Track topics on Twitter Track topics that are important to you
In this article, the authors propose an ethical framework for using and sharing clinical data for the development of artificial intelligence (AI) applications. The philosophical premise is as follows: when clinical data are used to provide care, the primary purpose for acquiring the data is fulfilled. At that point, clinical data should be treated as a form of public good, to be used for the benefit of future patients. In their 2013 article, Faden et al argued that all who participate in the health care system, including patients, have a moral obligation to contribute to improving that system. The authors extend that framework to questions surrounding the secondary use of clinical data for AI applications. Specifically, the authors propose that all individuals and entities with access to clinical data become data stewards, with fiduciary (or trust) responsibilities to patients to carefully safeguard patient privacy, and to the public to ensure that the data are made widely available for the development of knowledge and tools to benefit future patients. According to this framework, the authors maintain that it is unethical for providers to "sell" clinical data to other parties by granting access to clinical data, especially under exclusive arrangements, in exchange for monetary or in-kind payments that exceed costs. The authors also propose that patient consent is not required before the data are used for secondary purposes when obtaining such consent is prohibitively costly or burdensome, as long as mechanisms are in place to ensure that ethical standards are strictly followed. Rather than debate whether patients or provider organizations "own" the data, the authors propose that clinical data are not owned at all in the traditional sense, but rather that all who interact with or control the data have an obligation to ensure that the data are used for the benefit of future patients and society.
This article was published in the following journal.
Health care systems worldwide have been influenced by the globally growing trend toward a sharing economy and will likely advance with these trends in the near future. Therefore, based on peer-to-peer...
Artificial intelligence (AI) technologies are facilitating the work of modern healthcare organisations to leverage the power of big data in clinical practice. In most cases, AI-based systems improve c...
Artificial intelligence involves a wide range of smart techniques that are applicable to medical services including nuclear medicine. Recent advances in computer power, availability of accumulated dig...
Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six ...
This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imag...
This study is to build an multi-modal artificial intelligence ophthalmological imaging diagnostic system covering multi-level medical institutions. We are going to evaluate this system in ...
This is an artificial intelligence-based optical artificial intelligence assisted system that can assist endoscopists in improving the quality of endoscopy.
Ovarian cancer is relatively rare but fatal with an annual incidence rate of 11.8 per 100 000 and a high mortality‐to‐incidence ratio of >0.6. The modest diagnostic accuracy of TVU has ...
The aim of this study was to analyze using an artificial intelligence engine (IA) the influence of the pathophysiological environment (set parametric monitoring data, imaging, biology etc....
The Tomey CASIA (Tomey Corporation, Nagoya, Japan) is a novel rapid imaging device that captures high-quality imaging of the entire anterior chamber of the eye over detailed imaging of a s...
A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.
Committees established to review interim data and efficacy outcomes in clinical trials. The findings of these committees are used in deciding whether a trial should be continued as designed, changed, or terminated. Government regulations regarding federally-funded research involving human subjects (the "Common Rule") require (45 CFR 46.111) that research ethics committees reviewing large-scale clinical trials monitor the data collected using a mechanism such as a data monitoring committee. FDA regulations (21 CFR 50.24) require that such committees be established to monitor studies conducted in emergency settings.
The study and implementation of techniques and methods for designing computer systems to perform functions normally associated with human intelligence, such as understanding language, learning, reasoning, problem solving, etc.
Services provided by an individual ethicist (ETHICISTS) or an ethics team or committee (ETHICS COMMITTEES, CLINICAL) to address the ethical issues involved in a specific clinical case. The central purpose is to improve the process and outcomes of patients' care by helping to identify, analyze, and resolve ethical problems.
A formal process of examination of patient care or research proposals for conformity with ethical standards. The review is usually conducted by an organized clinical or research ethics committee (CLINICAL ETHICS COMMITTEES or RESEARCH ETHICS COMMITTEES), sometimes by a subset of such a committee, an ad hoc group, or an individual ethicist (ETHICISTS).
Bioethics is the study of controversial ethics brought about by advances in biology and medicine. Bioethicists are concerned with the ethical questions that arise in the relationships among life sciences, biotechnology, medicine, politics, law, and philo...