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Bouhouita-Guermech S, Haidar H. Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of "Responsibility" in Artificial Intelligence within the Healthcare Context. Asian Bioeth Rev 2024; 16:315-344. [PMID: 39022380 PMCID: PMC11250714 DOI: 10.1007/s41649-024-00292-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 07/20/2024] Open
Abstract
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.
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Affiliation(s)
| | - Hazar Haidar
- Ethics Programs, Department of Letters and Humanities, University of Quebec at Rimouski, Rimouski, Québec Canada
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Byeon H, Al-Kubaisi M, Dutta AK, Alghayadh F, Soni M, Bhende M, Chunduri V, Suresh Babu K, Jeet R. Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model. Front Comput Neurosci 2024; 18:1391025. [PMID: 38634017 PMCID: PMC11021780 DOI: 10.3389/fncom.2024.1391025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 03/21/2024] [Indexed: 04/19/2024] Open
Abstract
According to experts in neurology, brain tumours pose a serious risk to human health. The clinical identification and treatment of brain tumours rely heavily on accurate segmentation. The varied sizes, forms, and locations of brain tumours make accurate automated segmentation a formidable obstacle in the field of neuroscience. U-Net, with its computational intelligence and concise design, has lately been the go-to model for fixing medical picture segmentation issues. Problems with restricted local receptive fields, lost spatial information, and inadequate contextual information are still plaguing artificial intelligence. A convolutional neural network (CNN) and a Mel-spectrogram are the basis of this cough recognition technique. First, we combine the voice in a variety of intricate settings and improve the audio data. After that, we preprocess the data to make sure its length is consistent and create a Mel-spectrogram out of it. A novel model for brain tumor segmentation (BTS), Intelligence Cascade U-Net (ICU-Net), is proposed to address these issues. It is built on dynamic convolution and uses a non-local attention mechanism. In order to reconstruct more detailed spatial information on brain tumours, the principal design is a two-stage cascade of 3DU-Net. The paper's objective is to identify the best learnable parameters that will maximize the likelihood of the data. After the network's ability to gather long-distance dependencies for AI, Expectation-Maximization is applied to the cascade network's lateral connections, enabling it to leverage contextual data more effectively. Lastly, to enhance the network's ability to capture local characteristics, dynamic convolutions with local adaptive capabilities are used in place of the cascade network's standard convolutions. We compared our results to those of other typical methods and ran extensive testing utilising the publicly available BraTS 2019/2020 datasets. The suggested method performs well on tasks involving BTS, according to the experimental data. The Dice scores for tumor core (TC), complete tumor, and enhanced tumor segmentation BraTS 2019/2020 validation sets are 0.897/0.903, 0.826/0.828, and 0.781/0.786, respectively, indicating high performance in BTS.
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Affiliation(s)
- Haewon Byeon
- Department of Digital Anti-Aging Healthcare, Inje University, Gimhae, Republic of Korea
| | - Mohannad Al-Kubaisi
- Department of Computer Science, Al-Maarif University College, Al-Anbar Governorate, Iraq
| | - Ashit Kumar Dutta
- Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
| | - Faisal Alghayadh
- Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
| | - Mukesh Soni
- Department of CSE, University Centre for Research and Development, Chandigarh University, Mohali, Punjab, India
| | - Manisha Bhende
- Dr. D. Y. Patil Vidyapeeth, Pune, Dr. D. Y. Patil School of Science & Technology, Tathawade, Pune, India
| | - Venkata Chunduri
- Department of Mathematics and Computer Science, Indiana State University, Terre Haute, IN, United States
| | - K. Suresh Babu
- Department of Biochemistry, Symbiosis Medical College for Women, Symbiosis International (Deemed University), Pune, India
| | - Rubal Jeet
- Chandigarh Engineering College, Jhanjeri, Mohali, India
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Sokołowska B. Being in Virtual Reality and Its Influence on Brain Health-An Overview of Benefits, Limitations and Prospects. Brain Sci 2024; 14:72. [PMID: 38248287 PMCID: PMC10813118 DOI: 10.3390/brainsci14010072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/17/2023] [Accepted: 01/08/2024] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Dynamic technological development and its enormous impact on modern societies are posing new challenges for 21st-century neuroscience. A special place is occupied by technologies based on virtual reality (VR). VR tools have already played a significant role in both basic and clinical neuroscience due to their high accuracy, sensitivity and specificity and, above all, high ecological value. OBJECTIVE Being in a digital world affects the functioning of the body as a whole and its individual systems. The data obtained so far, both from experimental and modeling studies, as well as (clinical) observations, indicate their great and promising potential, but apart from the benefits, there are also losses and negative consequences for users. METHODS This review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework across electronic databases (such as Web of Science Core Collection; PubMed; and Scopus, Taylor & Francis Online and Wiley Online Library) to identify beneficial effects and applications, as well as adverse impacts, especially on brain health in human neuroscience. RESULTS More than half of these articles were published within the last five years and represent state-of-the-art approaches and results (e.g., 54.7% in Web of Sciences and 63.4% in PubMed), with review papers accounting for approximately 16%. The results show that in addition to proposed novel devices and systems, various methods or procedures for testing, validation and standardization are presented (about 1% of articles). Also included are virtual developers and experts, (bio)(neuro)informatics specialists, neuroscientists and medical professionals. CONCLUSIONS VR environments allow for expanding the field of research on perception and cognitive and motor imagery, both in healthy and patient populations. In this context, research on neuroplasticity phenomena, including mirror neuron networks and the effects of applied virtual (mirror) tasks and training, is of interest in virtual prevention and neurogeriatrics, especially in neurotherapy and neurorehabilitation in basic/clinical and digital neuroscience.
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Affiliation(s)
- Beata Sokołowska
- Bioinformatics Laboratory, Mossakowski Medical Research Institute, Polish Academy of Sciences, 02-106 Warsaw, Poland
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Lissak IA, Edlow BL, Rosenthal E, Young MJ. Ethical Considerations in Neuroprognostication Following Acute Brain Injury. Semin Neurol 2023; 43:758-767. [PMID: 37802121 DOI: 10.1055/s-0043-1775597] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
Neuroprognostication following acute brain injury (ABI) is a complex process that involves integrating vast amounts of information to predict a patient's likely trajectory of neurologic recovery. In this setting, critically evaluating salient ethical questions is imperative, and the implications often inform high-stakes conversations about the continuation, limitation, or withdrawal of life-sustaining therapy. While neuroprognostication is central to these clinical "life-or-death" decisions, the ethical underpinnings of neuroprognostication itself have been underexplored for patients with ABI. In this article, we discuss the ethical challenges of individualized neuroprognostication including parsing and communicating its inherent uncertainty to surrogate decision-makers. We also explore the population-based ethical considerations that arise in the context of heterogenous prognostication practices. Finally, we examine the emergence of artificial intelligence-aided neuroprognostication, proposing an ethical framework relevant to both modern and longstanding prognostic tools.
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Affiliation(s)
- India A Lissak
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Brian L Edlow
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Eric Rosenthal
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Michael J Young
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
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Ishida S, Nishitsutsumi Y, Kashioka H, Taguchi T, Shineha R. A comparative review on neuroethical issues in neuroscientific and neuroethical journals. Front Neurosci 2023; 17:1160611. [PMID: 37781239 PMCID: PMC10536163 DOI: 10.3389/fnins.2023.1160611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
This study is a pilot literature review that compares the interest of neuroethicists and neuroscientists. It aims to determine whether there is a significant gap between the neuroethical issues addressed in philosophical neuroethics journals and neuroscience journals. We retrieved 614 articles from two specialist neuroethics journals (Neuroethics and AJOB Neuroscience) and 82 neuroethics-focused articles from three specialist neuroscience journals (Neuron, Nature Neuroscience, and Nature Reviews Neuroscience). We classified these articles in light of the neuroethical issue in question before we compared the neuroethical issues addressed in philosophical neuroethics with those addressed by neuroscientists. A notable result is a parallelism between them as a general tendency. Neuroscientific articles cover most neuroethical issues discussed by philosophical ethicists and vice versa. Subsequently, there are notable discrepancies between the two bodies of neuroethics literature. For instance, theoretical questions, such as the ethics of moral enhancement and the philosophical implications of neuroscientific findings on our conception of personhood, are more intensely discussed in philosophical-neuroethical articles. Conversely, neuroscientific articles tend to emphasize practical questions, such as how to successfully integrate ethical perspectives into scientific research projects and justifiable practices of animal-involving neuroscientific research. These observations will help us settle the common starting point of the attempt at "ethics integration" in emerging neuroscience, contributing to better governance design and neuroethical practice.
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Affiliation(s)
- Shu Ishida
- Graduate School of Life Sciences, Tohoku University, Sendai, Japan
| | - Yu Nishitsutsumi
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Japan
| | - Hideki Kashioka
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Japan
| | - Takahisa Taguchi
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Japan
| | - Ryuma Shineha
- Research Center on Ethical, Legal, and Social Issues, Osaka University, Suita, Japan
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Poalelungi DG, Musat CL, Fulga A, Neagu M, Neagu AI, Piraianu AI, Fulga I. Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. J Pers Med 2023; 13:1214. [PMID: 37623465 PMCID: PMC10455458 DOI: 10.3390/jpm13081214] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 08/26/2023] Open
Abstract
Artificial Intelligence (AI) has emerged as a transformative technology with immense potential in the field of medicine. By leveraging machine learning and deep learning, AI can assist in diagnosis, treatment selection, and patient monitoring, enabling more accurate and efficient healthcare delivery. The widespread implementation of AI in healthcare has the role to revolutionize patients' outcomes and transform the way healthcare is practiced, leading to improved accessibility, affordability, and quality of care. This article explores the diverse applications and reviews the current state of AI adoption in healthcare. It concludes by emphasizing the need for collaboration between physicians and technology experts to harness the full potential of AI.
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Affiliation(s)
- Diana Gina Poalelungi
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
| | - Carmina Liana Musat
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Ana Fulga
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Marius Neagu
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Anca Iulia Neagu
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
- ‘Saint John’ Clinical Emergency Hospital for Children, 800487 Galati, Romania
| | - Alin Ionut Piraianu
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Iuliu Fulga
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
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Bouhouita-Guermech S, Gogognon P, Bélisle-Pipon JC. Specific challenges posed by artificial intelligence in research ethics. Front Artif Intell 2023; 6:1149082. [PMID: 37483869 PMCID: PMC10358356 DOI: 10.3389/frai.2023.1149082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 06/13/2023] [Indexed: 07/25/2023] Open
Abstract
Background The twenty first century is often defined as the era of Artificial Intelligence (AI), which raises many questions regarding its impact on society. It is already significantly changing many practices in different fields. Research ethics (RE) is no exception. Many challenges, including responsibility, privacy, and transparency, are encountered. Research ethics boards (REB) have been established to ensure that ethical practices are adequately followed during research projects. This scoping review aims to bring out the challenges of AI in research ethics and to investigate if REBs are equipped to evaluate them. Methods Three electronic databases were selected to collect peer-reviewed articles that fit the inclusion criteria (English or French, published between 2016 and 2021, containing AI, RE, and REB). Two instigators independently reviewed each piece by screening with Covidence and then coding with NVivo. Results From having a total of 657 articles to review, we were left with a final sample of 28 relevant papers for our scoping review. The selected literature described AI in research ethics (i.e., views on current guidelines, key ethical concept and approaches, key issues of the current state of AI-specific RE guidelines) and REBs regarding AI (i.e., their roles, scope and approaches, key practices and processes, limitations and challenges, stakeholder perceptions). However, the literature often described REBs ethical assessment practices of projects in AI research as lacking knowledge and tools. Conclusion Ethical reflections are taking a step forward while normative guidelines adaptation to AI's reality is still dawdling. This impacts REBs and most stakeholders involved with AI. Indeed, REBs are not equipped enough to adequately evaluate AI research ethics and require standard guidelines to help them do so.
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Affiliation(s)
| | | | - Jean-Christophe Bélisle-Pipon
- School of Public Health, Université de Montréal, Montréal, QC, Canada
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
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Pinto-Pardo N, Ledezma P. Artificial Intelligence and Medicine: A Non-Dominant, Objective Approach to Supported Decision-Making? AJOB Neurosci 2023; 14:249-252. [PMID: 37682674 DOI: 10.1080/21507740.2023.2243867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
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Khosravi P, Schweitzer M. Artificial intelligence in neuroradiology: a scoping review of some ethical challenges. FRONTIERS IN RADIOLOGY 2023; 3:1149461. [PMID: 37492387 PMCID: PMC10365008 DOI: 10.3389/fradi.2023.1149461] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 04/27/2023] [Indexed: 07/27/2023]
Abstract
Artificial intelligence (AI) has great potential to increase accuracy and efficiency in many aspects of neuroradiology. It provides substantial opportunities for insights into brain pathophysiology, developing models to determine treatment decisions, and improving current prognostication as well as diagnostic algorithms. Concurrently, the autonomous use of AI models introduces ethical challenges regarding the scope of informed consent, risks associated with data privacy and protection, potential database biases, as well as responsibility and liability that might potentially arise. In this manuscript, we will first provide a brief overview of AI methods used in neuroradiology and segue into key methodological and ethical challenges. Specifically, we discuss the ethical principles affected by AI approaches to human neuroscience and provisions that might be imposed in this domain to ensure that the benefits of AI frameworks remain in alignment with ethics in research and healthcare in the future.
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Affiliation(s)
- Pegah Khosravi
- Department of Biological Sciences, New York City College of Technology, CUNY, New York City, NY, United States
| | - Mark Schweitzer
- Office of the Vice President for Health Affairs Office of the Vice President, Wayne State University, Detroit, MI, United States
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Surianarayanan C, Lawrence JJ, Chelliah PR, Prakash E, Hewage C. Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3062. [PMID: 36991773 PMCID: PMC10053494 DOI: 10.3390/s23063062] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.
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Affiliation(s)
| | | | | | - Edmond Prakash
- Research Center for Creative Arts, University for the Creative Arts (UCA), Farnham GU9 7DS, UK
| | - Chaminda Hewage
- Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
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Koutsouleris N, Hauser TU, Skvortsova V, De Choudhury M. From promise to practice: towards the realisation of AI-informed mental health care. THE LANCET DIGITAL HEALTH 2022; 4:e829-e840. [DOI: 10.1016/s2589-7500(22)00153-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/14/2022] [Accepted: 07/27/2022] [Indexed: 11/07/2022]
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Valeriani D, Santoro F, Ienca M. The present and future of neural interfaces. Front Neurorobot 2022; 16:953968. [PMID: 36304780 PMCID: PMC9592849 DOI: 10.3389/fnbot.2022.953968] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/13/2022] [Indexed: 11/18/2022] Open
Abstract
The 2020's decade will likely witness an unprecedented development and deployment of neurotechnologies for human rehabilitation, personalized use, and cognitive or other enhancement. New materials and algorithms are already enabling active brain monitoring and are allowing the development of biohybrid and neuromorphic systems that can adapt to the brain. Novel brain-computer interfaces (BCIs) have been proposed to tackle a variety of enhancement and therapeutic challenges, from improving decision-making to modulating mood disorders. While these BCIs have generally been developed in an open-loop modality to optimize their internal neural decoders, this decade will increasingly witness their validation in closed-loop systems that are able to continuously adapt to the user's mental states. Therefore, a proactive ethical approach is needed to ensure that these new technological developments go hand in hand with the development of a sound ethical framework. In this perspective article, we summarize recent developments in neural interfaces, ranging from neurohybrid synapses to closed-loop BCIs, and thereby identify the most promising macro-trends in BCI research, such as simulating vs. interfacing the brain, brain recording vs. brain stimulation, and hardware vs. software technology. Particular attention is devoted to central nervous system interfaces, especially those with application in healthcare and human enhancement. Finally, we critically assess the possible futures of neural interfacing and analyze the short- and long-term implications of such neurotechnologies.
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Affiliation(s)
| | - Francesca Santoro
- Institute for Biological Information Processing - Bioelectronics, IBI-3, Forschungszentrum Juelich, Juelich, Germany
- Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Aachen, Germany
| | - Marcello Ienca
- College of Humanities, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
- *Correspondence: Marcello Ienca
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Gundersen T, Bærøe K. The Future Ethics of Artificial Intelligence in Medicine: Making Sense of Collaborative Models. SCIENCE AND ENGINEERING ETHICS 2022; 28:17. [PMID: 35362822 PMCID: PMC8975759 DOI: 10.1007/s11948-022-00369-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 02/21/2022] [Indexed: 05/14/2023]
Abstract
This article examines the role of medical doctors, AI designers, and other stakeholders in making applied AI and machine learning ethically acceptable on the general premises of shared decision-making in medicine. Recent policy documents such as the EU strategy on trustworthy AI and the research literature have often suggested that AI could be made ethically acceptable by increased collaboration between developers and other stakeholders. The article articulates and examines four central alternative models of how AI can be designed and applied in patient care, which we call the ordinary evidence model, the ethical design model, the collaborative model, and the public deliberation model. We argue that the collaborative model is the most promising for covering most AI technology, while the public deliberation model is called for when the technology is recognized as fundamentally transforming the conditions for ethical shared decision-making.
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Affiliation(s)
- Torbjørn Gundersen
- Centre for the Study of Professions, Oslo Metropolitan University, Oslo, Norway.
| | - Kristine Bærøe
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
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Abstract
AbstractContemporary ethical analysis of Artificial Intelligence (AI) is growing rapidly. One of its most recognizable outcomes is the publication of a number of ethics guidelines that, intended to guide governmental policy, address issues raised by AI design, development, and implementation and generally present a set of recommendations. Here we propose two things: first, regarding content, since some of the applied issues raised by AI are related to fundamental questions about topics like intelligence, consciousness, and the ontological and ethical status of humans, among others, the treatment of these issues would benefit from interfacing with neuroethics that has been addressing those same issues in the context of brain research. Second, the identification and management of some of the practical ethical challenges raised by AI would be enriched by embracing the methodological resources used in neuroethics. In particular, we focus on the methodological distinction between conceptual and action-oriented neuroethical approaches. We argue that the normative (often principles-oriented) discussion about AI will benefit from further integration of conceptual analysis, including analysis of some operative assumptions, their meaning in different contexts, and their mutual relevance in order to avoid misplaced or disproportionate concerns and achieve a more realistic and useful approach to identifying and managing the emerging ethical issues.
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SHIFTing artificial intelligence to be responsible in healthcare: A systematic review. Soc Sci Med 2022; 296:114782. [DOI: 10.1016/j.socscimed.2022.114782] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 02/02/2022] [Accepted: 02/03/2022] [Indexed: 12/12/2022]
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Pang N, Liu Z, Lin Z, Chen X, Liu X, Pan M, Shi K, Xiao Y, Xu L. Fast identification and quantification of c-Fos protein using you-only-look-once-v5. Front Psychiatry 2022; 13:1011296. [PMID: 36213931 PMCID: PMC9537349 DOI: 10.3389/fpsyt.2022.1011296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 09/05/2022] [Indexed: 11/13/2022] Open
Abstract
In neuroscience, protein activity characterizes neuronal excitability in response to a diverse array of external stimuli and represents the cell state throughout the development of brain diseases. Importantly, it is necessary to characterize the proteins involved in disease progression, nuclear function determination, stimulation method effect, and other aspects. Therefore, the quantification of protein activity is indispensable in neuroscience. Currently, ImageJ software and manual counting are two of the most commonly used methods to quantify proteins. To improve the efficiency of quantitative protein statistics, the you-only-look-once-v5 (YOLOv5) model was proposed. In this study, c-Fos immunofluorescence images data set as an example to verify the efficacy of the system using protein quantitative statistics. The results indicate that YOLOv5 was less time-consuming or obtained higher accuracy than other methods (time: ImageJ software: 80.12 ± 1.67 s, manual counting: 3.41 ± 0.25 s, YOLOv5: 0.0251 ± 0.0003 s, p < 0.0001, n = 83; simple linear regression equation: ImageJ software: Y = 1.013 × X + 0.776, R 2 = 0.837; manual counting: Y = 1.0*X + 0, R 2 = 1; YOLOv5: Y = 0.9730*X + 0.3821, R 2 = 0.933, n = 130). The findings suggest that the YOLOv5 algorithm provides feasible methods for quantitative statistical analysis of proteins and has good potential for application in detecting target proteins in neuroscience.
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Affiliation(s)
- Na Pang
- The College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.,Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zihao Liu
- Shenzhen Hospital of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Zhengrong Lin
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiaoyan Chen
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiufang Liu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Min Pan
- Shenzhen Hospital of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Keke Shi
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yang Xiao
- National Innovation Center for Advanced Medical Devices, Shenzhen, China
| | - Lisheng Xu
- The College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.,Key Laboratory of Medical Image Computing, Ministry of Education, Shenyang, China
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17
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Martindale JM, Goldstein J, Xixis K, Lakhotia A, Rodman A, Strauss LD, Strowd RE, Bass N. Be in the Digital Room Where it Happens, Part I: Tweeting & Technology for Career Development. Child Neurol Open 2022; 9:2329048X221106843. [PMID: 35756969 PMCID: PMC9218913 DOI: 10.1177/2329048x221106843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 05/26/2022] [Indexed: 11/22/2022] Open
Abstract
Social media has become a part of everyday life. It has changed the way we obtain and distribute information, connect, and interact with others. As the number of platforms and users grow, medical professionals have learned the value social media can have in education, research, advocacy, and clinical care initiatives. Platforms provide opportunities to network, build collaborations, and develop a reputation. This is part one of a two-part series. This article provides an overview on how social media can benefit professional career development for clinicians and researchers, as well as for advocacy to raise awareness against biases, disparities, and for patient benefit. We review challenges, limitations, and best practices for social media use by medical professionals with neurology-specific examples.
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Affiliation(s)
- Jaclyn M. Martindale
- Department of Neurology, Atrium Health Wake Forest Baptist, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Jessica Goldstein
- Department of Neurology, University of Minnesota School of Medicine, Minneapolis, Minnesota, USA
| | - Kathryn Xixis
- Department of Neurology, University of Virginia, Charlottesville, Virginia, USA
| | - Arpita Lakhotia
- Department of Neurology, University of Louisville, Louisville, Kentucky, USA
| | - Adam Rodman
- Department of General Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Lauren D. Strauss
- Department of Neurology, Atrium Health Wake Forest Baptist, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Roy E. Strowd
- Department of Neurology, Atrium Health Wake Forest Baptist, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Nancy Bass
- Department of Neurology, Case Western Reserve University, Cleveland, Ohio, USA
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18
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Abstract
In recent years, philosophical-legal studies on neuroscience (mainly in the fields of neuroethics and neurolaw) have given increasing prominence to a normative analysis of the ethical-legal challenges in the mind and brain sciences in terms of rights, freedoms, entitlements and associated obligations. This way of analyzing the ethical and legal implications of neuroscience has come to be known as “neurorights.” Neurorights can be defined as the ethical, legal, social, or natural principles of freedom or entitlement related to a person’s cerebral and mental domain; that is, the fundamental normative rules for the protection and preservation of the human brain and mind. Although reflections on neurorights have received ample coverage in the mainstream media and have rapidly become a mainstream topic in the public neuroethics discourse, the frequency of such reflections in the academic literature is still relatively scarce. While the prominence of the neurorights debate in public opinion is crucial to ensure public engagement and democratic participation in deliberative processes on this issue, its relatively sporadic presence in the academic literature poses a risk of semantic-normative ambiguity and conceptual confusion. This risk is exacerbated by the presence of multiple and not always reconcilable terminologies. Several meta-ethical, normative ethical, and legal-philosophical questions need to be solved in order to ensure that neurorights can be used as effective instruments of global neurotechnology governance and be adequately imported into international human rights law. To overcome the shortcomings above, this paper attempts to provide a comprehensive normative-ethical, historical and conceptual analysis of neurorights. In particular, it attempts to (i) reconstruct a history of neurorights and locate these rights in the broader history of idea, (ii) outline a systematic conceptual taxonomy of neurorights, (iii) summarize ongoing policy initiatives related to neurorights, (iv) proactively address some unresolved ethico-legal challenges, and (v) identify priority areas for further academic reflection and policy work in this domain.
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Affiliation(s)
- Marcello Ienca
- College of Humanities, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
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19
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Saheb T, Saheb T, Carpenter DO. Mapping research strands of ethics of artificial intelligence in healthcare: A bibliometric and content analysis. Comput Biol Med 2021; 135:104660. [PMID: 34346319 DOI: 10.1016/j.compbiomed.2021.104660] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 07/15/2021] [Accepted: 07/15/2021] [Indexed: 02/07/2023]
Abstract
The growth of artificial intelligence in promoting healthcare is rapidly progressing. Notwithstanding its promising nature, however, AI in healthcare embodies certain ethical challenges as well. This research aims to delineate the most influential elements of scientific research on AI ethics in healthcare by conducting bibliometric, social network analysis, and cluster-based content analysis of scientific articles. Not only did the bibliometric analysis identify the most influential authors, countries, institutions, sources, and documents, but it also recognized four ethical concerns associated with 12 medical issues. These ethical categories are composed of normative, meta-ethics, epistemological and medical practice. The content analysis complemented this list of ethical categories and distinguished seven more ethical categories: ethics of relationships, medico-legal concerns, ethics of robots, ethics of ambient intelligence, patients' rights, physicians' rights, and ethics of predictive analytics. This analysis likewise identified 40 general research gaps in the literature and plausible future research strands. This analysis furthers conversations on the ethics of AI and associated emerging technologies such as nanotech and biotech in healthcare, hence, advances convergence research on the ethics of AI in healthcare. Practically, this research will provide a map for policymakers and AI engineers and scientists on what dimensions of AI-based medical interventions require stricter policies and guidelines and robust ethical design and development.
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Affiliation(s)
- Tahereh Saheb
- Management Studies Center, Tarbiat Modares University, Tehran, Iran.
| | - Tayebeh Saheb
- Assistant professor, Faculty of Law, Tarbiat Modares University, Tehran, Iran.
| | - David O Carpenter
- Director for the Institute for Health and the Environment, School of Public Health, State University of New York, University at Albany, USA.
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20
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Li X, Fan F, Chen X, Li J, Ning L, Lin K, Chen Z, Qin Z, Yeung AS, Li X, Wang L, So KF. Computer Vision for Brain Disorders Based Primarily on Ocular Responses. Front Neurol 2021; 12:584270. [PMID: 33967931 PMCID: PMC8096911 DOI: 10.3389/fneur.2021.584270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 03/15/2021] [Indexed: 11/18/2022] Open
Abstract
Real-time ocular responses are tightly associated with emotional and cognitive processing within the central nervous system. Patterns seen in saccades, pupillary responses, and spontaneous blinking, as well as retinal microvasculature and morphology visualized via office-based ophthalmic imaging, are potential biomarkers for the screening and evaluation of cognitive and psychiatric disorders. In this review, we outline multiple techniques in which ocular assessments may serve as a non-invasive approach for the early detections of various brain disorders, such as autism spectrum disorder (ASD), Alzheimer's disease (AD), schizophrenia (SZ), and major depressive disorder (MDD). In addition, rapid advances in artificial intelligence (AI) present a growing opportunity to use machine learning-based AI, especially computer vision (CV) with deep-learning neural networks, to shed new light on the field of cognitive neuroscience, which is most likely to lead to novel evaluations and interventions for brain disorders. Hence, we highlight the potential of using AI to evaluate brain disorders based primarily on ocular features.
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Affiliation(s)
- Xiaotao Li
- Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States.,BIAI INC., Chelmsford, MA, United States.,BIAI Intelligence Biotech LLC, Shenzhen, China
| | - Fangfang Fan
- Department of Neurology, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Xuejing Chen
- Retina Division, Department of Ophthalmology, Boston University Eye Associates, Boston University, Boston, MA, United States
| | - Juan Li
- Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China.,BIAI INC., Chelmsford, MA, United States.,BIAI Intelligence Biotech LLC, Shenzhen, China
| | - Li Ning
- Center for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Kangguang Lin
- Department of Affective Disorders and Academician Workstation of Mood and Brain Sciences, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China.,Guangdong-Hong Kong-Macau Institute of Central Nervous System (CNS) Regeneration, Jinan University, Guangzhou, China
| | - Zan Chen
- Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Zhenyun Qin
- Key Laboratory for Nonlinear Mathematical Models and Methods, School of Mathematical Science, Fudan University, Shanghai, China
| | - Albert S Yeung
- Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
| | - Xiaojian Li
- Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Liping Wang
- Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Kwok-Fai So
- Guangdong-Hong Kong-Macau Institute of Central Nervous System (CNS) Regeneration, Jinan University, Guangzhou, China.,The State Key Laboratory of Brain and Cognitive Sciences, Department of Ophthalmology, University of Hong Kong, Pok Fu Lam, Hong Kong
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21
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Ganesan V, Garrard P. Technology, big data, and the future of paediatric neuroscience: let us go then, you and AI. Dev Med Child Neurol 2020; 62:884. [PMID: 32608506 DOI: 10.1111/dmcn.14563] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/20/2020] [Indexed: 11/28/2022]
Affiliation(s)
| | - Peter Garrard
- Molecular and Clinical Science Research Institute, St. George's, University of London, London, UK
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