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Farah L, Borget I, Martelli N, Vallee A. Suitability of the Current Health Technology Assessment of Innovative Artificial Intelligence-Based Medical Devices: Scoping Literature Review. J Med Internet Res 2024; 26:e51514. [PMID: 38739911 PMCID: PMC11130781 DOI: 10.2196/51514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/17/2023] [Accepted: 12/28/2023] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI)-based medical devices have garnered attention due to their ability to revolutionize medicine. Their health technology assessment framework is lacking. OBJECTIVE This study aims to analyze the suitability of each health technology assessment (HTA) domain for the assessment of AI-based medical devices. METHODS We conducted a scoping literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We searched databases (PubMed, Embase, and Cochrane Library), gray literature, and HTA agency websites. RESULTS A total of 10.1% (78/775) of the references were included. Data quality and integration are vital aspects to consider when describing and assessing the technical characteristics of AI-based medical devices during an HTA process. When it comes to implementing specialized HTA for AI-based medical devices, several practical challenges and potential barriers could be highlighted and should be taken into account (AI technological evolution timeline, data requirements, complexity and transparency, clinical validation and safety requirements, regulatory and ethical considerations, and economic evaluation). CONCLUSIONS The adaptation of the HTA process through a methodological framework for AI-based medical devices enhances the comparability of results across different evaluations and jurisdictions. By defining the necessary expertise, the framework supports the development of a skilled workforce capable of conducting robust and reliable HTAs of AI-based medical devices. A comprehensive adapted HTA framework for AI-based medical devices can provide valuable insights into the effectiveness, cost-effectiveness, and societal impact of AI-based medical devices, guiding their responsible implementation and maximizing their benefits for patients and health care systems.
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Affiliation(s)
- Line Farah
- Innovation Center for Medical Devices Department, Foch Hospital, Suresnes, France
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France
| | - Isabelle Borget
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France
- Department of Biostatistics and Epidemiology, Gustave Roussy, University Paris-Saclay, Villejuif, France
- Oncostat U1018, Inserm, Équipe Labellisée Ligue Contre le Cancer, University Paris-Saclay, Villejuif, France
| | - Nicolas Martelli
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France
- Pharmacy Department, Georges Pompidou European Hospital, Paris, France
| | - Alexandre Vallee
- Department of Epidemiology and Public Health, Foch Hospital, Suresnes, France
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Cecchi R, Haja TM, Calabrò F, Fasterholdt I, Rasmussen BSB. Artificial intelligence in healthcare: why not apply the medico-legal method starting with the Collingridge dilemma? Int J Legal Med 2024; 138:1173-1178. [PMID: 38172326 DOI: 10.1007/s00414-023-03152-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 12/15/2023] [Indexed: 01/05/2024]
Abstract
Technology has greatly influenced and radically changed human life, from communication to creativity and from productivity to entertainment. The authors, starting from considerations concerning the implementation of new technologies with a strong impact on people's everyday lives, take up Collingridge's dilemma and relate it to the application of AI in healthcare. Collingridge's dilemma is an ethical and epistemological problem concerning the relationship between technology and society which involves two approaches. The proactive approach and socio-technological experimentation taken into account in the dilemma are discussed, the former taking health technology assessment (HTA) processes as a reference and the latter the AI studies conducted so far. As a possible prevention of the critical issues raised, the use of the medico-legal method is proposed, which classically lies between the prevention of possible adverse events and the reconstruction of how these occurred.The authors believe that this methodology, adopted as a European guideline in the medico-legal field for the assessment of medical liability, can be adapted to AI applied to the healthcare scenario and used for the assessment of liability issues. The topic deserves further investigation and will certainly be taken into consideration as a possible key to future scenarios.
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Affiliation(s)
- Rossana Cecchi
- Laboratory of Forensic Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy.
| | - Tudor Mihai Haja
- Laboratory of Forensic Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Francesco Calabrò
- Laboratory of Forensic Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Iben Fasterholdt
- CIMT - Centre for Innovative Medical Technology, Odense University Hospital, Odense, Denmark
- Program for Health System and Technology Evaluation, Toronto General Hospital Research Institute, University Health Network, Toronto, Canada
| | - Benjamin S B Rasmussen
- Department of Radiology & CAI-X - Centre for Clinical Artificial Intelligence, Odense University Hospital, Odense, Denmark
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Reason T, Rawlinson W, Langham J, Gimblett A, Malcolm B, Klijn S. Artificial Intelligence to Automate Health Economic Modelling: A Case Study to Evaluate the Potential Application of Large Language Models. PHARMACOECONOMICS - OPEN 2024; 8:191-203. [PMID: 38340276 PMCID: PMC10884386 DOI: 10.1007/s41669-024-00477-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/01/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Current generation large language models (LLMs) such as Generative Pre-Trained Transformer 4 (GPT-4) have achieved human-level performance on many tasks including the generation of computer code based on textual input. This study aimed to assess whether GPT-4 could be used to automatically programme two published health economic analyses. METHODS The two analyses were partitioned survival models evaluating interventions in non-small cell lung cancer (NSCLC) and renal cell carcinoma (RCC). We developed prompts which instructed GPT-4 to programme the NSCLC and RCC models in R, and which provided descriptions of each model's methods, assumptions and parameter values. The results of the generated scripts were compared to the published values from the original, human-programmed models. The models were replicated 15 times to capture variability in GPT-4's output. RESULTS GPT-4 fully replicated the NSCLC model with high accuracy: 100% (15/15) of the artificial intelligence (AI)-generated NSCLC models were error-free or contained a single minor error, and 93% (14/15) were completely error-free. GPT-4 closely replicated the RCC model, although human intervention was required to simplify an element of the model design (one of the model's fifteen input calculations) because it used too many sequential steps to be implemented in a single prompt. With this simplification, 87% (13/15) of the AI-generated RCC models were error-free or contained a single minor error, and 60% (9/15) were completely error-free. Error-free model scripts replicated the published incremental cost-effectiveness ratios to within 1%. CONCLUSION This study provides a promising indication that GPT-4 can have practical applications in the automation of health economic model construction. Potential benefits include accelerated model development timelines and reduced costs of development. Further research is necessary to explore the generalisability of LLM-based automation across a larger sample of models.
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Affiliation(s)
- Tim Reason
- Estima Scientific, Mediaworks, 191 Wood Ln, London, W12 7FP, UK.
| | | | - Julia Langham
- Estima Scientific, Mediaworks, 191 Wood Ln, London, W12 7FP, UK
| | - Andy Gimblett
- Estima Scientific, Mediaworks, 191 Wood Ln, London, W12 7FP, UK
| | | | - Sven Klijn
- Bristol Myers Squibb, Princeton, NJ, USA
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Carapinha JL, Botes D, Carapinha R. Balancing innovation and ethics in AI governance for health technology assessment. J Med Econ 2024; 27:754-757. [PMID: 38711204 DOI: 10.1080/13696998.2024.2352821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 05/05/2024] [Indexed: 05/08/2024]
Affiliation(s)
- João L Carapinha
- Syenza, Anaheim, CA, USA
- Northeastern University School of Pharmacy, Boston, MA, USA
| | - Danélia Botes
- Health Economics and Outcomes Research Division, Syenza, Pretoria, South Africa
| | - René Carapinha
- Dynamic Intelligence Division, Syenza, Andorra la Vella, Andorra
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Miró Catalina Q, Femenia J, Fuster-Casanovas A, Marin-Gomez FX, Escalé-Besa A, Solé-Casals J, Vidal-Alaball J. Knowledge and Perception of the Use of AI and its Implementation in the Field of Radiology: Cross-Sectional Study. J Med Internet Res 2023; 25:e50728. [PMID: 37831495 PMCID: PMC10612005 DOI: 10.2196/50728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/31/2023] [Accepted: 09/25/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Artificial Intelligence (AI) has been developing for decades, but in recent years its use in the field of health care has experienced an exponential increase. Currently, there is little doubt that these tools have transformed clinical practice. Therefore, it is important to know how the population perceives its implementation to be able to propose strategies for acceptance and implementation and to improve or prevent problems arising from future applications. OBJECTIVE This study aims to describe the population's perception and knowledge of the use of AI as a health support tool and its application to radiology through a validated questionnaire, in order to develop strategies aimed at increasing acceptance of AI use, reducing possible resistance to change and identifying possible sociodemographic factors related to perception and knowledge. METHODS A cross-sectional observational study was conducted using an anonymous and voluntarily validated questionnaire aimed at the entire population of Catalonia aged 18 years or older. The survey addresses 4 dimensions defined to describe users' perception of the use of AI in radiology, (1) "distrust and accountability," (2) "personal interaction," (3) "efficiency," and (4) "being informed," all with questions in a Likert scale format. Results closer to 5 refer to a negative perception of the use of AI, while results closer to 1 express a positive perception. Univariate and bivariate analyses were performed to assess possible associations between the 4 dimensions and sociodemographic characteristics. RESULTS A total of 379 users responded to the survey, with an average age of 43.9 (SD 17.52) years and 59.8% (n=226) of them identified as female. In addition, 89.8% (n=335) of respondents indicated that they understood the concept of AI. Of the 4 dimensions analyzed, "distrust and accountability" obtained a mean score of 3.37 (SD 0.53), "personal interaction" obtained a mean score of 4.37 (SD 0.60), "efficiency" obtained a mean score of 3.06 (SD 0.73) and "being informed" obtained a mean score of 3.67 (SD 0.57). In relation to the "distrust and accountability" dimension, women, people older than 65 years, the group with university studies, and the population that indicated not understanding the AI concept had significantly more distrust in the use of AI. On the dimension of "being informed," it was observed that the group with university studies rated access to information more positively and those who indicated not understanding the concept of AI rated it more negatively. CONCLUSIONS The majority of the sample investigated reported being familiar with the concept of AI, with varying degrees of acceptance of its implementation in radiology. It is clear that the most conflictive dimension is "personal interaction," whereas "efficiency" is where there is the greatest acceptance, being the dimension in which there are the best expectations for the implementation of AI in radiology.
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Affiliation(s)
- Queralt Miró Catalina
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
| | - Joaquim Femenia
- Faculty of Medicine, University of Vic-Central University of Catalonia, Vic, Spain
| | - Aïna Fuster-Casanovas
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
| | - Francesc X Marin-Gomez
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
| | - Anna Escalé-Besa
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
- Faculty of Medicine, University of Vic-Central University of Catalonia, Vic, Spain
| | - Jordi Solé-Casals
- Data and Signal Processing group, Faculty of Science, Technology and Engineering, University of Vic-Central University of Catalonia, Vic, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Josep Vidal-Alaball
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
- Faculty of Medicine, University of Vic-Central University of Catalonia, Vic, Spain
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Cresswell K, Rigby M, Magrabi F, Scott P, Brender J, Craven CK, Wong ZSY, Kukhareva P, Ammenwerth E, Georgiou A, Medlock S, De Keizer NF, Nykänen P, Prgomet M, Williams R. The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision. Health Policy 2023; 136:104889. [PMID: 37579545 DOI: 10.1016/j.healthpol.2023.104889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 08/04/2023] [Indexed: 08/16/2023]
Abstract
Despite the renewed interest in Artificial Intelligence-based clinical decision support systems (AI-CDS), there is still a lack of empirical evidence supporting their effectiveness. This underscores the need for rigorous and continuous evaluation and monitoring of processes and outcomes associated with the introduction of health information technology. We illustrate how the emergence of AI-CDS has helped to bring to the fore the critical importance of evaluation principles and action regarding all health information technology applications, as these hitherto have received limited attention. Key aspects include assessment of design, implementation and adoption contexts; ensuring systems support and optimise human performance (which in turn requires understanding clinical and system logics); and ensuring that design of systems prioritises ethics, equity, effectiveness, and outcomes. Going forward, information technology strategy, implementation and assessment need to actively incorporate these dimensions. International policy makers, regulators and strategic decision makers in implementing organisations therefore need to be cognisant of these aspects and incorporate them in decision-making and in prioritising investment. In particular, the emphasis needs to be on stronger and more evidence-based evaluation surrounding system limitations and risks as well as optimisation of outcomes, whilst ensuring learning and contextual review. Otherwise, there is a risk that applications will be sub-optimally embodied in health systems with unintended consequences and without yielding intended benefits.
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Affiliation(s)
- Kathrin Cresswell
- The University of Edinburgh, Usher Institute, Edinburgh, United Kingdom.
| | - Michael Rigby
- Keele University, School of Social, Political and Global Studies and School of Primary, Community and Social Care, Keele, United Kingdom
| | - Farah Magrabi
- Macquarie University, Australian Institute of Health Innovation, Sydney, Australia
| | - Philip Scott
- University of Wales Trinity Saint David, Swansea, United Kingdom
| | - Jytte Brender
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Catherine K Craven
- University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Zoie Shui-Yee Wong
- St. Luke's International University, Graduate School of Public Health, Tokyo, Japan
| | - Polina Kukhareva
- Department of Biomedical Informatics, University of Utah, United States of America
| | - Elske Ammenwerth
- UMIT TIROL, Private University for Health Sciences and Health Informatics, Institute of Medical Informatics, Hall in Tirol, Austria
| | - Andrew Georgiou
- Macquarie University, Australian Institute of Health Innovation, Sydney, Australia
| | - Stephanie Medlock
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Public Health research institute, Digital Health and Quality of Care Amsterdam, the Netherlands
| | - Nicolette F De Keizer
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Public Health research institute, Digital Health and Quality of Care Amsterdam, the Netherlands
| | - Pirkko Nykänen
- Tampere University, Faculty for Information Technology and Communication Sciences, Finland
| | - Mirela Prgomet
- Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Robin Williams
- The University of Edinburgh, Institute for the Study of Science, Technology and Innovation, Edinburgh, United Kingdom
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Victor G, Bélisle-Pipon JC, Ravitsky V. Generative AI, Specific Moral Values: A Closer Look at ChatGPT's New Ethical Implications for Medical AI. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2023; 23:65-68. [PMID: 37812098 PMCID: PMC10575680 DOI: 10.1080/15265161.2023.2250311] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
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Bélisle-Pipon JC, Ravitsky V, Bensoussan Y. Individuals and (Synthetic) Data Points: Using Value-Sensitive Design to Foster Ethical Deliberations on Epistemic Transitions. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2023; 23:69-72. [PMID: 37647464 DOI: 10.1080/15265161.2023.2237436] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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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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10
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Walter W, Pohlkamp C, Meggendorfer M, Nadarajah N, Kern W, Haferlach C, Haferlach T. Artificial intelligence in hematological diagnostics: Game changer or gadget? Blood Rev 2023; 58:101019. [PMID: 36241586 DOI: 10.1016/j.blre.2022.101019] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 09/21/2022] [Accepted: 10/03/2022] [Indexed: 11/30/2022]
Abstract
The future of clinical diagnosis and treatment of hematologic diseases will inevitably involve the integration of artificial intelligence (AI)-based systems into routine practice to support the hematologists' decision making. Several studies have shown that AI-based models can already be used to automatically differentiate cells, reliably detect malignant cell populations, support chromosome banding analysis, and interpret clinical variants, contributing to early disease detection and prognosis. However, even the best tool can become useless if it is misapplied or the results are misinterpreted. Therefore, in order to comprehensively judge and correctly apply newly developed AI-based systems, the hematologist must have a basic understanding of the general concepts of machine learning. In this review, we provide the hematologist with a comprehensive overview of various machine learning techniques, their current implementations and approaches in different diagnostic subfields (e.g., cytogenetics, molecular genetics), and the limitations and unresolved challenges of the systems.
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Affiliation(s)
- Wencke Walter
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 München, Germany.
| | - Christian Pohlkamp
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 München, Germany.
| | - Manja Meggendorfer
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 München, Germany.
| | - Niroshan Nadarajah
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 München, Germany.
| | - Wolfgang Kern
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 München, Germany.
| | - Claudia Haferlach
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 München, Germany.
| | - Torsten Haferlach
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 München, Germany.
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Bélisle-Pipon JC, David PM. Digital Therapies (DTx) as New Tools within Physicians' Therapeutic Arsenal: Key Observations to Support their Effective and Responsible Development and Use. Pharmaceut Med 2023; 37:121-127. [PMID: 36653600 DOI: 10.1007/s40290-022-00459-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2022] [Indexed: 01/20/2023]
Abstract
In recent years, there has been a swift rise in the development of digital therapies (DTx). As a result of various technological advances and accessibility to patients, it is now possible to develop and offer therapeutic interventions in a digital manner. These take the form of an evidence-based intervention that is administered in digital form to prevent, manage, or treat a medical condition. What makes DTx significantly different from other types of digital applications or services (e.g., wellness applications) is that they are interventions authorised by regulatory agencies for the treatment, like a drug, of a health condition. Yielding actual therapeutic benefits and being at the crossroads of health and digital means that DTx are subject to both the upsides and downsides of both sectors. Thus, it is of particular interest to look at the facilitators and barriers to be foreseen in the development, assessment, and implementation of DTx. In this article, we will present key observations and outline the main challenges that may be faced in the development and integration of DTx into practice. It is certain that DTx can represent an interesting avenue for physicians to bring their prescribing role into the 21st century. We conclude with broad lessons that the emerging field of DTx can learn from decades of drug industry practice to avoid history repeating itself and to fast-track the development and ethical and optimal use of DTx.
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Affiliation(s)
- Jean-Christophe Bélisle-Pipon
- Faculty of Health Sciences, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia, V5A 1S6, Canada.
| | - Pierre-Marie David
- Faculty of Pharmacy, Université de Montréal, 2900 Blvd Edouard Montpetit, Montréal, Québec, H3T 1J4, Canada
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12
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Zemplényi A, Tachkov K, Balkanyi L, Németh B, Petykó ZI, Petrova G, Czech M, Dawoud D, Goettsch W, Gutierrez Ibarluzea I, Hren R, Knies S, Lorenzovici L, Maravic Z, Piniazhko O, Savova A, Manova M, Tesar T, Zerovnik S, Kaló Z. Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment. Front Public Health 2023; 11:1088121. [PMID: 37181704 PMCID: PMC10171457 DOI: 10.3389/fpubh.2023.1088121] [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: 11/08/2022] [Accepted: 04/03/2023] [Indexed: 05/16/2023] Open
Abstract
Background Artificial intelligence (AI) has attracted much attention because of its enormous potential in healthcare, but uptake has been slow. There are substantial barriers that challenge health technology assessment (HTA) professionals to use AI-generated evidence for decision-making from large real-world databases (e.g., based on claims data). As part of the European Commission-funded HTx H2020 (Next Generation Health Technology Assessment) project, we aimed to put forward recommendations to support healthcare decision-makers in integrating AI into the HTA processes. The barriers, addressed by the paper, are particularly focusing on Central and Eastern European (CEE) countries, where the implementation of HTA and access to health databases lag behind Western European countries. Methods We constructed a survey to rank the barriers to using AI for HTA purposes, completed by respondents from CEE jurisdictions with expertise in HTA. Using the results, two members of the HTx consortium from CEE developed recommendations on the most critical barriers. Then these recommendations were discussed in a workshop by a wider group of experts, including HTA and reimbursement decision-makers from both CEE countries and Western European countries, and summarized in a consensus report. Results Recommendations have been developed to address the top 15 barriers in areas of (1) human factor-related barriers, focusing on educating HTA doers and users, establishing collaborations and best practice sharing; (2) regulatory and policy-related barriers, proposing increasing awareness and political commitment and improving the management of sensitive information for AI use; (3) data-related barriers, suggesting enhancing standardization and collaboration with data networks, managing missing and unstructured data, using analytical and statistical approaches to address bias, using quality assessment tools and quality standards, improving reporting, and developing better conditions for the use of data; and (4) technological barriers, suggesting sustainable development of AI infrastructure. Conclusion In the field of HTA, the great potential of AI to support evidence generation and evaluation has not yet been sufficiently explored and realized. Raising awareness of the intended and unintended consequences of AI-based methods and encouraging political commitment from policymakers is necessary to upgrade the regulatory and infrastructural environment and knowledge base required to integrate AI into HTA-based decision-making processes better.
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Affiliation(s)
- Antal Zemplényi
- Center for Health Technology Assessment and Pharmacoeconomics Research, Faculty of Pharmacy, University of Pécs, Pécs, Hungary
- Syreon Research Institute, Budapest, Hungary
- *Correspondence: Antal Zemplényi,
| | - Konstantin Tachkov
- Department of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
| | - Laszlo Balkanyi
- Medical Informatics R&D Center, Pannon University, Veszprém, Hungary
| | | | | | - Guenka Petrova
- Department of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
| | - Marcin Czech
- Department of Pharmacoeconomics, Institute of Mother and Child, Warsaw, Poland
| | - Dalia Dawoud
- Science Policy and Research Programme, Science Evidence and Analytics Directorate, National Institute for Health and Care Excellence (NICE), London, United Kingdom
- Cairo University, Faculty of Pharmacy, Cairo, Egypt
| | - Wim Goettsch
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht University, Utrecht, Netherlands
- National Health Care Institute, Diemen, Netherlands
| | | | - Rok Hren
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - Saskia Knies
- National Health Care Institute, Diemen, Netherlands
| | - László Lorenzovici
- Syreon Research Romania, Tirgu Mures, Romania
- G. E. Palade University of Medicine, Pharmacy, Science and Technology, Tirgu Mures, Romania
| | | | - Oresta Piniazhko
- HTA Department of State Expert Centre of the Ministry of Health of Ukraine, Kyiv, Ukraine
| | - Alexandra Savova
- Department of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
- National Council of Prices and Reimbursement of Medicinal Products, Sofia, Bulgaria
| | - Manoela Manova
- Department of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
- National Council of Prices and Reimbursement of Medicinal Products, Sofia, Bulgaria
| | - Tomas Tesar
- Department of Organisation and Management of Pharmacy, Faculty of Pharmacy, Comenius University in Bratislava, Bratislava, Slovakia
| | | | - Zoltán Kaló
- Syreon Research Institute, Budapest, Hungary
- Centre for Health Technology Assessment, Semmelweis University, Budapest, Hungary
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13
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Couture V, Roy MC, Dez E, Laperle S, Bélisle-Pipon JC. Ethical Implications of Artificial Intelligence in Population Health and the Public’s Role in its Governance: Perspectives from a Citizen and Expert Panel (Preprint). J Med Internet Res 2022; 25:e44357. [PMID: 37104026 PMCID: PMC10176139 DOI: 10.2196/44357] [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: 11/16/2022] [Revised: 02/14/2023] [Accepted: 03/10/2023] [Indexed: 03/12/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) systems are widely used in the health care sector. Mainly applied for individualized care, AI is increasingly aimed at population health. This raises important ethical considerations but also calls for responsible governance, considering that this will affect the population. However, the literature points to a lack of citizen participation in the governance of AI in health. Therefore, it is necessary to investigate the governance of the ethical and societal implications of AI in population health. OBJECTIVE This study aimed to explore the perspectives and attitudes of citizens and experts regarding the ethics of AI in population health, the engagement of citizens in AI governance, and the potential of a digital app to foster citizen engagement. METHODS We recruited a panel of 21 citizens and experts. Using a web-based survey, we explored their perspectives and attitudes on the ethical issues of AI in population health, the relative role of citizens and other actors in AI governance, and the ways in which citizens can be supported to participate in AI governance through a digital app. The responses of the participants were analyzed quantitatively and qualitatively. RESULTS According to the participants, AI is perceived to be already present in population health and its benefits are regarded positively, but there is a consensus that AI has substantial societal implications. The participants also showed a high level of agreement toward involving citizens into AI governance. They highlighted the aspects to be considered in the creation of a digital app to foster this involvement. They recognized the importance of creating an app that is both accessible and transparent. CONCLUSIONS These results offer avenues for the development of a digital app to raise awareness, to survey, and to support citizens' decision-making regarding the ethical, legal, and social issues of AI in population health.
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Affiliation(s)
| | | | - Emma Dez
- School of Research, Sciences Po Paris, Paris, France
| | - Samuel Laperle
- Department of Linguistics, Université du Québec à Montréal, Montréal, QC, Canada
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14
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Chen Y, Moreira P, Liu WW, Monachino M, Nguyen TLH, Wang A. Is there a gap between artificial intelligence applications and priorities in health care and nursing management? J Nurs Manag 2022; 30:3736-3742. [PMID: 36216773 PMCID: PMC10092524 DOI: 10.1111/jonm.13851] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/02/2022] [Accepted: 10/02/2022] [Indexed: 12/30/2022]
Abstract
AIM The article aims to outline a contrast between three priorities for nursing management proposed a decade ago and key features of the following 10 years of developments on artificial intelligence for health care and nursing management. This analysis intends to contribute to update the international debate on bridging the essence of health care and nursing management priorities and the focus of artificial intelligence developers. BACKGROUND Artificial intelligence research promises innovative approaches to supporting nurses' clinical decision-making and to conduct tasks not related to patient interaction, including administrative activities and patient records. Yet, even though there has been an increase in international research and development of artificial intelligence applications for nursing care during the past 10 years, it is unclear to what extent the priorities of nursing management have been embedded in the devised artificial intelligence solutions. EVALUATION Starting from three priorities for nursing management identified in 2011 in a special issue of the Journal Nursing Management, we went on to identify recent evidence concerning 10 years of artificial intelligence applications developed to support health care management and nursing activities since then. KEY ISSUE The article discusses to what extent priorities in health care and nursing management may have to be revised while adopting artificial intelligence applications or, alternatively, to what extent the direction of artificial intelligence developments may need to be revised to contribute to long acknowledged priorities of nursing management. CONCLUSION We have identified a conceptual gap between both sets of ideas and provide a discussion on the need to bridge that gap, while admitting that there may have been recent field developments still unreported in scientific literature. IMPLICATIONS FOR NURSING MANAGEMENT Artificial intelligence developers and health care nursing managers need to be more engaged in coordinating the future development of artificial intelligence applications with a renewed set of nursing management priorities.
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Affiliation(s)
- Yanjiao Chen
- Research Center on Social Work and Social Governance in Henan Province, Henan Normal University, Sociology Department, Xinxiang, China
| | - Paulo Moreira
- Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China.,Departamento de Ciencias da Gestao (Gestao em Saude), Atlantica Instituto Universitario, Oeiras, Portugal
| | - Wei-Wei Liu
- School of Social Work, Henan Normal University, Xinxiang, China
| | | | - Thi Le Ha Nguyen
- VNU University of Medicine and Pharmacy, Vietnam National University, Hanoi, Vietnam
| | - Aihua Wang
- Obstetrics Department, Kunming Maternal and Child Hospital, Kunming, China
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15
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Luo X, Wu Y, Niu L, Huang L. Bibliometric Analysis of Health Technology Research: 1990~2020. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:9044. [PMID: 35897415 PMCID: PMC9330553 DOI: 10.3390/ijerph19159044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/19/2022] [Accepted: 07/19/2022] [Indexed: 12/10/2022]
Abstract
This paper aims to summarize the publishing trends, current status, research topics, and frontier evolution trends of health technology between 1990 and 2020 through various bibliometric analysis methods. In total, 6663 articles retrieved from the Web of Science core database were analyzed by Vosviewer and CiteSpace software. This paper found that: (1) The number of publications in the field of health technology increased exponentially; (2) there is no stable core group of authors in this research field, and the influence of the publishing institutions and journals in China is insufficient compared with those in Europe and the United States; (3) there are 21 core research topics in the field of health technology research, and these research topics can be divided into four classes: hot spots, potential hot spots, margin topics, and mature topics. C21 (COVID-19 prevention) and C10 (digital health technology) are currently two emerging research topics. (4) The number of research frontiers has increased in the past five years (2016-2020), and the research directions have become more diverse; rehabilitation, pregnancy, e-health, m-health, machine learning, and patient engagement are the six latest research frontiers.
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Affiliation(s)
| | | | | | - Lucheng Huang
- College of Economics and Management, Beijing University of Technology, Beijing 100124, China; (X.L.); (Y.W.); (L.N.)
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16
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Tachkov K, Zemplenyi A, Kamusheva M, Dimitrova M, Siirtola P, Pontén J, Nemeth B, Kalo Z, Petrova G. Barriers to Use Artificial Intelligence Methodologies in Health Technology Assessment in Central and East European Countries. Front Public Health 2022; 10:921226. [PMID: 35910914 PMCID: PMC9330148 DOI: 10.3389/fpubh.2022.921226] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 06/20/2022] [Indexed: 12/05/2022] Open
Abstract
The aim of this paper is to identify the barriers that are specifically relevant to the use of Artificial Intelligence (AI)-based evidence in Central and Eastern European (CEE) Health Technology Assessment (HTA) systems. The study relied on two main parallel sources to identify barriers to use AI methodologies in HTA in CEE, including a scoping literature review and iterative focus group meetings with HTx team members. Most of the other selected articles discussed AI from a clinical perspective (n = 25), and the rest are from regulatory perspective (n = 13), and transfer of knowledge point of view (n = 3). Clinical areas studied are quite diverse—from pediatric, diabetes, diagnostic radiology, gynecology, oncology, surgery, psychiatry, cardiology, infection diseases, and oncology. Out of all 38 articles, 25 (66%) describe the AI method and the rest are more focused on the utilization barriers of different health care services and programs. The potential barriers could be classified as data related, methodological, technological, regulatory and policy related, and human factor related. Some of the barriers are quite similar, especially concerning the technologies. Studies focusing on the AI usage for HTA decision making are scarce. AI and augmented decision making tools are a novel science, and we are in the process of adapting it to existing needs. HTA as a process requires multiple steps, multiple evaluations which rely on heterogenous data. Therefore, the observed range of barriers come as a no surprise, and experts in the field need to give their opinion on the most important barriers in order to develop recommendations to overcome them and to disseminate the practical application of these tools.
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Affiliation(s)
| | - Antal Zemplenyi
- Syreon Research Institute, Budapest, Hungary
- Center for Health Technology Assessment and Pharmacoeconomic Research, University of Pecs, Pecs, Hungary
| | - Maria Kamusheva
- Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
| | - Maria Dimitrova
- Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
| | - Pekka Siirtola
- Biomimetics and Intelligent Systems Group, University of Oulu, Oulu, Finland
| | - Johan Pontén
- Dental and Pharmaceutical Benefits Agency, Stockholm, Sweden
| | | | - Zoltan Kalo
- Syreon Research Institute, Budapest, Hungary
- Centre for Health Technology Assessment, Semmelweis University, Budapest, Hungary
| | - Guenka Petrova
- Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
- *Correspondence: Guenka Petrova
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17
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Artificial intelligence ethics has a black box problem. AI & SOCIETY 2022. [DOI: 10.1007/s00146-021-01380-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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18
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Monteith S, Glenn T, Geddes J, Whybrow PC, Achtyes E, Bauer M. Expectations for Artificial Intelligence (AI) in Psychiatry. Curr Psychiatry Rep 2022; 24:709-721. [PMID: 36214931 PMCID: PMC9549456 DOI: 10.1007/s11920-022-01378-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/15/2022] [Indexed: 01/29/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine. RECENT FINDINGS For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.
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Affiliation(s)
- Scott Monteith
- Michigan State University College of Human Medicine, Traverse City Campus, Traverse City, MI, 49684, USA.
| | | | - John Geddes
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Peter C. Whybrow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los Angeles, CA USA
| | - Eric Achtyes
- Michigan State University College of Human Medicine, Grand Rapids, MI 49684 USA ,Network180, Grand Rapids, MI USA
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus Medical Faculty, Technische Universität Dresden, Dresden, Germany
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