: AI has the potential to revolutionize many areas of healthcare. Radiology, dermatology, and ophthalmology are some of the areas most likely to be impacted in the near future, and they have received significant attention from the broader research community. But AI techniques are now also starting to be used in in vitro fertilization (IVF), in particular for selecting which embryos to transfer to the woman. The contribution of AI to IVF is potentially significant, but must be done carefully and transparently, as the ethical issues are significant, in part because this field involves creating new people. We first give a brief introduction to IVF and review the use of AI for embryo selection. We discuss concerns with the interpretation of the reported results from scientific and practical perspectives. We then consider the broader ethical issues involved. We discuss in detail the problems that result from the use of black-box methods in this context and advocate strongly for the use of interpretable models. Importantly, there have been no published trials of clinical effectiveness, a problem in both the AI and IVF communities, and we therefore argue that clinical implementation at this point would be premature. Finally, we discuss ways for the broader AI community to become involved to ensure scientifically sound and ethically responsible development of AI in IVF.

Related papers

• Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learning [0.9866581731906279]
  This study offers a comprehensive overview of the limitations and opportunities of AI in pediatric echocardiography. It emphasizes the synergistic workflow and role of XAI and FL, identifying research gaps, and exploring potential future developments. Three relevant clinical use cases demonstrate the functionality of XAI and FL with a focus on (i) view recognition, (ii) disease classification, (iii) segmentation of cardiac structures, and (iv) quantitative assessment of cardiac function.
  arXiv  Detail & Related papers  (2024-11-15T15:03:34Z)
• Multimodal Learning for Embryo Viability Prediction in Clinical IVF [24.257300904706902]
  In clinical In-Vitro Fertilization (IVF), identifying the most viable embryo for transfer is important to increasing the likelihood of a successful pregnancy. Traditionally, this process involves embryologists manually assessing embryos' static morphological features at specific intervals using light microscopy. This manual evaluation is not only time-intensive and costly, due to the need for expert analysis, but also inherently subjective, leading to variability in the selection process.
  arXiv  Detail & Related papers  (2024-10-21T01:58:26Z)
• Towards Clinical AI Fairness: Filling Gaps in the Puzzle [15.543248260582217]
  This review systematically pinpoints several deficiencies concerning both healthcare data and the provided AI fairness solutions. We highlight the scarcity of research on AI fairness in many medical domains where AI technology is increasingly utilized. To bridge these gaps, our review advances actionable strategies for both the healthcare and AI research communities.
  arXiv  Detail & Related papers  (2024-05-28T07:42:55Z)
• A survey of recent methods for addressing AI fairness and bias in biomedicine [48.46929081146017]
  Artificial intelligence systems may perpetuate social inequities or demonstrate biases, such as those based on race or gender. We surveyed recent publications on different debiasing methods in the fields of biomedical natural language processing (NLP) or computer vision (CV) We performed a literature search on PubMed, ACM digital library, and IEEE Xplore of relevant articles published between January 2018 and December 2023 using multiple combinations of keywords. We reviewed other potential methods from the general domain that could be applied to biomedicine to address bias and improve fairness.
  arXiv  Detail & Related papers  (2024-02-13T06:38:46Z)
• Exploring the Role of Explainability in AI-Assisted Embryo Selection [0.0]
  In Vitro Fertilization is among the most widespread treatments for infertility. One of its main challenges is the evaluation and selection of embryo for implantation. Deep learning based methods are gaining attention, but their opaque nature compromises their acceptance in the clinical context.
  arXiv  Detail & Related papers  (2023-08-01T09:46:31Z)
• 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)
• Breaking Bad News in the Era of Artificial Intelligence and Algorithmic Medicine: An Exploration of Disclosure and its Ethical Justification using the Hedonic Calculus [0.0]
  We show how the 'Felicific Calculus' may have a timely quasi-quantitative application in the age of AI. We show how this ethical algorithm can be used to assess, across seven mutually exclusive and exhaustive domains, whether an AI-supported action can be morally justified.
  arXiv  Detail & Related papers  (2022-06-23T16:54:18Z)
• Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
  Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research. An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system. We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
  arXiv  Detail & Related papers  (2022-04-11T14:36:39Z)
• A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 [75.84689958489724]
  The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19.
  arXiv  Detail & Related papers  (2020-07-04T22:48:15Z)
• Artificial Artificial Intelligence: Measuring Influence of AI 'Assessments' on Moral Decision-Making [48.66982301902923]
  We examined the effect of feedback from false AI on moral decision-making about donor kidney allocation. We found some evidence that judgments about whether a patient should receive a kidney can be influenced by feedback about participants' own decision-making perceived to be given by AI.
  arXiv  Detail & Related papers  (2020-01-13T14:15:18Z)

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.