Artificial Intelligence Alongside Physicians in Canada: Reality and Risks (original) (raw)
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AI Ethics Journal, 2020
Machine learning algorithms have been shown to be capable of diagnosing cancer, Alzheimer's disease and even selecting treatment options. However, the majority of machine learning systems implemented in the healthcare setting tend to be based on the supervised machine learning paradigm. These systems tend to rely on previously collected data annotated by medical personnel from specific populations. This leads to 'learnt' machine learning models that lack generalizability. In other words, the machine's predictions are not as accurate for certain populations and can disagree with recommendations of medical experts who did not annotate the data used to train these models. With each human-decided aspect of building supervised machine learning models, human bias is introduced into the machine's decision-making. This human bias is the source of numerous ethical concerns. In this article, we describe and discuss three challenges to generalizability which affect real world deployment of machine learning systems in clinical practice. First, there is bias which occurs due to the characteristics of the population from which data was collected. Second, the bias which occurs due to the prejudice of the expert annotator involved. And third, the bias by the timing of when A.I. processes start training themselves. We also discuss the future implications of these biases. More importantly, we describe how responsible data sharing can help mitigate the effects of these biases-and allow for the development of novel algorithms which may be able to train in an unbiased manner. We discuss environmental and regulatory hurdles which hinder the sharing of data in medicine-and discuss possible updates to current regulations that may enable ethical data sharing for machine learning. With these updates in mind, we also discuss emerging algorithmic frameworks being used to create medical machine learning systems, which can eventually learn to be free from population-and expert-induced bias. These models can then truly be deployed to clinics worldwide, making medicine both cheaper and more accessible for the world at large.
Regulatory responses to medical machine learning
Journal of Law and the Biosciences
Companies and healthcare providers are developing and implementing new applications of medical artificial intelligence, including the artificial intelligence sub-type of medical machine learning (MML). MML is based on the application of machine learning (ML) algorithms to automatically identify patterns and act on medical data to guide clinical decisions. MML poses challenges and raises important questions, including (1) How will regulators evaluate MML-based medical devices to ensure their safety and effectiveness? and (2) What additional MML considerations should be taken into account in the international context? To address these questions, we analyze the current regulatory approaches to MML in the USA and Europe. We then examine international perspectives and broader implications, discussing considerations such as data privacy, exportation, explanation, training set bias, contextual bias, and trade secrecy.
Artificial Intelligence and Healthcare Regulatory and Legal Concerns
Telehealth and Medicine Today, 2021
We are in a stage of transition as artificial intelligence (AI) is increasingly being used in healthcare across the world. Transitions offer opportunities compounded with difficulties. It is universally accepted that regulations and the law can never keep up with the exponential growth of technology. This paper discusses liability issues when AI is deployed in healthcare. Ever-changing, futuristic, user friendly, uncomplicated regulatory requirements promoting compliance and adherence are needed. Regulators have to understand that software itself could be a software as a medical device (SaMD). Benefits of AI could be delayed if slow, expensive clinical trials are mandated. Regulations should distinguish between diagnostic errors, malfunction of technology, or errors due to initial use of inaccurate/inappropriate data as training data sets. The sharing of responsibility and accountability when implementation of an AI-based recommendation causes clinical problems is not clear. Legislation is necessary to allow apportionment of damages consequent to malfunction of an AI-enabled system. Product liability is ascribed to defective equipment and medical devices. However, Watson, the AI-enabled supercomputer, is treated as a consulting physician and not categorised as a product. In India, algorithms cannot be patented. There are no specific laws enacted to deal with AI in healthcare. DISHA or the Digital Information Security in Healthcare Act when implemented in India would hopefully cover some issues. Ultimately, the law is interpreted contextually and perceptions could be different among patients, clinicians and the legal system. This communication is to create the necessary awareness among all stakeholders.
Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?
Frontiers in Surgery, 2022
The legal and ethical issues that confront society due to Artificial Intelligence (AI) include privacy and surveillance, bias or discrimination, and potentially the philosophical challenge is the role of human judgment. Concerns about newer digital technologies becoming a new source of inaccuracy and data breaches have arisen as a result of its use. Mistakes in the procedure or protocol in the field of healthcare can have devastating consequences for the patient who is the victim of the error. Because patients come into contact with physicians at moments in their lives when they are most vulnerable, it is crucial to remember this. Currently, there are no well-defined regulations in place to address the legal and ethical issues that may arise due to the use of artificial intelligence in healthcare settings. This review attempts to address these pertinent issues highlighting the need for algorithmic transparency, privacy, and protection of all the beneficiaries involved and cybersecur...
American Journal of Bioethics, 2023
[01] A model of a person’s health condition does not of itself entail the analyst’s lack of moral concern for the person modeled. A competent physician can both treat the body as an object of technical analysis and at the same time grasp that it is always also a morally relevant human subject. [02] Identifying often subtle social determinants of an individual’s health is a technical task that requires interdisciplinary analysis combining, say, sociological scholarship with clinical insights. Scientists and engineers may unknowingly embed social prejudices in developing AI-systems. But the goal of bias-free AI need not be defeated by this enduring danger. Researchers are well able to identify the biases where they manifest themselves and to make corrections in design or programming. [03] A medical professional who deploys a digital model of a patient need not confuse the model with reality. She need not displace bioethical principles of clinical practice with patient data. [04] The cognitive act of representing does not require the analyst to exclude alternative acts of representation, nor does it constrain her to represent in only certain ways. [05] Digital medical solutions pose no inherent threat to patients because social bias is not primarily a computational or algorithmic phenomenon. It is a product of institutions, cultural inheritances, poverty, and other environments that produce and perpetuate social inequities as well as some health disparities. AI-based medical practices can threaten physicians’ ethical obligations only if allowed to do so. [06] While AI may generate unwanted, unintended consequences, the potential moral and legal challenges that AI poses derive from inadequate precautionary measures by humans, not from features of AI as such. [07] Responsibility for failures of AI to meet normative standards for the treatment of human beings resides with human beings. [08] The moral capacity of human cognition is the capacity for a mutual attribution of responsibility among members of political community. Outsourcing, to AI, moral and legal responsibility for social conditions that affect citizens adversely would undermine the politics of mutual responsibility. [09] The project of identifying AI-based medical solutions that threaten physicians’ ethical obligations should ask: How are real bodies to be digitally represented such that all members of the population benefit from these rapidly developing technologies equitably? This question is not about the nature of AI-based representation but about the just distribution, within a political community, of the health benefits that medical digital solutions may offer.
Artificial Intelligence: Legal and Ethical Perspectives in the Health Care Sector
Science of Law, 2024
In this study, the researchers aim to establish how Artificial Intelligence (AI) has revolutionized the health care industry and the ethical and legal issues pertaining to the use of such technology in this organization. The study provides recommendations for implementing value-adding measures to ensure the safe, secure, and ethical use of AI in healthcare, as well as addressing important concerns and providing solutions to effectively implement AI. Using a quantitative research design, the study uses primary and secondary data to critically analyze relevant literature and existing information. It highlights key challenges that come about because of the current boundaries of regulating AI in healthcare, including but not limited to informed consent, transparency, privacy, data protection, and fairness. The study is fundamentally important to the theory and practice of the implementation of AI technologies, as it illustrates the high potential of using them in the sphere of patient care and, at the same time, cites significant ethical and legal issues in their application. To fully achieve the rightly hailed benefits of AI in health care, we must address these issues. To use the AI components responsibly, rules and regulations of ethical and legal standards must change to accommodate key concerns such as consent, ownership, disclosure, and bias. These measures are critically important to centralize patient rights protection and build confidence in health care organizations. Consequently, this study offers practical policy implications that policymakers, healthcare practitioners, and technologists should consider when implementing regulatory policies. Thus, on one hand, such frameworks allow bringing innovation into the field of healthcare by AI while, on the other hand, maintaining compliance to guarantee that such solutions will be both effective and fair.
Accountability, secrecy, and innovation in AI-enabled clinical decision software
Journal of Law and the Biosciences
This article employs analytical and empirical tools to dissect the complex relationship between secrecy, accountability, and innovation incentives in clinical decision software enabled by machine learning (ML-CD). Although secrecy can provide incentives for innovation, it can also diminish the ability of third parties to adjudicate risk and benefit responsibly. Our first aim is descriptive. We address how the interrelated regimes of intellectual property law, Food and Drug Administration (FDA) regulation, and tort liability are currently shaping information flow and innovation incentives. We find that developers regard secrecy over training data and details of the trained model as central to competitive advantage. Meanwhile, neither FDA nor adopters are currently asking for these types of details. In addition, in some cases, it is not clear whether developers are being asked to provide rigorous evidence of performance. FDA, Congress, developers, and adopters could all do more to pro...
Artificial intelligence in medicine and the disclosure of risks
AI & SOCIETY
This paper focuses on the use of ‘black box’ AI in medicine and asks whether the physician needs to disclose to patients that even the best AI comes with the risks of cyberattacks, systematic bias, and a particular type of mismatch between AI’s implicit assumptions and an individual patient’s background situation. Pace current clinical practice, I argue that, under certain circumstances, these risks do need to be disclosed. Otherwise, the physician either vitiates a patient’s informed consent or violates a more general obligation to warn him about potentially harmful consequences. To support this view, I argue, first, that the already widely accepted conditions in the evaluation of risks, i.e. the ‘nature’ and ‘likelihood’ of risks, speak in favour of disclosure and, second, that principled objections against the disclosure of these risks do not withstand scrutiny. Moreover, I also explain that these risks are exacerbated by pandemics like the COVID-19 crisis, which further emphasis...
Implementing Machine Learning in Health Care - Addressing Ethical Challenges
The New England journal of medicine, 2018
The incorporation of machine learning into clinical medicine holds promise for substantially improving health care delivery. Private companies are rushing to build machine learning into medical decision making, pursuing both tools that support physicians and algorithms designed to function independently of them. Physician-researchers are predicting that familiarity with machine-learning tools for analyzing big data will be a fundamental requirement for the next generation of physicians and that algorithms might soon rival or replace physicians in fields that involve close scrutiny of images, such as radiology and anatomical pathology. 1
The Regulation of Artificial Intelligence in Healthcare
Proceedings of the Conference on Computers and People Research
The new capabilities that emerged with artificial intelligence also target healthcare settings. Nevertheless, they carry potential benefits and challenges that need to be addressed appropriately. In this paper, we mapped the current status of scientific debate on two key regulation aspects of AI in healthcare: the objects of regulation and the affected actors. Data-related issues are the most researched, such as collection, storage, and privacy. In addition, nearly half of the papers targeted policymakers with challenges, comparative analysis, and governance models. Based on these results, we propose a scheme to organize these aspects and discuss implications for AI management, policymaking, and research in this field. CCS CONCEPTS •Social and professional topics → Computing / technology policy →Medical information policy → Medical technologies• Applied computing →Life and medical sciences → Health informatics