A critical perspective on guidelines for responsible and trustworthy artificial intelligence

Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of "Responsibility" in Artificial Intelligence within the Healthcare Context

The increasing integration of artificial intelligence (AI) in healthcare presents a host of ethical, legal, social, and political challenges involving various stakeholders. These challenges prompt various studies proposing frameworks and guidelines to tackle these issues, emphasizing distinct phases of AI development, deployment, and oversight. As a result, the notion of responsible AI has become widespread, incorporating ethical principles such as transparency, fairness, responsibility, and privacy. This paper explores the existing literature on AI use in healthcare to examine how it addresses, defines, and discusses the concept of responsibility. We conducted a scoping review of literature related to AI responsibility in healthcare, searching databases and reference lists between January 2017 and January 2022 for terms related to "responsibility" and "AI in healthcare", and their derivatives. Following screening, 136 articles were included. Data were grouped into four thematic categories: (1) the variety of terminology used to describe and address responsibility; (2) principles and concepts associated with responsibility; (3) stakeholders' responsibilities in AI clinical development, use, and deployment; and (4) recommendations for addressing responsibility concerns. The results show the lack of a clear definition of AI responsibility in healthcare and highlight the importance of ensuring responsible development and implementation of AI in healthcare. Further research is necessary to clarify this notion to contribute to developing frameworks regarding the type of responsibility (ethical/moral/professional, legal, and causal) of various stakeholders involved in the AI lifecycle.

Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices

The use of AI in healthcare has sparked much debate among philosophers, ethicists, regulators and policymakers who raised concerns about the implications of such technologies. The presented scoping review captures the progression of the ethical and legal debate and the proposed ethical frameworks available concerning the use of AI-based medical technologies, capturing key themes across a wide range of medical contexts. The ethical dimensions are synthesised in order to produce a coherent ethical framework for AI-based medical technologies, highlighting how transparency, accountability, confidentiality, autonomy, trust and fairness are the top six recurrent ethical issues. The literature also highlighted how it is essential to increase ethical awareness through interdisciplinary research, such that researchers, AI developers and regulators have the necessary education/competence or networks and tools to ensure proper consideration of ethical matters in the conception and design of new AI technologies and their norms. Interdisciplinarity throughout research, regulation and implementation will help ensure AI-based medical devices are ethical, clinically effective and safe. Achieving these goals will facilitate successful translation of AI into healthcare systems, which currently is lagging behind other sectors, to ensure timely achievement of health benefits to patients and the public.

Generative Adversarial Networks in Medicine: Important Considerations for this Emerging Innovation in Artificial Intelligence

The advent of artificial intelligence (AI) and machine learning (ML) has revolutionized the field of medicine. Although highly effective, the rapid expansion of this technology has created some anticipated and unanticipated bioethical considerations. With these powerful applications, there is a necessity for framework regulations to ensure equitable and safe deployment of technology. Generative Adversarial Networks (GANs) are emerging ML techniques that have immense applications in medical imaging due to their ability to produce synthetic medical images and aid in medical AI training. Producing accurate synthetic images with GANs can address current limitations in AI development for medical imaging and overcome current dataset type and size constraints. Offsetting these constraints can dramatically improve the development and implementation of AI medical imaging and restructure the practice of medicine. As observed with its other AI predecessors, considerations must be taken into place to help regulate its development for clinical use. In this paper, we discuss the legal, ethical, and technical challenges for future safe integration of this technology in the healthcare sector.

Artificial Intelligence-Based Clinical Decision Support Systems in Geriatrics: An Ethical Analysis

OBJECTIVES

To provide an ethical analysis of the implications of the usage of artificial intelligence-supported clinical decision support systems (AI-CDSS) in geriatrics.

DESIGN

Ethical analysis based on the normative arguments regarding the use of AI-CDSS in geriatrics using a principle-based ethical framework.

SETTING AND PARTICIPANTS

Normative arguments identified in 29 articles on AI-CDSS in geriatrics.

METHODS

Our analysis is based on a literature search that was done to determine ethical arguments that are currently discussed regarding AI-CDSS. The relevant articles were subjected to a detailed qualitative analysis regarding the ethical considerations Supplementary Datamentioned therein. We then discussed the identified arguments within the frame of the 4 principles of medical ethics according to Beauchamp and Childress and with respect to the needs of frail older adults.

RESULTS

We found a total of 5089 articles; 29 articles met the inclusion criteria and were subsequently subjected to a detailed qualitative analysis. We could not identify any systematic analysis of the ethical implications of AI-CDSS in geriatrics. The ethical considerations are very unsystematic and scattered, and the existing literature has a predominantly technical focus emphasizing the technology's utility. In an extensive ethical analysis, we systematically discuss the ethical implications of the usage of AI-CDSS in geriatrics.

CONCLUSIONS AND IMPLICATIONS

AI-CDSS in geriatrics can be a great asset, especially when dealing with patients with cognitive disorders; however, from an ethical perspective, we see the need for further research. By using AI-CDSS, older patients' values and beliefs might be overlooked, and the quality of the doctor-patient relationship might be altered, endangering compliance to the 4 ethical principles of Beauchamp and Childress.

Ethical Challenges in AI Approaches to Eating Disorders

The use of artificial intelligence (AI) to assist with the prevention, identification, and management of eating disorders and body image concerns is exciting, but it is not without risk. Technology is advancing rapidly, and ensuring that responsible standards are in place to mitigate risk and protect users is vital to the success and safety of technologies and users.

Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging

A growing number of artificial intelligence (AI)-based systems are being proposed and developed in cardiology, driven by the increasing need to deal with the vast amount of clinical and imaging data with the ultimate aim of advancing patient care, diagnosis and prognostication. However, there is a critical gap between the development and clinical deployment of AI tools. A key consideration for implementing AI tools into real-life clinical practice is their "trustworthiness" by end-users. Namely, we must ensure that AI systems can be trusted and adopted by all parties involved, including clinicians and patients. Here we provide a summary of the concepts involved in developing a "trustworthy AI system." We describe the main risks of AI applications and potential mitigation techniques for the wider application of these promising techniques in the context of cardiovascular imaging. Finally, we show why trustworthy AI concepts are important governing forces of AI development.

Sage Against the Machine: Promise and Challenge of Artificial Intelligence in Epilepsy

Multicenter Validation of a Deep Learning Detection Algorithm for Focal Cortical Dysplasia

Gill RS, Lee HM, Caldairou B, et al. Neurology. 2021 Oct 19;97(16):e1571-e1582. doi:10.1212/WNL.0000000000012698. Epub 2021 Sep 14. PMID: 34521691; PMCID: PMC8548962.

Background and Objective:

To test the hypothesis that a multicenter-validated computer deep learning algorithm detects MRI-negative focal cortical dysplasia (FCD).

Methods:

We used clinically acquired 3-dimensional (3D) T1-weighted and 3D fluid-attenuated inversion recovery MRI of 148 patients (median age 23 years [range 2-55 years]; 47% female) with histologically verified FCD at 9 centers to train a deep convolutional neural network (CNN) classifier. Images were initially deemed MRI-negative in 51% of patients, in whom intracranial EEG determined the focus. For risk stratification, the CNN incorporated bayesian uncertainty estimation as a measure of confidence. To evaluate performance, detection maps were compared to expert FCD manual labels. Sensitivity was tested in an independent cohort of 23 cases with FCD (13 ± 10 years). Applying the algorithm to 42 healthy controls and 89 controls with temporal lobe epilepsy disease tested specificity.

Results:

Overall sensitivity was 93% (137 of 148 FCD detected) using a leave-one-site-out cross-validation, with an average of 6 false positives per patient. Sensitivity in MRI-negative FCD was 85%. In 73% of patients, the FCD was among the clusters with the highest confidence; in half, it ranked the highest. Sensitivity in the independent cohort was 83% (19 of 23; average of 5 false positives per patient). Specificity was 89% in healthy and disease controls.

Discussion:

This first multicenter-validated deep learning detection algorithm yields the highest sensitivity to date in MRI-negative FCD. By pairing predictions with risk stratification, this classifier may assist clinicians in adjusting hypotheses relative to other tests, increasing diagnostic confidence. Moreover, generalizability across age and MRI hardware makes this approach ideal for presurgical evaluation of MRI-negative epilepsy. Classification of evidence: This study provides Class III evidence that deep learning on multimodal MRI accurately identifies FCD in patients with epilepsy initially diagnosed as MRI negative.

Man is a "Rope" Stretched Between Virosphere and Humanoid Robots: On the Urgent Need of an Ethical Code for Ecosystem Survival

In this paper we compare the strategies applied by two successful biological components of the ecosystem, the viruses and the human beings, to interact with the environment. Viruses have had and still exert deep and vast actions on the ecosystem especially at the genome level of most of its biotic components. We discuss on the importance of the human being as contraptions maker in particular of robots, hence of machines capable of automatically carrying out complex series of actions. Beside the relevance of designing and assembling these contraptions, it is of basic importance the goal for which they are assembled and future scenarios of their possible impact on the ecosystem. We can't procrastinate the development and implementation of a highly inspired and stringent "ethical code" for human beings and humanoid robots because it will be a crucial aspect for the wellbeing of the mankind and of the entire ecosystem.

Gut microbiome, big data and machine learning to promote precision medicine for cancer

The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The 'omics' technologies used for microbiome analysis continuously evolve and, although much of the research is still at an early stage, large-scale datasets of ever increasing size and complexity are being produced. However, there are varying levels of difficulty in realizing the full potential of these new tools, which limit our ability to critically analyse much of the available data. In this Perspective, we provide a brief overview on the role of gut microbiome in cancer and focus on the need, role and limitations of a machine learning-driven approach to analyse large amounts of complex health-care information in the era of big data. We also discuss the potential application of microbiome-based big data aimed at promoting precision medicine in cancer.