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Arif WM. Radiologic Technology Students' Perceptions on Adoption of Artificial Intelligence Technology in Radiology. Int J Gen Med 2024; 17:3129-3136. [PMID: 39049835 PMCID: PMC11268710 DOI: 10.2147/ijgm.s465944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 07/11/2024] [Indexed: 07/27/2024] Open
Abstract
Study Purpose This study aims to analyze radiologic technology student's perceptions of artificial intelligence (AI) and its applications in radiology. Methods A quantitative cross-sectional survey was conducted. A pre-validated survey questionnaire with 17 items related to students perceptions of AI and its applications was used. The sample included radiologic technology students from three universities in Saudi Arabia. The survey was conducted online for several weeks, resulting in a sample of 280 radiologic technology students. Results Of the participants, 63.9% were aware of AI and its applications. T-tests revealed a statistically significant difference (p = 0.0471) between genders with male participants reflecting slightly higher AI awareness than female participants. Regarding the choice of radiology as specialization, 35% of the participants stated that they would not choose radiology, whereas 65% preferred it. Approximately 56% of the participants expressed concerns about the potential replacement of radiology technologists with AI, and 62.1% strongly agreed on the necessity of incorporating known ethical principles into AI. Conclusion The findings reflect a positive evaluation of the applications of this technology, which is attributed to its essential support role. However, tailored education and training programs are necessary to prepare future healthcare professionals for the increasing role of AI in medical sciences.
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Affiliation(s)
- Wejdan M Arif
- King Saud University, College of Applied Medical Sciences, Department of Radiological Sciences, Riyadh, Saudi Arabia
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2
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Contaldo MT, Pasceri G, Vignati G, Bracchi L, Triggiani S, Carrafiello G. AI in Radiology: Navigating Medical Responsibility. Diagnostics (Basel) 2024; 14:1506. [PMID: 39061643 PMCID: PMC11276428 DOI: 10.3390/diagnostics14141506] [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: 05/08/2024] [Revised: 07/10/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024] Open
Abstract
The application of Artificial Intelligence (AI) facilitates medical activities by automating routine tasks for healthcare professionals. AI augments but does not replace human decision-making, thus complicating the process of addressing legal responsibility. This study investigates the legal challenges associated with the medical use of AI in radiology, analyzing relevant case law and literature, with a specific focus on professional liability attribution. In the case of an error, the primary responsibility remains with the physician, with possible shared liability with developers according to the framework of medical device liability. If there is disagreement with the AI's findings, the physician must not only pursue but also justify their choices according to prevailing professional standards. Regulations must balance the autonomy of AI systems with the need for responsible clinical practice. Effective use of AI-generated evaluations requires knowledge of data dynamics and metrics like sensitivity and specificity, even without a clear understanding of the underlying algorithms: the opacity (referred to as the "black box phenomenon") of certain systems raises concerns about the interpretation and actual usability of results for both physicians and patients. AI is redefining healthcare, underscoring the imperative for robust liability frameworks, meticulous updates of systems, and transparent patient communication regarding AI involvement.
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Affiliation(s)
- Maria Teresa Contaldo
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy; (G.V.); (S.T.); (G.C.)
| | - Giovanni Pasceri
- Information Society Law Center, Department “Cesare Beccaria”, University of Milan, 20122 Milan, Italy
| | - Giacomo Vignati
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy; (G.V.); (S.T.); (G.C.)
| | | | - Sonia Triggiani
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy; (G.V.); (S.T.); (G.C.)
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy; (G.V.); (S.T.); (G.C.)
- Radiology and Inverventional Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, 20122 Milan, Italy
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3
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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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4
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Olver IN. Ethics of artificial intelligence in supportive care in cancer. Med J Aust 2024; 220:499-501. [PMID: 38714360 DOI: 10.5694/mja2.52297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 12/22/2023] [Indexed: 05/09/2024]
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Lysø EH, Hesjedal MB, Skolbekken JA, Solbjør M. Men's sociotechnical imaginaries of artificial intelligence for prostate cancer diagnostics - A focus group study. Soc Sci Med 2024; 347:116771. [PMID: 38537333 DOI: 10.1016/j.socscimed.2024.116771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 04/20/2024]
Abstract
Artificial intelligence (AI) is increasingly used for diagnostic purposes in cancer care. Prostate cancer is one of the most prevalent cancers affecting men worldwide, but current diagnostic approaches have limitations in terms of specificity and sensitivity. Using AI to interpret MR images in prostate cancer diagnostics shows promising results, but raises questions about implementation, user acceptance, trust, and doctor-patient communication. Drawing on approaches from the sociology of expectations and theories about sociotechnical imaginaries, we explore men's expectations of artificial intelligence for prostate cancer diagnostics. We conducted ten focus groups with 48 men aged 54-85 in Norway with various experiences of prostate cancer diagnostics. Five groups of men had been treated for prostate cancer, one group was on active surveillance, two groups had been through prostate cancer diagnostics without having a diagnosis, and two groups of men had no experience with prostate cancer diagnostics or treatment. Data was subject to reflexive thematic analysis. Our analysis suggests that men's expectations of AI for prostate cancer diagnostics come from two perspectives: Technology-centered expectations that build on their conceptions of AI's form and agency, and human-centered expectations of AI that build on their perceptions of patient-professional relationships and decision-making processes. These two perspectives are intertwined in three imaginaries of AI: The tool imaginary, the advanced machine imaginary, and the intelligence imaginary - each carrying distinct expectations and ideas of technologies and humans' role in decision-making processes. These expectations are multifaceted and simultaneously optimistic and pessimistic; while AI is expected to improve the accuracy of cancer diagnoses and facilitate more personalized medicine, AI is also expected to threaten interpersonal and communicational relationships between patients and healthcare professionals, and the maintenance of trust in these relationships. This emphasizes how AI cannot be implemented without caution about maintaining human healthcare relationships.
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Affiliation(s)
- Emilie Hybertsen Lysø
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway.
| | - Maria Bårdsen Hesjedal
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway
| | - John-Arne Skolbekken
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway
| | - Marit Solbjør
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway
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Chang CH, Chen CJ, Ma YS, Shen YT, Sung MI, Hsu CC, Lin HJ, Chen ZC, Huang CC, Liu CF. Real-time artificial intelligence predicts adverse outcomes in acute pancreatitis in the emergency department: Comparison with clinical decision rule. Acad Emerg Med 2024; 31:149-155. [PMID: 37885118 DOI: 10.1111/acem.14824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 10/10/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023]
Abstract
OBJECTIVE Artificial intelligence (AI) prediction is increasingly used for decision making in health care, but its application for adverse outcomes in emergency department (ED) patients with acute pancreatitis (AP) is not well understood. This study aimed to clarify this aspect. METHODS Data from 8274 ED patients with AP in three hospitals from 2009 to 2018 were analyzed. Demographic data, comorbidities, laboratory results, and adverse outcomes were included. Six algorithms were evaluated, and the one with the highest area under the curve (AUC) was implemented into the hospital information system (HIS) for real-time prediction. Predictive accuracy was compared between the AI model and Bedside Index for Severity in Acute Pancreatitis (BISAP). RESULTS The mean ± SD age was 56.1 ± 16.7 years, with 67.7% being male. The AI model was successfully implemented in the HIS, with Light Gradient Boosting Machine (LightGBM) showing the highest AUC for sepsis (AUC 0.961) and intensive care unit (ICU) admission (AUC 0.973), and eXtreme Gradient Boosting (XGBoost) showing the highest AUC for mortality (AUC 0.975). Compared to BISAP, the AI model had superior AUC for sepsis (BISAP 0.785), ICU admission (BISAP 0.778), and mortality (BISAP 0.817). CONCLUSIONS The first real-time AI prediction model implemented in the HIS for predicting adverse outcomes in ED patients with AP shows favorable initial results. However, further external validation is needed to ensure its reliability and accuracy.
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Affiliation(s)
- Ching-Hung Chang
- Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chia-Jung Chen
- Department of Information Systems, Chi Mei Medical Center, Tainan, Taiwan
| | - Yu-Shan Ma
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
| | - Yu-Ting Shen
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
| | - Mei-I Sung
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
| | - Chien-Chin Hsu
- Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan
| | - Hung-Jung Lin
- Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan
- Department of Emergency Medicine, Taipei Medical University, Taipei, Taiwan
| | - Zhih-Cherng Chen
- School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan
- Division of Cardiology, Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chien-Cheng Huang
- Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan
- Department of Emergency Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
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7
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Grauman Å, Ancillotti M, Veldwijk J, Mascalzoni D. Precision cancer medicine and the doctor-patient relationship: a systematic review and narrative synthesis. BMC Med Inform Decis Mak 2023; 23:286. [PMID: 38098034 PMCID: PMC10722840 DOI: 10.1186/s12911-023-02395-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 12/06/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND The implementation of precision medicine is likely to have a huge impact on clinical cancer care, while the doctor-patient relationship is a crucial aspect of cancer care that needs to be preserved. This systematic review aimed to map out perceptions and concerns regarding how the implementation of precision medicine will impact the doctor-patient relationship in cancer care so that threats against the doctor-patient relationship can be addressed. METHODS Electronic databases (Pubmed, Scopus, Web of Science, Social Science Premium Collection) were searched for articles published from January 2010 to December 2021, including qualitative, quantitative, and theoretical methods. Two reviewers completed title and abstract screening, full-text screening, and data extraction. Findings were summarized and explained using narrative synthesis. RESULTS Four themes were generated from the included articles (n = 35). Providing information addresses issues of information transmission and needs, and of complex concepts such as genetics and uncertainty. Making decisions in a trustful relationship addresses opacity issues, the role of trust, and and physicians' attitude towards the role of precision medicine tools in decision-making. Managing negative reactions of non-eligible patients addresses patients' unmet expectations of precision medicine. Conflicting roles in the blurry line between clinic and research addresses issues stemming from physicians' double role as doctors and researchers. CONCLUSIONS Many findings have previously been addressed in doctor-patient communication and clinical genetics. However, precision medicine adds complexity to these fields and further emphasizes the importance of clear communication on specific themes like the distinction between genomic and gene expression and patients' expectations about access, eligibility, effectiveness, and side effects of targeted therapies.
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Affiliation(s)
- Å Grauman
- Centre for Research Ethics and Bioethics, Uppsala University, Box 564, Uppsala, SE-751 22, Sweden.
| | - M Ancillotti
- Centre for Research Ethics and Bioethics, Uppsala University, Box 564, Uppsala, SE-751 22, Sweden
| | - J Veldwijk
- Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, the Netherlands
- Erasmus Choice Modelling Centre, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - D Mascalzoni
- Centre for Research Ethics and Bioethics, Uppsala University, Box 564, Uppsala, SE-751 22, Sweden
- Erasmus Choice Modelling Centre, Erasmus University Rotterdam, Rotterdam, the Netherlands
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8
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McCradden M, Hui K, Buchman DZ. Evidence, ethics and the promise of artificial intelligence in psychiatry. JOURNAL OF MEDICAL ETHICS 2023; 49:573-579. [PMID: 36581457 PMCID: PMC10423547 DOI: 10.1136/jme-2022-108447] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 11/29/2022] [Indexed: 05/20/2023]
Abstract
Researchers are studying how artificial intelligence (AI) can be used to better detect, prognosticate and subgroup diseases. The idea that AI might advance medicine's understanding of biological categories of psychiatric disorders, as well as provide better treatments, is appealing given the historical challenges with prediction, diagnosis and treatment in psychiatry. Given the power of AI to analyse vast amounts of information, some clinicians may feel obligated to align their clinical judgements with the outputs of the AI system. However, a potential epistemic privileging of AI in clinical judgements may lead to unintended consequences that could negatively affect patient treatment, well-being and rights. The implications are also relevant to precision medicine, digital twin technologies and predictive analytics generally. We propose that a commitment to epistemic humility can help promote judicious clinical decision-making at the interface of big data and AI in psychiatry.
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Affiliation(s)
- Melissa McCradden
- Joint Centre for Bioethics, University of Toronto Dalla Lana School of Public Health, Toronto, Ontario, Canada
- Bioethics, The Hospital for Sick Children, Toronto, Ontario, Canada
- Genetics & Genome Biology, Peter Gilgan Centre for Research and Learning, Toronto, Ontario, Canada
| | - Katrina Hui
- Everyday Ethics Lab, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Daniel Z Buchman
- Joint Centre for Bioethics, University of Toronto Dalla Lana School of Public Health, Toronto, Ontario, Canada
- Everyday Ethics Lab, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
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9
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Holohan M, Buyx A, Fiske A. Staying Curious With Conversational AI in Psychotherapy. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2023; 23:14-16. [PMID: 37130403 DOI: 10.1080/15265161.2023.2191059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
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10
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Stroud AM, Pacyna JE, Sharp RR. Ethical Aspects of Machine Listening in Healthcare. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2023; 23:1-3. [PMID: 37130383 DOI: 10.1080/15265161.2023.2199646] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
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11
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Sauerbrei A, Kerasidou A, Lucivero F, Hallowell N. The impact of artificial intelligence on the person-centred, doctor-patient relationship: some problems and solutions. BMC Med Inform Decis Mak 2023; 23:73. [PMID: 37081503 PMCID: PMC10116477 DOI: 10.1186/s12911-023-02162-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 03/29/2023] [Indexed: 04/22/2023] Open
Abstract
Artificial intelligence (AI) is often cited as a possible solution to current issues faced by healthcare systems. This includes the freeing up of time for doctors and facilitating person-centred doctor-patient relationships. However, given the novelty of artificial intelligence tools, there is very little concrete evidence on their impact on the doctor-patient relationship or on how to ensure that they are implemented in a way which is beneficial for person-centred care.Given the importance of empathy and compassion in the practice of person-centred care, we conducted a literature review to explore how AI impacts these two values. Besides empathy and compassion, shared decision-making, and trust relationships emerged as key values in the reviewed papers. We identified two concrete ways which can help ensure that the use of AI tools have a positive impact on person-centred doctor-patient relationships. These are (1) using AI tools in an assistive role and (2) adapting medical education. The study suggests that we need to take intentional steps in order to ensure that the deployment of AI tools in healthcare has a positive impact on person-centred doctor-patient relationships. We argue that the proposed solutions are contingent upon clarifying the values underlying future healthcare systems.
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Affiliation(s)
- Aurelia Sauerbrei
- Ethox Centre, Nuffield Department of Population Health, University of Oxford, Big Data Institute, Old Road Campus, Oxford, OX3 7LF, UK.
| | - Angeliki Kerasidou
- Ethox Centre, Nuffield Department of Population Health, University of Oxford, Big Data Institute, Old Road Campus, Oxford, OX3 7LF, UK
| | - Federica Lucivero
- Ethox Centre, Nuffield Department of Population Health, University of Oxford, Big Data Institute, Old Road Campus, Oxford, OX3 7LF, UK
| | - Nina Hallowell
- Ethox Centre, Nuffield Department of Population Health, University of Oxford, Big Data Institute, Old Road Campus, Oxford, OX3 7LF, UK
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Prelaj A, Ganzinelli M, Trovo' F, Roisman LC, Pedrocchi ALG, Kosta S, Restelli M, Ambrosini E, Broggini M, Pravettoni G, Monzani D, Nuara A, Amat R, Spathas N, Willis M, Pearson A, Dolezal J, Mazzeo L, Sangaletti S, Correa AM, Aguaron A, Watermann I, Popa C, Raimondi G, Triulzi T, Steurer S, Lo Russo G, Linardou H, Peled N, Felip E, Reck M, Garassino MC. The EU-funded I 3LUNG Project: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care With Immunotherapy. Clin Lung Cancer 2023; 24:381-387. [PMID: 36959048 DOI: 10.1016/j.cllc.2023.02.005] [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: 09/29/2022] [Revised: 02/15/2023] [Accepted: 02/15/2023] [Indexed: 03/25/2023]
Abstract
Although immunotherapy (IO) has changed the paradigm for the treatment of patients with advanced non-small cell lung cancers (aNSCLC), only around 30% to 50% of treated patients experience a long-term benefit from IO. Furthermore, the identification of the 30 to 50% of patients who respond remains a major challenge, as programmed Death-Ligand 1 (PD-L1) is currently the only biomarker used to predict the outcome of IO in NSCLC patients despite its limited efficacy. Considering the dynamic complexity of the immune system-tumor microenvironment (TME) and its interaction with the host's and patient's behavior, it is unlikely that a single biomarker will accurately predict a patient's outcomes. In this scenario, Artificial Intelligence (AI) and Machine Learning (ML) are becoming essential to the development of powerful decision-making tools that are able to deal with this high-complexity and provide individualized predictions to better match treatments to individual patients and thus improve patient outcomes and reduce the economic burden of aNSCLC on healthcare systems. I3LUNG is an international, multicenter, retrospective and prospective, observational study of patients with aNSCLC treated with IO, entirely funded by European Union (EU) under the Horizon 2020 (H2020) program. Using AI-based tools, the aim of this study is to promote individualized treatment in aNSCLC, with the goals of improving survival and quality of life, minimizing or preventing undue toxicity and promoting efficient resource allocation. The final objective of the project is the construction of a novel, integrated, AI-assisted data storage and elaboration platform to guide IO administration in aNSCLC, ensuring easy access and cost-effective use by healthcare providers and patients.
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Affiliation(s)
- Arsela Prelaj
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), 20133 Milan, Italy; Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy.
| | - Monica Ganzinelli
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), 20133 Milan, Italy
| | - Francesco Trovo'
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Laila C Roisman
- Oncology Division, Shaare Zedek Medical Center, Jerusalem, Israel
| | | | - Sokol Kosta
- Department of Electronic Systems, Aalborg University, Copenhagen, Denmark
| | - Marcello Restelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Emilia Ambrosini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Massimo Broggini
- Laboratory of Molecular Pharmacology, Istituto di Ricerche Farmacologiche Mario Negri - IRCCS, Milan, Italy
| | - Gabriella Pravettoni
- Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Haemato-Oncology, University of Milano, Milan, Italy; Department of Oncology and Haemato-Oncology, University of Milano, Milan, Italy
| | - Dario Monzani
- Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Haemato-Oncology, University of Milano, Milan, Italy; Department of Psychology, Educational Science and Human Movement (SPPEFF), University of Palermo, Italy
| | | | - Ramon Amat
- Thoracic Cancers Translational Genomics Unit, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Nikos Spathas
- 4th Oncology Department & Comprehensive Clinical Trials Center, Metropolitan Hospital, Athens, Greece (MH)
| | - Michael Willis
- The Swedish Institute for Health Economics, Lund, Sweden
| | - Alexander Pearson
- Department of Medicine, Section of Hematology and Oncology, University of Chicago, Chicago, IL, USA
| | - James Dolezal
- Department of Medicine, Section of Hematology and Oncology, University of Chicago, Chicago, IL, USA
| | - Laura Mazzeo
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), 20133 Milan, Italy
| | - Sabina Sangaletti
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), 20133 Milan, Italy
| | - Ana Maria Correa
- Research Unit KU Leuven Centre for IT & IP Law (CiTiP). Leuven, Belgium
| | | | - Iris Watermann
- LungenClinic Grosshansdorf (GHD), Airway Research Center North (ARCN), German Center for Lung Research (DZL), Großhansdorf, Deutschland
| | - Crina Popa
- Medica Scientia Innovation Research, Barcelona, Spain
| | | | - Tiziana Triulzi
- Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
| | - Stefan Steurer
- Institute for Pathology, University Medical Center Hamburg-Eppendorf, Hamburg Germany
| | - Giuseppe Lo Russo
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), 20133 Milan, Italy
| | - Helena Linardou
- 4th Oncology Department & Comprehensive Clinical Trials Center, Metropolitan Hospital, Athens, Greece (MH)
| | - Nir Peled
- Oncology Division, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Enriqueta Felip
- Thoracic Cancers Translational Genomics Unit, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Martin Reck
- LungenClinic Grosshansdorf (GHD), Airway Research Center North (ARCN), German Center for Lung Research (DZL), Großhansdorf, Deutschland
| | - Marina Chiara Garassino
- Department of Medicine, Section of Hematology and Oncology, University of Chicago, Chicago, IL, USA
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Gabelloni M, Faggioni L, Fusco R, De Muzio F, Danti G, Grassi F, Grassi R, Palumbo P, Bruno F, Borgheresi A, Bruno A, Catalano O, Gandolfo N, Giovagnoni A, Miele V, Barile A, Granata V. Exploring Radiologists' Burnout in the COVID-19 Era: A Narrative Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3350. [PMID: 36834044 PMCID: PMC9966123 DOI: 10.3390/ijerph20043350] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/03/2023] [Accepted: 02/12/2023] [Indexed: 06/18/2023]
Abstract
Since its beginning in March 2020, the COVID-19 pandemic has claimed an exceptionally high number of victims and brought significant disruption to the personal and professional lives of millions of people worldwide. Among medical specialists, radiologists have found themselves at the forefront of the crisis due to the pivotal role of imaging in the diagnostic and interventional management of COVID-19 pneumonia and its complications. Because of the disruptive changes related to the COVID-19 outbreak, a proportion of radiologists have faced burnout to several degrees, resulting in detrimental effects on their working activities and overall wellbeing. This paper aims to provide an overview of the literature exploring the issue of radiologists' burnout in the COVID-19 era.
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Affiliation(s)
- Michela Gabelloni
- Nuclear Medicine Unit, Department of Translational Research, University of Pisa, 56126 Pisa, Italy
| | - Lorenzo Faggioni
- Academic Radiology, Department of Translational Research, University of Pisa, 56126 Pisa, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy
| | - Ginevra Danti
- Department of Emergency Radiology, Careggi University Hospital, 50134 Florence, Italy
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, 20122 Milan, Italy
| | - Francesca Grassi
- Department of Precision Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Roberta Grassi
- Department of Precision Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Pierpaolo Palumbo
- Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, Department of Diagnostic Imaging, 67100 L’Aquila, Italy
| | - Federico Bruno
- Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, Department of Diagnostic Imaging, 67100 L’Aquila, Italy
| | - Alessandra Borgheresi
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, Università Politecnica delle Marche, 60126 Ancona, Italy
| | - Alessandra Bruno
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, Università Politecnica delle Marche, 60126 Ancona, Italy
| | - Orlando Catalano
- Department of Radiology, Istituto Diagnostico Varelli, 80126 Naples, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, 16149 Genoa, Italy
| | - Andrea Giovagnoni
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, Università Politecnica delle Marche, 60126 Ancona, Italy
| | - Vittorio Miele
- Department of Emergency Radiology, Careggi University Hospital, 50134 Florence, Italy
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, 20122 Milan, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
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14
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Prelaj A, Galli EG, Miskovic V, Pesenti M, Viscardi G, Pedica B, Mazzeo L, Bottiglieri A, Provenzano L, Spagnoletti A, Marinacci R, De Toma A, Proto C, Ferrara R, Brambilla M, Occhipinti M, Manglaviti S, Galli G, Signorelli D, Giani C, Beninato T, Pircher CC, Rametta A, Kosta S, Zanitti M, Di Mauro MR, Rinaldi A, Di Gregorio S, Antonia M, Garassino MC, de Braud FGM, Restelli M, Lo Russo G, Ganzinelli M, Trovò F, Pedrocchi ALG. Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients. Front Oncol 2023; 12:1078822. [PMID: 36755856 PMCID: PMC9899835 DOI: 10.3389/fonc.2022.1078822] [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: 10/24/2022] [Accepted: 12/14/2022] [Indexed: 01/24/2023] Open
Abstract
Introduction Artificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. Methods We prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. Results Of 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. Conclusions In this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients.
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Affiliation(s)
- Arsela Prelaj
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy,*Correspondence: Arsela Prelaj,
| | - Edoardo Gregorio Galli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Vanja Miskovic
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Mattia Pesenti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Giuseppe Viscardi
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Medical Oncology Unit, Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Benedetta Pedica
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Laura Mazzeo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Achille Bottiglieri
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Leonardo Provenzano
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Andrea Spagnoletti
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Roberto Marinacci
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Alessandro De Toma
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Claudia Proto
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Roberto Ferrara
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Marta Brambilla
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Mario Occhipinti
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Sara Manglaviti
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Giulia Galli
- Medical Oncology Unit, Policlinico San Matteo Fondazione IRCCS, Pavia, Italy
| | - Diego Signorelli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Claudia Giani
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Teresa Beninato
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Chiara Carlotta Pircher
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Alessandro Rametta
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Sokol Kosta
- Department of Electronic System, Aalborg University, Copenhagen, Aalborg, Denmark
| | - Michele Zanitti
- Department of Electronic System, Aalborg University, Copenhagen, Aalborg, Denmark
| | - Maria Rosa Di Mauro
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Arturo Rinaldi
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Settimio Di Gregorio
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Martinetti Antonia
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Marina Chiara Garassino
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Thoracic Oncology Program, Section of Hematology/Oncology, University of Chicago, Chicago, IL, United States
| | - Filippo G. M. de Braud
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Marcello Restelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Giuseppe Lo Russo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Monica Ganzinelli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Francesco Trovò
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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Derevianko A, Pizzoli SFM, Pesapane F, Rotili A, Monzani D, Grasso R, Cassano E, Pravettoni G. The Use of Artificial Intelligence (AI) in the Radiology Field: What Is the State of Doctor-Patient Communication in Cancer Diagnosis? Cancers (Basel) 2023; 15:cancers15020470. [PMID: 36672417 PMCID: PMC9856827 DOI: 10.3390/cancers15020470] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/04/2023] [Accepted: 01/10/2023] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND In the past decade, interest in applying Artificial Intelligence (AI) in radiology to improve diagnostic procedures increased. AI has potential benefits spanning all steps of the imaging chain, from the prescription of diagnostic tests to the communication of test reports. The use of AI in the field of radiology also poses challenges in doctor-patient communication at the time of the diagnosis. This systematic review focuses on the patient role and the interpersonal skills between patients and physicians when AI is implemented in cancer diagnosis communication. METHODS A systematic search was conducted on PubMed, Embase, Medline, Scopus, and PsycNet from 1990 to 2021. The search terms were: ("artificial intelligence" or "intelligence machine") and "communication" "radiology" and "oncology diagnosis". The PRISMA guidelines were followed. RESULTS 517 records were identified, and 5 papers met the inclusion criteria and were analyzed. Most of the articles emphasized the success of the technological support of AI in radiology at the expense of patient trust in AI and patient-centered communication in cancer disease. Practical implications and future guidelines were discussed according to the results. CONCLUSIONS AI has proven to be beneficial in helping clinicians with diagnosis. Future research may improve patients' trust through adequate information about the advantageous use of AI and an increase in medical compliance with adequate training on doctor-patient diagnosis communication.
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Affiliation(s)
- Alexandra Derevianko
- Applied Research Division for Cognitive and Psychological Science, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Silvia Francesca Maria Pizzoli
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
- Correspondence: ; Tel.: +39-0294372099
| | - Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20139 Milan, Italy
| | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20139 Milan, Italy
| | - Dario Monzani
- Department of Psychology, Educational Science and Human Movement, University of Palermo, 90128 Palermo, Italy
| | - Roberto Grasso
- Applied Research Division for Cognitive and Psychological Science, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20139 Milan, Italy
| | - Gabriella Pravettoni
- Applied Research Division for Cognitive and Psychological Science, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
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16
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Calvas P. Chapitre 7. Un regard de généticien. JOURNAL INTERNATIONAL DE BIOETHIQUE ET D'ETHIQUE DES SCIENCES 2023; 34:111-120. [PMID: 37684198 DOI: 10.3917/jibes.342.0111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
Examined through the eyes of the geneticist, the modifications of the bioethics law seem relatively modest with regard to the supervision of the practices of his discipline. The introduction of rules concerning the use of algorithms in medical practice is the truly new point. It seemed beneficial to take into account “the interference of thinking machines” in medical decision-making and to initiate the outlines of a framework. We will debate the proposals and terms. Precisions made to the obligation to inform relatives of the existence of a genetic anomaly are defined around the concept of solidarity. Without neglecting this latter, we will recall other determinants, the complexity and the issues underlying the delivery of predictive genetic information as well as the risks that informed persons may incur. It seems appropriate to also consider the ethical tensions that can impose themselves on the physicians involved in the mandatory process of information..
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Mavragani A, Horstmanshof L. Human Decision-making in an Artificial Intelligence-Driven Future in Health: Protocol for Comparative Analysis and Simulation. JMIR Res Protoc 2022; 11:e42353. [PMID: 36460486 PMCID: PMC9823572 DOI: 10.2196/42353] [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: 09/01/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Health care can broadly be divided into two domains: clinical health services and complex health services (ie, nonclinical health services, eg, health policy and health regulation). Artificial intelligence (AI) is transforming both of these areas. Currently, humans are leaders, managers, and decision makers in complex health services. However, with the rise of AI, the time has come to ask whether humans will continue to have meaningful decision-making roles in this domain. Further, rationality has long dominated this space. What role will intuition play? OBJECTIVE The aim is to establish a protocol of protocols to be used in the proposed research, which aims to explore whether humans will continue in meaningful decision-making roles in complex health services in an AI-driven future. METHODS This paper describes a set of protocols for the proposed research, which is designed as a 4-step project across two phases. This paper describes the protocols for each step. The first step is a scoping review to identify and map human attributes that influence decision-making in complex health services. The research question focuses on the attributes that influence human decision-making in this context as reported in the literature. The second step is a scoping review to identify and map AI attributes that influence decision-making in complex health services. The research question focuses on attributes that influence AI decision-making in this context as reported in the literature. The third step is a comparative analysis: a narrative comparison followed by a mathematical comparison of the two sets of attributes-human and AI. This analysis will investigate whether humans have one or more unique attributes that could influence decision-making for the better. The fourth step is a simulation of a nonclinical environment in health regulation and policy into which virtual human and AI decision makers (agents) are introduced. The virtual human and AI will be based on the human and AI attributes identified in the scoping reviews. The simulation will explore, observe, and document how humans interact with AI, and whether humans are likely to compete, cooperate, or converge with AI. RESULTS The results will be presented in tabular form, visually intuitive formats, and-in the case of the simulation-multimedia formats. CONCLUSIONS This paper provides a road map for the proposed research. It also provides an example of a protocol of protocols for methods used in complex health research. While there are established guidelines for a priori protocols for scoping reviews, there is a paucity of guidance on establishing a protocol of protocols. This paper takes the first step toward building a scaffolding for future guidelines in this regard. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/42353.
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Affiliation(s)
| | - Louise Horstmanshof
- Faculty of Health, Southern Cross University, Lismore, New South Wales, Australia
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18
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Ed-Driouch C, Mars F, Gourraud PA, Dumas C. Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human-Machine Intelligence. SENSORS (BASEL, SWITZERLAND) 2022; 22:8313. [PMID: 36366011 PMCID: PMC9653746 DOI: 10.3390/s22218313] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/25/2022] [Accepted: 10/26/2022] [Indexed: 06/12/2023]
Abstract
Machine learning (ML) models have proven their potential in acquiring and analyzing large amounts of data to help solve real-world, complex problems. Their use in healthcare is expected to help physicians make diagnoses, prognoses, treatment decisions, and disease outcome predictions. However, ML solutions are not currently deployed in most healthcare systems. One of the main reasons for this is the provenance, transparency, and clinical utility of the training data. Physicians reject ML solutions if they are not at least based on accurate data and do not clearly include the decision-making process used in clinical practice. In this paper, we present a hybrid human-machine intelligence method to create predictive models driven by clinical practice. We promote the use of quality-approved data and the inclusion of physician reasoning in the ML process. Instead of training the ML algorithms on the given data to create predictive models (conventional method), we propose to pre-categorize the data according to the expert physicians' knowledge and experience. Comparing the results of the conventional method of ML learning versus the hybrid physician-algorithm method showed that the models based on the latter can perform better. Physicians' engagement is the most promising condition for the safe and innovative use of ML in healthcare.
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Affiliation(s)
- Chadia Ed-Driouch
- École Centrale Nantes, IMT Atlantique, Nantes Université, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
| | - Franck Mars
- Centrale Nantes, Nantes Université, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
| | - Pierre-Antoine Gourraud
- Clinique des Données, Pôle Hospitalo-Universitaire 11: Santé Publique, CHU Nantes, Nantes Université, INSERM, CIC 1413, F-44000 Nantes, France
| | - Cédric Dumas
- Département Automatique, Productique et Informatique, IMT Atlantique, CNRS, LS2N, UMR CNRS 6004, F-44000 Nantes, France
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Abbasgholizadeh Rahimi S, Cwintal M, Huang Y, Ghadiri P, Grad R, Poenaru D, Gore G, Zomahoun HTV, Légaré F, Pluye P. Application of Artificial Intelligence in Shared Decision Making: Scoping Review. JMIR Med Inform 2022; 10:e36199. [PMID: 35943 PMCID: PMC9399841 DOI: 10.2196/36199] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 04/16/2022] [Accepted: 04/21/2022] [Indexed: 12/04/2022] Open
Abstract
Background Artificial intelligence (AI) has shown promising results in various fields of medicine. It has the potential to facilitate shared decision making (SDM). However, there is no comprehensive mapping of how AI may be used for SDM. Objective We aimed to identify and evaluate published studies that have tested or implemented AI to facilitate SDM. Methods We performed a scoping review informed by the methodological framework proposed by Levac et al, modifications to the original Arksey and O'Malley framework of a scoping review, and the Joanna Briggs Institute scoping review framework. We reported our results based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) reporting guideline. At the identification stage, an information specialist performed a comprehensive search of 6 electronic databases from their inception to May 2021. The inclusion criteria were: all populations; all AI interventions that were used to facilitate SDM, and if the AI intervention was not used for the decision-making point in SDM, it was excluded; any outcome related to patients, health care providers, or health care systems; studies in any health care setting, only studies published in the English language, and all study types. Overall, 2 reviewers independently performed the study selection process and extracted data. Any disagreements were resolved by a third reviewer. A descriptive analysis was performed. Results The search process yielded 1445 records. After removing duplicates, 894 documents were screened, and 6 peer-reviewed publications met our inclusion criteria. Overall, 2 of them were conducted in North America, 2 in Europe, 1 in Australia, and 1 in Asia. Most articles were published after 2017. Overall, 3 articles focused on primary care, and 3 articles focused on secondary care. All studies used machine learning methods. Moreover, 3 articles included health care providers in the validation stage of the AI intervention, and 1 article included both health care providers and patients in clinical validation, but none of the articles included health care providers or patients in the design and development of the AI intervention. All used AI to support SDM by providing clinical recommendations or predictions. Conclusions Evidence of the use of AI in SDM is in its infancy. We found AI supporting SDM in similar ways across the included articles. We observed a lack of emphasis on patients’ values and preferences, as well as poor reporting of AI interventions, resulting in a lack of clarity about different aspects. Little effort was made to address the topics of explainability of AI interventions and to include end-users in the design and development of the interventions. Further efforts are required to strengthen and standardize the use of AI in different steps of SDM and to evaluate its impact on various decisions, populations, and settings.
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Affiliation(s)
- Samira Abbasgholizadeh Rahimi
- Department of Family Medicine, McGill University, Montreal, QC, Canada.,Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.,Mila-Quebec AI Institute, Montreal, QC, Canada
| | - Michelle Cwintal
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada
| | - Yuhui Huang
- Department of Integrated Studies in Education, McGill University, Montreal, QC, Canada
| | - Pooria Ghadiri
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Roland Grad
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Dan Poenaru
- Department of Pediatric Surgery, McGill University Health Centre, Montreal, QC, Canada
| | - Genevieve Gore
- Schulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Montreal, QC, Canada
| | - Hervé Tchala Vignon Zomahoun
- Centre de recherche en santé durable, Centre intégré universitaire de santé et services sociaux de la Capitale-Nationale, Quebec City, QC, Canada.,Quebec Support for People and Patient-Oriented Research and Trials Unit, Quebec City, QC, Canada
| | - France Légaré
- Centre de recherche en santé durable, Centre intégré universitaire de santé et services sociaux de la Capitale-Nationale, Quebec City, QC, Canada.,Quebec Support for People and Patient-Oriented Research and Trials Unit, Quebec City, QC, Canada.,Department of Family Medicine and Emergency Medicine, Faculty of Medicine, Université Laval, Quebec City, QC, Canada
| | - Pierre Pluye
- Department of Family Medicine, McGill University, Montreal, QC, Canada
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20
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Chiarella SG, Torromino G, Gagliardi DM, Rossi D, Babiloni F, Cartocci G. Investigating the negative bias towards artificial intelligence: Effects of prior assignment of AI-authorship on the aesthetic appreciation of abstract paintings. COMPUTERS IN HUMAN BEHAVIOR 2022. [DOI: 10.1016/j.chb.2022.107406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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21
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Terblanche N, Molyn J, de Haan E, Nilsson VO. Comparing artificial intelligence and human coaching goal attainment efficacy. PLoS One 2022; 17:e0270255. [PMID: 35727801 PMCID: PMC9212136 DOI: 10.1371/journal.pone.0270255] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 06/08/2022] [Indexed: 11/22/2022] Open
Abstract
The history of artificial intelligence (AI) is filled with hype and inflated expectations. Notwithstanding, AI is finding its way into numerous aspects of humanity including the fast-growing helping profession of coaching. Coaching has been shown to be efficacious in a variety of human development facets. The application of AI in a narrow, specific area of coaching has also been shown to work. What remains uncertain, is how the two compare. In this paper we compare two equivalent longitudinal randomised control trial studies that measured the increase in clients' goal attainment as a result of having received coaching over a 10-month period. The first study involved human coaches and the replication study used an AI chatbot coach. In both studies, human coaches and the AI coach were significantly more effective in helping clients reach their goals compared to the two control groups. Surprisingly however, the AI coach was as effective as human coaches at the end of the trials. We interpret this result using AI and goal theory and present three significant implications: AI coaching could be scaled to democratize coaching; AI coaching could grow the demand for human coaching; and AI could replace human coaches who use simplistic, model-based coaching approaches. At present, AI's lack of empathy and emotional intelligence make human coaches irreplicable. However, understanding the efficacy of AI coaching relative to human coaching may promote the focused use of AI, to the significant benefit of society.
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Affiliation(s)
- Nicky Terblanche
- University of Stellenbosch Business School, Cape Town, South Africa
| | - Joanna Molyn
- University of Oxford Brookes, Oxford, United Kingdom
| | - Erik de Haan
- Ashridge Centre for Coaching, Hult International Business School, Berkhamsted (Herts.), United Kingdom
- VU University Amsterdam, Amsterdam, The Netherlands
| | - Viktor O. Nilsson
- Ashridge Centre for Coaching, Hult International Business School, Berkhamsted (Herts.), United Kingdom
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Čartolovni A, Tomičić A, Lazić Mosler E. Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review. Int J Med Inform 2022; 161:104738. [PMID: 35299098 DOI: 10.1016/j.ijmedinf.2022.104738] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 02/11/2022] [Accepted: 03/10/2022] [Indexed: 10/18/2022]
Abstract
INTRODUCTION Recent developments in the field of Artificial Intelligence (AI) applied to healthcare promise to solve many of the existing global issues in advancing human health and managing global health challenges. This comprehensive review aims not only to surface the underlying ethical and legal but also social implications (ELSI) that have been overlooked in recent reviews while deserving equal attention in the development stage, and certainly ahead of implementation in healthcare. It is intended to guide various stakeholders (eg. designers, engineers, clinicians) in addressing the ELSI of AI at the design stage using the Ethics by Design (EbD) approach. METHODS The authors followed a systematised scoping methodology and searched the following databases: Pubmed, Web of science, Ovid, Scopus, IEEE Xplore, EBSCO Search (Academic Search Premier, CINAHL, PSYCINFO, APA PsycArticles, ERIC) for the ELSI of AI in healthcare through January 2021. Data were charted and synthesised, and the authors conducted a descriptive and thematic analysis of the collected data. RESULTS After reviewing 1108 papers, 94 were included in the final analysis. Our results show a growing interest in the academic community for ELSI in the field of AI. The main issues of concern identified in our analysis fall into four main clusters of impact: AI algorithms, physicians, patients, and healthcare in general. The most prevalent issues are patient safety, algorithmic transparency, lack of proper regulation, liability & accountability, impact on patient-physician relationship and governance of AI empowered healthcare. CONCLUSIONS The results of our review confirm the potential of AI to significantly improve patient care, but the drawbacks to its implementation relate to complex ELSI that have yet to be addressed. Most ELSI refer to the impact on and extension of the reciprocal and fiduciary patient-physician relationship. With the integration of AIbased decision making tools, a bilateral patient-physician relationship may shift into a trilateral one.
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Affiliation(s)
- Anto Čartolovni
- Digital Healthcare Ethics Laboratory (Digit-HeaL), Catholic University of Croatia, Ilica 242, 10 000 Zagreb, Croatia; School of Medicine, Catholic University of Croatia, Ilica 242, 10 000 Zagreb, Croatia.
| | - Ana Tomičić
- Digital Healthcare Ethics Laboratory (Digit-HeaL), Catholic University of Croatia, Ilica 242, 10 000 Zagreb, Croatia.
| | - Elvira Lazić Mosler
- School of Medicine, Catholic University of Croatia, Ilica 242, 10 000 Zagreb, Croatia; General Hospital Dr. Ivo Pedišić, Sisak, Croatia.
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Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection. Cancers (Basel) 2022; 14:cancers14030606. [PMID: 35158874 PMCID: PMC8833500 DOI: 10.3390/cancers14030606] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/20/2022] [Accepted: 01/24/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Multiple myeloma is a malignant neoplasm of plasma cells with complex pathogenesis. With major progresses in multiple myeloma research, it is essential that we reconsider our methods for diagnosing and monitoring multiple myeloma disease. This fact needs the integration of serology, histology, radiology, and genetic data; therefore, multiple myeloma study has generated massive quantities of granular high-dimensional data exceeding human understanding. With improved computational techniques, artificial intelligence tools for data processing and analysis are becoming more and more relevant. Artificial intelligence represents a wide set of algorithms for which machine learning and deep learning are presently among the most impactful. This review focuses on artificial intelligence applications in multiple myeloma research, first illustrating machine learning and deep learning procedures and workflow, followed by how these algorithms are used for multiple myeloma diagnosis, prognosis, bone lesions identification, and evaluation of response to the treatment. Abstract Artificial intelligence has recently modified the panorama of oncology investigation thanks to the use of machine learning algorithms and deep learning strategies. Machine learning is a branch of artificial intelligence that involves algorithms that analyse information, learn from that information, and then employ their discoveries to make abreast choice, while deep learning is a field of machine learning basically represented by algorithms inspired by the organization and function of the brain, named artificial neural networks. In this review, we examine the possibility of the artificial intelligence applications in multiple myeloma evaluation, and we report the most significant experimentations with respect to the machine and deep learning procedures in the relevant field. Multiple myeloma is one of the most common haematological malignancies in the world, and among them, it is one of the most difficult ones to cure due to the high occurrence of relapse and chemoresistance. Machine learning- and deep learning-based studies are expected to be among the future strategies to challenge this negative-prognosis tumour via the detection of new markers for their prompt discovery and therapy selection and by a better evaluation of its relapse and survival.
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Prelaj A, Boeri M, Robuschi A, Ferrara R, Proto C, Lo Russo G, Galli G, De Toma A, Brambilla M, Occhipinti M, Manglaviti S, Beninato T, Bottiglieri A, Massa G, Zattarin E, Gallucci R, Galli EG, Ganzinelli M, Sozzi G, de Braud FGM, Garassino MC, Restelli M, Pedrocchi ALG, Trovo' F. Machine Learning Using Real-World and Translational Data to Improve Treatment Selection for NSCLC Patients Treated with Immunotherapy. Cancers (Basel) 2022; 14:cancers14020435. [PMID: 35053597 PMCID: PMC8773718 DOI: 10.3390/cancers14020435] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 01/05/2022] [Accepted: 01/12/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary In this paper, the authors show that artificial intelligence (AI) and machine learning (ML) are useful approaches to integrate multifactorial data and helpful for personalized prediction. In detail, compared to PD-L1 for advanced non-small cell lung cancer (NSCLC), ML tools predicted better responder (R) and non-responder (NR) patients to immunotherapy (IO). It was also able to indirectly foresee OS and PFS of R and NR patients. Given the high incidence of NSCLC, and the absence of reliable biomarkers to predict the response to IO other than PD-L1, the authors believe this research may be of great interest to anyone involved in thoracic oncology. Furthermore, given the growing interest from the scientific community in AI and ML, the authors believe that this manuscript could represent a fascinating topic to anyone who needs to exploit the enormous potential of these tools in the treatment of cancer. Abstract (1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO.
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Affiliation(s)
- Arsela Prelaj
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (A.R.); (M.R.); (A.L.G.P.); (F.T.)
- Correspondence:
| | - Mattia Boeri
- Tumor Genomics Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (M.B.); (G.S.)
| | - Alessandro Robuschi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (A.R.); (M.R.); (A.L.G.P.); (F.T.)
| | - Roberto Ferrara
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Claudia Proto
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Giuseppe Lo Russo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Giulia Galli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Alessandro De Toma
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Marta Brambilla
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Mario Occhipinti
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Sara Manglaviti
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Teresa Beninato
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Achille Bottiglieri
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Giacomo Massa
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Emma Zattarin
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Rosaria Gallucci
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Edoardo Gregorio Galli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Monica Ganzinelli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Gabriella Sozzi
- Tumor Genomics Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (M.B.); (G.S.)
| | - Filippo G. M. de Braud
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Marina Chiara Garassino
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (R.F.); (C.P.); (G.L.R.); (G.G.); (A.D.T.); (M.B.); (M.O.); (S.M.); (T.B.); (A.B.); (G.M.); (E.Z.); (R.G.); (E.G.G.); (M.G.); (F.G.M.d.B.); (M.C.G.)
| | - Marcello Restelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (A.R.); (M.R.); (A.L.G.P.); (F.T.)
| | - Alessandra Laura Giulia Pedrocchi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (A.R.); (M.R.); (A.L.G.P.); (F.T.)
| | - Francesco Trovo'
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (A.R.); (M.R.); (A.L.G.P.); (F.T.)
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Triberti S, Durosini I, Lin J, La Torre D, Ruiz Galán M. Editorial: On the "Human" in Human-Artificial Intelligence Interaction. Front Psychol 2022; 12:808995. [PMID: 35002900 PMCID: PMC8738165 DOI: 10.3389/fpsyg.2021.808995] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 12/06/2021] [Indexed: 12/14/2022] Open
Affiliation(s)
- Stefano Triberti
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.,Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Ilaria Durosini
- Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Jianyi Lin
- Dipartimento di Scienze Statistiche, Università Cattolica del Sacro Cuore, Milan, Italy.,Department of Mathematics, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Davide La Torre
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.,SKEMA Business School and Université Cote d'Azur, Sophia Antipolis Campus, Sophia Antipolis, France
| | - Manuel Ruiz Galán
- Department of Applied Mathematics, University of Granada, Granada, Spain
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26
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Holohan M, Fiske A. "Like I'm Talking to a Real Person": Exploring the Meaning of Transference for the Use and Design of AI-Based Applications in Psychotherapy. Front Psychol 2021; 12:720476. [PMID: 34646209 PMCID: PMC8502869 DOI: 10.3389/fpsyg.2021.720476] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 08/16/2021] [Indexed: 11/18/2022] Open
Abstract
AI-enabled virtual and robot therapy is increasingly being integrated into psychotherapeutic practice, supporting a host of emotional, cognitive, and social processes in the therapeutic encounter. Given the speed of research and development trajectories of AI-enabled applications in psychotherapy and the practice of mental healthcare, it is likely that therapeutic chatbots, avatars, and socially assistive devices will soon translate into clinical applications much more broadly. While AI applications offer many potential opportunities for psychotherapy, they also raise important ethical, social, and clinical questions that have not yet been adequately considered for clinical practice. In this article, we begin to address one of these considerations: the role of transference in the psychotherapeutic relationship. Drawing on Karen Barad’s conceptual approach to theorizing human–non-human relations, we show that the concept of transference is necessarily reconfigured within AI-human psychotherapeutic encounters. This has implications for understanding how AI-driven technologies introduce changes in the field of traditional psychotherapy and other forms of mental healthcare and how this may change clinical psychotherapeutic practice and AI development alike. As more AI-enabled apps and platforms for psychotherapy are developed, it becomes necessary to re-think AI-human interaction as more nuanced and richer than a simple exchange of information between human and nonhuman actors alone.
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Affiliation(s)
- Michael Holohan
- Institute of History and Ethics in Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Amelia Fiske
- Institute of History and Ethics in Medicine, School of Medicine, Technical University of Munich, Munich, Germany
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Coppola F, Faggioni L, Gabelloni M, De Vietro F, Mendola V, Cattabriga A, Cocozza MA, Vara G, Piccinino A, Lo Monaco S, Pastore LV, Mottola M, Malavasi S, Bevilacqua A, Neri E, Golfieri R. Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging. Front Psychol 2021; 12:710982. [PMID: 34650476 PMCID: PMC8505993 DOI: 10.3389/fpsyg.2021.710982] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/02/2021] [Indexed: 12/22/2022] Open
Abstract
Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.
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Affiliation(s)
- Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, Milan, Italy
| | - Lorenzo Faggioni
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Fabrizio De Vietro
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Vincenzo Mendola
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Arrigo Cattabriga
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Maria Adriana Cocozza
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Giulio Vara
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Alberto Piccinino
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Silvia Lo Monaco
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Luigi Vincenzo Pastore
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Margherita Mottola
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Silvia Malavasi
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Alessandro Bevilacqua
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Emanuele Neri
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, Milan, Italy
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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Talamo A, Marocco S, Tricol C. "The Flow in the Funnel": Modeling Organizational and Individual Decision-Making for Designing Financial AI-Based Systems. Front Psychol 2021; 12:697101. [PMID: 34381402 PMCID: PMC8350770 DOI: 10.3389/fpsyg.2021.697101] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 06/29/2021] [Indexed: 12/29/2022] Open
Abstract
Nowadays, the current application of artificial intelligence (AI) to financial context is opening a new field of study, named financial intelligence, in which the implementation of AI-based solutions as "financial brain" aims at assisting in complex decision-making (DM) processes as wealth and risk management, financial security, financial consulting, and blockchain. For venture capitalist organizations (VCOs), this aspect becomes even more critical, since different actors (shareholders, bondholders, management, suppliers, customers) with different DM behaviors are involved. One last layer of complexity is the potential variation of behaviors performed by managers even in presence of fixed organizational goals. The aim of this study is twofold: a general analysis of the debate on implementing AI in DM processes is introduced, and a proposal for modeling financial AI-based services is presented. A set of qualitative methods based on the application of cultural psychology is presented for modeling financial DM processes of all actors involved in the process, machines as well as individuals and organizations. The integration of some design thinking techniques with strategic organizational counseling supports the modeling of a hierarchy of selective criteria of fund-seekers and the creation of an innovative value proposition accordingly with goals of VCOs to be represented and supported in AI-based systems. Implications suggest that human/AI integration in the field can be implemented by developing systems where AI can be conceived in two distinct functions: (a) automation: treating Big Data from the market defined by management of VCO; and (b) support: creating alert systems that are coherent with ordered weighted decisional criteria of VCO.
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Affiliation(s)
- Alessandra Talamo
- Department of Social and Developmental Psychology, Sapienza University of Rome, Rome, Italy
| | - Silvia Marocco
- Department of Social and Developmental Psychology, Sapienza University of Rome, Rome, Italy
| | - Chiara Tricol
- Department of Social and Developmental Psychology, Sapienza University of Rome, Rome, Italy
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Copp CJ, Cabell JJ, Kemmelmeier M. Plenty of blame to go around: Attributions of responsibility in a fatal autonomous vehicle accident. CURRENT PSYCHOLOGY 2021. [DOI: 10.1007/s12144-021-01956-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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