ChatGPT in private practice: The opportunities and pitfalls of novel technology

OBJECTIVE

This article explores the transformative impact of OpenAI and ChatGPT on Australian medical practitioners, particularly psychiatrists in the private practice setting. It delves into the extensive benefits and limitations associated with integrating ChatGPT into medical practice, summarising current policies and scrutinising medicolegal implications.

CONCLUSION

A careful assessment is imperative to determine whether the benefits of AI integration outweigh the associated risks. Practitioners are urged to review AI-generated content to ensure its accuracy, recognising that liability likely resides with them rather than with AI platforms, despite the lack of case law specific to negligence and AI in the Australian context at present. It is important to employ measures that ensure patient confidentiality is not breached and practitioners are encouraged to seek counsel from their professional indemnity insurer. There is considerable potential for future development of specialised AI software tailored specifically for the medical profession, making the use of AI more suitable for the medical field in the Australian legal landscape. Moving forward, it is essential to embrace technology and actively address its challenges rather than dismissing AI integration into medical practice. It is becoming increasingly essential that both the psychiatric community, medical community at large and policy makers develop comprehensive guidelines to fill existing policy gaps and adapt to the evolving landscape of AI technologies in healthcare.

Health Care AI and Patient Privacy-Dinerstein v Google

This Viewpoint summarizes a recent lawsuit alleging that a hospital violated patients’ privacy by sharing electronic health record (EHR) data with Google for development of medical artificial intelligence (AI) and discusses how the federal court’s decision in the case provides key insights for hospitals planning to share EHR data with for-profit companies developing medical AI.

How Do Policymakers Regulate AI and Accommodate Innovation in Research and Medicine?

In this Medical News article, JAMA Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, and Alondra Nelson, PhD, the Harold F. Linder Professor at the Institute for Advanced Study, discuss effective AI regulation frameworks to accommodate innovation.

Navigating the AI Revolution: The Case for Precise Regulation in Health Care

Health care is undergoing a profound transformation through the integration of artificial intelligence (AI). However, the rapid integration and expansive growth of AI within health care systems present ethical and legal challenges that warrant careful consideration. In this viewpoint, the author argues that the health care domain, due to its complexity, requires specialized approaches to regulating AI. Precise regulation can provide clear guidelines for addressing these challenges, thereby ensuring ethical and legal AI implementations.

The Challenges for Regulating Medical Use of ChatGPT and Other Large Language Models

This Viewpoint discusses how regulators across the world should approach the legal and ethical challenges, including privacy, device regulation, competition, intellectual property rights, cybersecurity, and liability, raised by the medical use of large language models.

A Blanket That Leaves the Feet Cold: Exploring the AI Act Safety Framework for Medical AI

The AI Act is based on, and at the same time aims to protect fundamental rights, implying their protection, while fulfilling the safety requirement prescribed by the AI Act within the whole lifecycle of AI systems. Based on a risk classification, the AI Act provides a set of requirements that each risk class must meet in order for AI to be legitimately offered on the EU market and be considered safe. However, despite their classification, some minimal risk AI systems may still be prone to cause risks to fundamental rights and user safety, and therefore require attention. In this paper we explore the assumption that despite the fact that the AI Act can find broad ex litteris coverage, the significance of this applicability is limited.

AI-based smartphone apps for risk assessment of skin cancer need more evaluation and better regulation

Smartphone applications ("apps") with artificial intelligence (AI) algorithms are increasingly used in healthcare. Widespread adoption of these apps must be supported by a robust evidence-base and app manufacturers' claims appropriately regulated. Current CE marking assessment processes inadequately protect the public against the risks created by using smartphone diagnostic apps.

How US law will evaluate artificial intelligence for covid-19

Daniel E Ho and colleagues explore the legal implications of using artificial intelligence in the response to covid-19 and call for more robust evaluation frameworks

Precision Regulation Approach: A COVID-19 Triggered Regulatory Drive in South Korea

COVID-19 has triggered various changes in our everyday lives and how we conceptualize the functions of governments. Some areas require stricter forms of regulation while others call for deregulation. The challenge for the regulatory authorities is to manage these potentially conflicting demands in regulation and define coherently their overall regulatory rationale. The precision regulation approach can be a helpful approach. It is defined here as a streamlined approach to regulation to deliver the right methods of regulation for the right group of people at the right time. This problem-solving innovation in regulation triggered by the recent epidemiologic crisis in South Korea demonstrates the emergence of the precision regulation approach. South Korea has implemented streamlined fast-track services for the biotechnology industry to produce test kits swiftly. This article expands the definition of precision regulation from AI regulation literature, and positions the term as a new regulatory rationale, not as a regulatory tool, using the case study from South Korea.

Unexpected Inequality: Disparate-Impact From Artificial Intelligence in Healthcare Decisions

Systemic discrimination in healthcare plagues marginalized groups. Physicians incorrectly view people of color as having high pain tolerance, leading to undertreatment. Women with disabilities are often undiagnosed because their symptoms are dismissed. Low-income patients have less access to appropriate treatment. These patterns, and others, reflect long-standing disparities that have become engrained in U.S. health systems. As the healthcare industry adopts artificial intelligence and algorithminformed (AI) tools, it is vital that regulators address healthcare discrimination. AI tools are increasingly used to make both clinical and administrative decisions by hospitals, physicians, and insurers--yet there is no framework that specifically places nondiscrimination obligations on AI users. The Food and Drug Administration has limited authority to regulate AI and has not sought to incorporate anti-discrimination principles in its guidance. Section 1557 of the Affordable Care Act has not been used to enforce nondiscrimination in healthcare AI and is under-utilized by the Office of Civil Rights. State level protections by medical licensing boards or malpractice liability are similarly untested and have not yet extended nondiscrimination obligations to AI. This Article discusses the role of each legal obligation on healthcare AI and the ways in which each system can improve to address discrimination. It highlights the ways in which industries can self-regulate to set nondiscrimination standards and concludes by recommending standards and creating a super-regulator to address disparate impact by AI. As the world moves towards automation, it is imperative that ongoing concerns about systemic discrimination are removed to prevent further marginalization in healthcare.

Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management

PURPOSE OF REVIEW

Machine learning (ML) is increasingly being studied for the screening, diagnosis, and management of diabetes and its complications. Although various models of ML have been developed, most have not led to practical solutions for real-world problems. There has been a disconnect between ML developers, regulatory bodies, health services researchers, clinicians, and patients in their efforts. Our aim is to review the current status of ML in various aspects of diabetes care and identify key challenges that must be overcome to leverage ML to its full potential.

RECENT FINDINGS

ML has led to impressive progress in development of automated insulin delivery systems and diabetic retinopathy screening tools. Compared with these, use of ML in other aspects of diabetes is still at an early stage. The Food & Drug Administration (FDA) is adopting some innovative models to help bring technologies to the market in an expeditious and safe manner. ML has great potential in managing diabetes and the future is in furthering the partnership of regulatory bodies with health service researchers, clinicians, developers, and patients to improve the outcomes of populations and individual patients with diabetes.

[Healthcare data and artificial intelligence: a geostrategic vision]

The rapid deployment of artificial intelligence (AI) and automation in healthcare is highlighting the importance of health data-driven management as a geostrategic lever. From this point of view, the progress made by the United States and China requires a strong European response to develop a responsible vision which adopts an approach aiming at the positive regulation of AI in healthcare.

Is Watson for Oncology per se Unreasonably Dangerous?: Making A Case for How to Prove Products Liability Based on a Flawed Artificial Intelligence Design

Artificial intelligence (AI) machines hold the world's curiosity captive. Futuristic television shows like West World are set in desert lands against pink sunsets where sleek, autonomous AI fulfill every human need, desire, and kink. But I, Robot, a movie where robots turn against the humans they serve, reminds us that AI is precarious. Academicians who study how AI interacts with tort law, such as Jessica Allain, David Vladeck, and Sjur Dyrkoltbotn, claim that the current legal regime is incapable of addressing the liability issues AI present. Both Allain and Vladeck focus their research on whether tort law can accommodate claims against fully autonomous AI machines, while Dyrkoltbotn explores how AI can be leveraged to help plaintiffs identify the genesis of their injuries. The solution this article presents is not exclusively tailored to fully autonomous AI and does not identify how technology can be used in tort claims. It instead demonstrates that the current tort law regime can provide relief to plaintiffs who are injured by AI machines. In particular, this article argues that the manner in which Watson for Oncology is designed presents a new context in which courts should adopt a per se rule of liability that favors plaintiffs who bring damage claims against AI machines by expanding the definition of what it means for a device to be unreasonably dangerous.

Making Policy on Augmented Intelligence in Health Care

In June 2018, the American Medical Association adopted new policy to provide a broad framework for the evolution of artificial intelligence (AI) in health care that is designed to help ensure that AI realizes the benefits it promises for patients, physicians, and the health care community.

[Ethics and artificial intelligence in healthcare, towards positive regulation]

The positive regulation of artificial intelligence in healthcare represents a major stake to allow a diffusion of digital innovation, in a spirit of openness and coherence with ethical values. Operational principles have been proposed, particularly around the concept of Human Guarantee. The opinion issued at the end of 2018 by the National Consultative Ethics Committee is an important step forward in the recognition of this idea which leaves a large capacity for initiative to professionals and patients.

Legally Human? 'Novel Beings' and English Law

Novel beings-intelligent, conscious life-forms sapient in the same way or greater than are human beings-are no longer the preserve of science fiction. Through technologies such as artificial general intelligence, synthetic genomics, gene printing, cognitive enhancement, advanced neuroscience, and more, they are becoming ever more likely and by some definitions may already be emerging. Consideration of the nature of intelligent, conscious novel beings such as those that may result from these technologies requires analysis of the concept of the 'reasonable creature in being' in English law, as well as of the right to life as founded in the European Convention on Human Rights and the attempts to endow human status on animals in recent years. Our exploration of these issues leads us to conclude that there is a strong case to recognize such 'novel' beings as entitled to the same fundamental rights to life, freedom from inhumane treatment, and liberty as we are.

Artificial Intelligence and the 'Good Society': the US, EU, and UK approach

In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence (AI). In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a 'good AI society'. To do so, we examine how each report addresses the following three topics: (a) the development of a 'good AI society'; (b) the role and responsibility of the government, the private sector, and the research community (including academia) in pursuing such a development; and (c) where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a 'good AI society'. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach.

Artificial intelligence. Fears of an AI pioneer

From the enraged robots in the 1920 play R.U.R. to the homicidal computer H.A.L. in 2001: A Space Odyssey, science fiction writers have embraced the dark side of artificial intelligence (AI) ever since the concept entered our collective imagination. Sluggish progress in AI research, especially during the “AI winter” of the 1970s and 1980s, made such worries seem far-fetched. But recent breakthroughs in machine learning and vast improvements in computational power have brought a flood of research funding— and fresh concerns about where AI may lead us. One researcher now speaking up is Stuart Russell, a computer scientist at the University of California, Berkeley, who with Peter Norvig, director of research at Google, wrote the premier AI textbook, Artificial Intelligence: A Modern Approach, now in its third edition. Last year, Russell joined the Centre for the Study of Existential Risk at Cambridge University in the United Kingdom as an AI expert focusing on “risks that could lead to human extinction.” Among his chief concerns, which he aired at an April meeting in Geneva, Switzerland, run by the United Nations, is the danger of putting military drones and weaponry under the full control of AI systems. This interview has been edited for clarity and brevity.

Legal aspects of personal health monitoring

Personal health monitoring (PHM) can be defined as comprising all technical systems, processing, collecting, and storing of data linked to a person. PHM involves several legal issues that are described in this paper. This article analyses firstly the short term actions that are needed at the European level to allow personal health monitoring in respect of the interests and rights of patients such as the need to have more harmonised medical liability rules at the EU level. Introducing PHM implies also legal action at the EU level on the long run. These long-term actions are related to e.g. the way in which hospitals are organised in their relation with healthcare professionals and with other hospitals or healthcare actors. The paper analyses finally also how health monitoring projects may change the traditional (non-)relationship between patients and pharmaceutical/medical device industry. Today, the producers and distributors of medicinal products have no specific contact with patients. This situation may change when applying telemonitoring projects and may require new legal rules.