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Madaudo C, Parlati ALM, Di Lisi D, Carluccio R, Sucato V, Vadalà G, Nardi E, Macaione F, Cannata A, Manzullo N, Santoro C, Iervolino A, D'Angelo F, Marzano F, Basile C, Gargiulo P, Corrado E, Paolillo S, Novo G, Galassi AR, Filardi PP. Artificial intelligence in cardiology: a peek at the future and the role of ChatGPT in cardiology practice. J Cardiovasc Med (Hagerstown) 2024; 25:766-771. [PMID: 39347723 DOI: 10.2459/jcm.0000000000001664] [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: 04/24/2024] [Accepted: 08/19/2024] [Indexed: 10/01/2024]
Abstract
Artificial intelligence has increasingly become an integral part of our daily activities. ChatGPT, a natural language processing technology developed by OpenAI, is widely used in various industries, including healthcare. The application of ChatGPT in healthcare is still evolving, with studies exploring its potential in clinical decision-making, patient education, workflow optimization, and scientific literature. ChatGPT could be exploited in the medical field to improve patient education and information, thus increasing compliance. ChatGPT could facilitate information exchange on major cardiovascular diseases, provide clinical decision support, and improve patient communication and education. It could assist the clinician in differential diagnosis, suggest appropriate imaging modalities, and optimize treatment plans based on evidence-based guidelines. However, it is unclear whether it will be possible to use ChatGPT for the management of patients who require rapid decisions. Indeed, many drawbacks are associated with the daily use of these technologies in the medical field, such as insufficient expertise in specialized fields and a lack of comprehension of the context in which it works. The pros and cons of its use have been explored in this review, which was not written with the help of ChatGPT.
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Affiliation(s)
- Cristina Madaudo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Cardiology Unit, University of Palermo, University Hospital P. Giaccone, Palermo
- Department of Cardiovascular Sciences, British Heart Foundation Centre of Research Excellence, School of Cardiovascular Medicine, Faculty of Life Sciences and Medicine, King's College London, The James Black Centre, 125 Coldharbour Lane, London, UK
| | - Antonio Luca Maria Parlati
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
- Department of Cardiovascular Sciences, British Heart Foundation Centre of Research Excellence, School of Cardiovascular Medicine, Faculty of Life Sciences and Medicine, King's College London, The James Black Centre, 125 Coldharbour Lane, London, UK
| | - Daniela Di Lisi
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Cardiology Unit, University of Palermo, University Hospital P. Giaccone, Palermo
| | - Raffaele Carluccio
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Vincenzo Sucato
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Cardiology Unit, University of Palermo, University Hospital P. Giaccone, Palermo
| | - Giuseppe Vadalà
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Cardiology Unit, University of Palermo, University Hospital P. Giaccone, Palermo
| | - Ermanno Nardi
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Francesca Macaione
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Cardiology Unit, University of Palermo, University Hospital P. Giaccone, Palermo
| | - Antonio Cannata
- Department of Cardiovascular Sciences, British Heart Foundation Centre of Research Excellence, School of Cardiovascular Medicine, Faculty of Life Sciences and Medicine, King's College London, The James Black Centre, 125 Coldharbour Lane, London, UK
| | - Nilla Manzullo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Cardiology Unit, University of Palermo, University Hospital P. Giaccone, Palermo
| | - Ciro Santoro
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Adelaide Iervolino
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Federica D'Angelo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Cardiology Unit, University of Palermo, University Hospital P. Giaccone, Palermo
| | - Federica Marzano
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Christian Basile
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Paola Gargiulo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Egle Corrado
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Cardiology Unit, University of Palermo, University Hospital P. Giaccone, Palermo
| | - Stefania Paolillo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Giuseppina Novo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Cardiology Unit, University of Palermo, University Hospital P. Giaccone, Palermo
| | - Alfredo Ruggero Galassi
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Cardiology Unit, University of Palermo, University Hospital P. Giaccone, Palermo
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Zainal H, Hui XX, Thumboo J, Fong W, Yong FK. Patients' Expectations of Doctors' Clinical Competencies in the Digital Health Care Era: Qualitative Semistructured Interview Study Among Patients. JMIR Hum Factors 2024; 11:e51972. [PMID: 39190915 PMCID: PMC11387909 DOI: 10.2196/51972] [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: 08/18/2023] [Revised: 01/31/2024] [Accepted: 05/05/2024] [Indexed: 08/29/2024] Open
Abstract
BACKGROUND Digital technologies have impacted health care delivery globally, and are increasingly being deployed in clinical practice. However, there is limited research on patients' expectations of doctors' clinical competencies when using digital health care technologies (DHTs) in medical care. Understanding these expectations can reveal competency gaps, enhance patient confidence, and contribute to digital innovation initiatives. OBJECTIVE This study explores patients' perceptions of doctors' use of DHTs in clinical care. Using Singapore as a case study, it examines patients' expectations regarding doctors' communication, diagnosis, and treatment skills when using telemedicine, health apps, wearable devices, electronic health records, and artificial intelligence. METHODS Findings were drawn from individual semistructured interviews with patients from outpatient clinics. Participants were recruited using purposive sampling. Data were analyzed qualitatively using thematic analysis. RESULTS Twenty-five participants from different backgrounds and with various chronic conditions participated in the study. They expected doctors to be adept in handling medical data from apps and wearable devices. For telemedicine, participants expected a level of assessment of their medical conditions akin to in-person consultations. In addition, they valued doctors recognizing when a physical examination was necessary. Interestingly, eye contact was appreciated but deemed nonessential by participants across all age bands when electronic health records were used, as they valued the doctor's efficiency more than eye contact. Nonetheless, participants emphasized the need for empathy throughout the clinical encounter regardless of DHT use. Furthermore, younger participants had a greater expectation for DHT use among doctors compared to older ones, who preferred DHTs as a complement rather than a replacement for clinical skills. The former expected doctors to be knowledgeable about the algorithms, principles, and purposes of DHTs such as artificial intelligence technologies to better assist them in diagnosis and treatment. CONCLUSIONS By identifying patients' expectations of doctors amid increasing health care digitalization, this study highlights that while basic clinical skills remain crucial in the digital age, the role of clinicians needs to evolve with the introduction of DHTs. It has also provided insights into how DHTs can be integrated effectively into clinical settings, aligning with patients' expectations and preferences. Overall, the findings offer a framework for high-income countries to harness DHTs in enhancing health care delivery in the digital era.
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Affiliation(s)
- Humairah Zainal
- Health Services Research Unit, Singapore General Hospital, Singapore, Singapore
| | - Xin Xiao Hui
- Health Services Research Unit, Singapore General Hospital, Singapore, Singapore
| | - Julian Thumboo
- Health Services Research Unit, Singapore General Hospital, Singapore, Singapore
- Department of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Warren Fong
- Department of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Fong Kok Yong
- Department of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
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Cao S, Fu D, Yang X, Wermter S, Liu X, Wu H. Pain recognition and pain empathy from a human-centered AI perspective. iScience 2024; 27:110570. [PMID: 39211548 PMCID: PMC11357883 DOI: 10.1016/j.isci.2024.110570] [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] [Indexed: 09/04/2024] Open
Abstract
Sensory and emotional experiences are essential for mental and physical well-being, especially within the realm of psychiatry. This article highlights recent advances in cognitive neuroscience, emphasizing the significance of pain recognition and empathic artificial intelligence (AI) in healthcare. We provide an overview of the recent development process in computational pain recognition and cognitive neuroscience regarding the mechanisms of pain and empathy. Through a comprehensive discussion, the article delves into critical questions such as the methodologies for AI in recognizing pain from diverse sources of information, the necessity for AI to exhibit empathic responses, and the associated advantages and obstacles linked with the development of empathic AI. Moreover, insights into the prospects and challenges are emphasized in relation to fostering artificial empathy. By delineating potential pathways for future research, the article aims to contribute to developing effective assistants equipped with empathic capabilities, thereby introducing safe and meaningful interactions between humans and AI, particularly in the context of mental health and psychiatry.
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Affiliation(s)
- Siqi Cao
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Di Fu
- School of Psychology, University of Surrey, Guildford, UK
| | - Xu Yang
- State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Stefan Wermter
- Department of Informatics, University of Hamburg, Hamburg, Germany
| | - Xun Liu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Haiyan Wu
- Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Taipa, Macau
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Rony MKK, Numan SM, Johra FT, Akter K, Akter F, Debnath M, Mondal S, Wahiduzzaman M, Das M, Ullah M, Rahman MH, Das Bala S, Parvin MR. Perceptions and attitudes of nurse practitioners toward artificial intelligence adoption in health care. Health Sci Rep 2024; 7:e70006. [PMID: 39175600 PMCID: PMC11339127 DOI: 10.1002/hsr2.70006] [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: 01/01/2024] [Revised: 07/31/2024] [Accepted: 08/06/2024] [Indexed: 08/24/2024] Open
Abstract
Background With the ever-increasing integration of artificial intelligence (AI) into health care, it becomes imperative to gain an in-depth understanding of how health care professionals, specifically nurse practitioners, perceive and approach this transformative technology. Objectives This study aimed to gain insights into nurse practitioners' perceptions and attitudes toward AI adoption in health care. Methods This qualitative research employed a descriptive and phenomenological approach using in-depth interviews. Data were collected through a semi-structured questionnaire with 37 nurse practitioners selected through purposive sampling, specifically Maximum Variation Sampling and Expert Sampling techniques, to ensure diversity in characteristics. Trustworthiness of the research was maintained through member checking and peer debriefing. Thematic analysis was employed to uncover recurring themes and patterns in the data. Results The thematic analysis revealed nine main themes that encapsulated nurse practitioners' perceptions and attitudes toward AI adoption in health care. These included nurse practitioners' perceptions of AI implementation, attitudes toward AI adoption, patient-centered care and AI, quality of health care delivery and AI, ethical and regulatory aspects of AI, education and training needs, collaboration and interdisciplinary relationships, obstacles in integrating AI, and AI and health care policy. While this study found that nurse practitioners held a wide range of perspectives, with many viewings AI as a tool to enhance patient care. Conclusions This research provides a valuable contribution to the evolving discourse surrounding AI adoption in health care. The findings underscore the necessity for comprehensive education and training in AI, accompanied by clear and robust ethical and regulatory guidelines to ensure the responsible integration of AI in health care practice. Furthermore, fostering collaboration and interdisciplinary relationships is pivotal for the successful incorporation of AI in health care. Policymakers should also address the challenges and opportunities that AI presents in the health care sector. This study enhances the ongoing conversation on AI adoption in health care by shedding light on the perspectives of nurses, thereby shaping future strategies for AI integration.
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Affiliation(s)
| | - Sharker Md. Numan
- School of Science and TechnologyBangladesh Open UniversityGazipurBangladesh
| | - Fateha tuj Johra
- Masters in Disaster ManagementUniversity of DhakaDhakaBangladesh
| | - Khadiza Akter
- Master of Public HealthDaffodil International UniversityDhakaBangladesh
| | - Fazila Akter
- Dhaka Nursing Collegeaffiliated with the University of DhakaDhakaBangladesh
| | - Mitun Debnath
- Master of Public HealthNational Institute of Preventive and Social MedicineDhakaBangladesh
| | - Sujit Mondal
- Master of Science in NursingNational Institute of Advanced Nursing Education and Research MugdaDhakaBangladesh
| | - Md. Wahiduzzaman
- School of Medical SciencesShahjalal University of Science and TechnologySylhetBangladesh
| | - Mousumi Das
- Master of Public HealthLeading UniversitySylhetBangladesh
| | - Mohammad Ullah
- College of NursingInternational University of Business Agriculture and TechnologyDhakaBangladesh
| | | | - Shuvashish Das Bala
- College of NursingInternational University of Business Agriculture and TechnologyDhakaBangladesh
| | - Mst. Rina Parvin
- Bangladesh Army (AFNS Officer)Combined Military Hospital DhakaDhakaBangladesh
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Koranteng E, Lewis ACF, Abel GA. Compassionate Machines: The Ethics of "Artificial Empathy" in Cancer Care. JAMA Oncol 2024; 10:857-858. [PMID: 38696207 DOI: 10.1001/jamaoncol.2024.0824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/19/2024]
Abstract
This Viewpoint discusses the ethics of artificial intelligence–generated compassion in cancer care and outlines 4 main points of concern.
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Affiliation(s)
- Erica Koranteng
- Division of Population Sciences, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Anna C F Lewis
- Harvard Medical School, Boston, Massachusetts
- Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts
| | - Gregory A Abel
- Division of Population Sciences, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
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Gwillim EC, Azzawi S, Aigen AR. Underserved populations and health equity in dermatology: Digital medicine and the role of artificial intelligence. Clin Dermatol 2024:S0738-081X(24)00105-6. [PMID: 38944248 DOI: 10.1016/j.clindermatol.2024.06.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2024]
Abstract
We have reviewed the current literature focused on the role of artificial intelligence (AI) for underserved populations and health equity in dermatology. Studies evaluating the utility and safety of AI model builds, and how they meet predefined benchmarks, as well as the clinical applications of AI, including decision-support systems and operational management, were the focus of this study. The seven studies included in our review provide an approach that assures underserved populations are the focus when developing and testing AI technology. They provide examples that could guide future studies focused on expanding care to underserved dermatology populations through the use of AI.
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Affiliation(s)
- Eran C Gwillim
- Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA; Jackson Health System, Miami, Florida, USA; Elli & Co, Miami, Florida, USA.
| | - Soraya Azzawi
- Division of Dermatology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Alyx Rosen Aigen
- Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA
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Hernández-Xumet JE, García-Hernández AM, Fernández-González JP, Marrero-González CM. Exploring levels of empathy and assertiveness in final year physiotherapy students during clinical placements. Sci Rep 2024; 14:13349. [PMID: 38858441 PMCID: PMC11164891 DOI: 10.1038/s41598-024-64148-8] [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: 01/23/2024] [Accepted: 06/05/2024] [Indexed: 06/12/2024] Open
Abstract
Empathy and assertiveness are two essential soft skills for any healthcare professional's competence and ethical development. It has been shown that empathy can be influenced throughout the training of a future healthcare professional, particularly during the clinical placement period. This research aims to assess fourth-year physiotherapy students' empathic and assertive development before and after clinical placement. A longitudinal observational study was conducted with fourth-year physiotherapy students during the academic year 2022/2023. A preliminary assessment of empathy and assertiveness levels was carried out before the start of the clinical placement and at the end of the placement using the Individual Reactivity Index to assess empathy and the Rathus Test to assess assertiveness. The results show a statistically significant difference (p ≤ 0.05) in both the empathy subscales of perspective-taking and empathic-concern between the pre- and postassessment, as well as an inverse correlation between the empathy subscale of personal distress and assertiveness. It is concluded that students show adequate results in empathy and assertiveness. However, there is some influence of clinical practice on the development of empathy, and future intervention studies need to be considered. Furthermore, students with higher levels of assertiveness have lower levels of personal distress, suggesting that assertiveness is closely related to empathy.
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Affiliation(s)
- Juan-Elicio Hernández-Xumet
- Movement and Health Research Group, Departamento de Medicina Física y Farmacología, Facultad de Ciencias de La Salud, Universidad de La Laguna (ULL), La Laguna, Spain.
- Hospital Universitario Nuestra Señora de Candelaria, Servicio Canario de La Salud, Santa Cruz de Tenerife, Spain.
| | | | - Jerónimo-Pedro Fernández-González
- Movement and Health Research Group, Departamento de Medicina Física y Farmacología, Facultad de Ciencias de La Salud, Universidad de La Laguna (ULL), La Laguna, Spain
- Gerencia de Atención Primaria de Tenerife, Servicio Canario de La Salud, Santa Cruz de Tenerife, Spain
| | - Cristo-Manuel Marrero-González
- Movement and Health Research Group, Departamento de Medicina Física y Farmacología, Facultad de Ciencias de La Salud, Universidad de La Laguna (ULL), La Laguna, Spain
- Departamento de Enfermería, Facultad de Ciencias de La Salud, Universidad de La Laguna (ULL), La Laguna, Spain
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Howick J, de Zulueta P, Gray M. Beyond empathy training for practitioners: Cultivating empathic healthcare systems and leadership. J Eval Clin Pract 2024; 30:548-558. [PMID: 38436621 DOI: 10.1111/jep.13970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 01/11/2024] [Indexed: 03/05/2024]
Abstract
Empathic care benefits patients and practitioners, and empathy training for practitioners can enhance empathy. However, practitioners do not operate in a vacuum. For empathy to thrive, healthcare consultations must be situated in a nurturing milieu, guided by empathic, compassionate leaders. Empathy will be suppressed, or even reversed if practitioners are burned out and working in an unpleasant, under-resourced environment with increasingly poorly served and dissatisfied patients. Efforts to enhance empathy must therefore go beyond training practitioners to address system-level factors that foster empathy. These include patient education, cultivating empathic leadership, customer service training for reception staff, valuing cleaning and all ancillary staff, creating healing spaces, and using appropriate, efficiency saving technology to reduce the administrative burden on healthcare practitioners. We divide these elements into environmental factors, organisational factors, job factors, and individual characteristics.
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Affiliation(s)
- Jeremy Howick
- Stoneygate Centre for Empathic Healthcare, Leicester Medical School, University of Leicester, Leicester, UK
| | - Paquita de Zulueta
- Department of Primary Care and Public Health, Imperial College London, London, UK
| | - Muir Gray
- Director of the Oxford Value and Stewardship Programme, Oxford, UK
- Faculty of Philosophy, University of Oxford, Oxford, United Kingdom
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Pop-Jordanova N. Opportunity to Use Artificial Intelligence in Medicine. Pril (Makedon Akad Nauk Umet Odd Med Nauki) 2024; 45:5-13. [PMID: 39008641 DOI: 10.2478/prilozi-2024-0009] [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] [Indexed: 07/17/2024]
Abstract
Over the past period different reports related to the artificial intelligence (AI) and machine learning used in everyday life have been growing intensely. However, the AI in our country is still very limited, especially in the field of medicine. The aim of this article is to give some review about AI in medicine and the related fields based on published articles in PubMed and Psych Net. A research showed more than 9 thousand articles available at the mentioned databases. After providing some historical data, different AI applications in different fields of medicine are discussed. Finally, some limitations and ethical implications are discussed.
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Ba H, Zhang L, Yi Z. Enhancing clinical skills in pediatric trainees: a comparative study of ChatGPT-assisted and traditional teaching methods. BMC MEDICAL EDUCATION 2024; 24:558. [PMID: 38778332 PMCID: PMC11112818 DOI: 10.1186/s12909-024-05565-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND As artificial intelligence (AI) increasingly integrates into medical education, its specific impact on the development of clinical skills among pediatric trainees needs detailed investigation. Pediatric training presents unique challenges which AI tools like ChatGPT may be well-suited to address. OBJECTIVE This study evaluates the effectiveness of ChatGPT-assisted instruction versus traditional teaching methods on pediatric trainees' clinical skills performance. METHODS A cohort of pediatric trainees (n = 77) was randomly assigned to two groups; one underwent ChatGPT-assisted training, while the other received conventional instruction over a period of two weeks. Performance was assessed using theoretical knowledge exams and Mini-Clinical Evaluation Exercises (Mini-CEX), with particular attention to professional conduct, clinical judgment, patient communication, and overall clinical skills. Trainees' acceptance and satisfaction with the AI-assisted method were evaluated through a structured survey. RESULTS Both groups performed similarly in theoretical exams, indicating no significant difference (p > 0.05). However, the ChatGPT-assisted group showed a statistically significant improvement in Mini-CEX scores (p < 0.05), particularly in patient communication and clinical judgment. The AI-teaching approach received positive feedback from the majority of trainees, highlighting the perceived benefits in interactive learning and skill acquisition. CONCLUSION ChatGPT-assisted instruction did not affect theoretical knowledge acquisition but did enhance practical clinical skills among pediatric trainees. The positive reception of the AI-based method suggests that it has the potential to complement and augment traditional training approaches in pediatric education. These promising results warrant further exploration into the broader applications of AI in medical education scenarios.
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Affiliation(s)
- Hongjun Ba
- Department of Pediatric Cardiology, Heart Center, First Affiliated Hospital of Sun Yat-sen University, 58# Zhongshan Road 2, Guangzhou, 510080, China.
- Key Laboratory on Assisted Circulation, Ministry of Health, 58# Zhongshan Road 2, Guangzhou, 510080, China.
| | - Lili Zhang
- Department of Pediatric Cardiology, Heart Center, First Affiliated Hospital of Sun Yat-sen University, 58# Zhongshan Road 2, Guangzhou, 510080, China
| | - Zizheng Yi
- Department of Pediatric Cardiology, Heart Center, First Affiliated Hospital of Sun Yat-sen University, 58# Zhongshan Road 2, Guangzhou, 510080, China
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Hasan HE, Jaber D, Khabour OF, Alzoubi KH. Ethical considerations and concerns in the implementation of AI in pharmacy practice: a cross-sectional study. BMC Med Ethics 2024; 25:55. [PMID: 38750441 PMCID: PMC11096093 DOI: 10.1186/s12910-024-01062-8] [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: 01/14/2024] [Accepted: 05/09/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Integrating artificial intelligence (AI) into healthcare has raised significant ethical concerns. In pharmacy practice, AI offers promising advances but also poses ethical challenges. METHODS A cross-sectional study was conducted in countries from the Middle East and North Africa (MENA) region on 501 pharmacy professionals. A 12-item online questionnaire assessed ethical concerns related to the adoption of AI in pharmacy practice. Demographic factors associated with ethical concerns were analyzed via SPSS v.27 software using appropriate statistical tests. RESULTS Participants expressed concerns about patient data privacy (58.9%), cybersecurity threats (58.9%), potential job displacement (62.9%), and lack of legal regulation (67.0%). Tech-savviness and basic AI understanding were correlated with higher concern scores (p < 0.001). Ethical implications include the need for informed consent, beneficence, justice, and transparency in the use of AI. CONCLUSION The findings emphasize the importance of ethical guidelines, education, and patient autonomy in adopting AI. Collaboration, data privacy, and equitable access are crucial to the responsible use of AI in pharmacy practice.
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Affiliation(s)
- Hisham E Hasan
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, 22110, Jordan.
- Department of Clinical Pharmacy, Faculty of Pharmacy, Zarqa University, Zarqa, 13110, Jordan.
| | - Deema Jaber
- Department of Clinical Pharmacy, Faculty of Pharmacy, Zarqa University, Zarqa, 13110, Jordan
| | - Omar F Khabour
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, 22110, Jordan
| | - Karem H Alzoubi
- Department of Pharmacy Practice and Pharmacotherapeutics, College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates
- Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, 22110, Jordan
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Liu L, Qu S, Zhao H, Kong L, Xie Z, Jiang Z, Zou P. Global trends and hotspots of ChatGPT in medical research: a bibliometric and visualized study. Front Med (Lausanne) 2024; 11:1406842. [PMID: 38818395 PMCID: PMC11137200 DOI: 10.3389/fmed.2024.1406842] [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: 03/25/2024] [Accepted: 05/06/2024] [Indexed: 06/01/2024] Open
Abstract
Objective With the rapid advancement of Chat Generative Pre-Trained Transformer (ChatGPT) in medical research, our study aimed to identify global trends and focal points in this domain. Method All publications on ChatGPT in medical research were retrieved from the Web of Science Core Collection (WoSCC) by Clarivate Analytics from January 1, 2023, to January 31, 2024. The research trends and focal points were visualized and analyzed using VOSviewer and CiteSpace. Results A total of 1,239 publications were collected and analyzed. The USA contributed the largest number of publications (458, 37.145%) with the highest total citation frequencies (2,461) and the largest H-index. Harvard University contributed the highest number of publications (33) among all full-time institutions. The Cureus Journal of Medical Science published the most ChatGPT-related research (127, 10.30%). Additionally, Wiwanitkit V contributed the majority of publications in this field (20). "Artificial Intelligence (AI) and Machine Learning (ML)," "Education and Training," "Healthcare Applications," and "Data Analysis and Technology" emerged as the primary clusters of keywords. These areas are predicted to remain hotspots in future research in this field. Conclusion Overall, this study signifies the interdisciplinary nature of ChatGPT research in medicine, encompassing AI and ML technologies, education and training initiatives, diverse healthcare applications, and data analysis and technology advancements. These areas are expected to remain at the forefront of future research, driving continued innovation and progress in the field of ChatGPT in medical research.
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Affiliation(s)
- Ling Liu
- Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People’s Hospital of Guangxi Zhuang Autonomous Region), Guilin, China
- School of Integrated Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Shenhong Qu
- Department of Otolaryngology-Head and Neck Oncology, The People’s Hospital of Guangxi Zhuang Autonoms Region, Nanning, China
| | - Haiyun Zhao
- Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People’s Hospital of Guangxi Zhuang Autonomous Region), Guilin, China
| | - Lingping Kong
- Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People’s Hospital of Guangxi Zhuang Autonomous Region), Guilin, China
| | - Zhuzhu Xie
- School of Integrated Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Zhichao Jiang
- Hunan Provincial Brain Hospital, The Second People’s Hospital of Hunan Province, Changsha, China
| | - Pan Zou
- Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People’s Hospital of Guangxi Zhuang Autonomous Region), Guilin, China
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13
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Habib MM, Hoodbhoy Z, Siddiqui MAR. Knowledge, attitudes, and perceptions of healthcare students and professionals on the use of artificial intelligence in healthcare in Pakistan. PLOS DIGITAL HEALTH 2024; 3:e0000443. [PMID: 38728363 PMCID: PMC11086889 DOI: 10.1371/journal.pdig.0000443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 03/27/2024] [Indexed: 05/12/2024]
Abstract
The advent of artificial intelligence (AI) technologies has emerged as a promising solution to enhance healthcare efficiency and improve patient outcomes. The objective of this study is to analyse the knowledge, attitudes, and perceptions of healthcare professionals in Pakistan about AI in healthcare. We conducted a cross-sectional study using a questionnaire distributed via Google Forms. This was distributed to healthcare professionals (e.g., doctors, nurses, medical students, and allied healthcare workers) working or studying in Pakistan. Consent was taken from all participants before initiating the questionnaire. The questions were related to participant demographics, basic understanding of AI, AI in education and practice, AI applications in healthcare systems, AI's impact on healthcare professions and the socio-ethical consequences of the use of AI. We analyzed the data using Statistical Package for Social Sciences (SPSS) statistical software, version 26.0. Overall, 616 individuals responded to the survey while n = 610 (99.0%) of respondents consented to participate. The mean age of participants was 32.2 ± 12.5 years. Most of the participants (78.7%, n = 480) had never received any formal sessions or training in AI during their studies/employment. A majority of participants, 70.3% (n = 429), believed that AI would raise more ethical challenges in healthcare. In all, 66.4% (n = 405) of participants believed that AI should be taught at the undergraduate level. The survey suggests that there is insufficient training about AI in healthcare in Pakistan despite the interest of many in this area. Future work in developing a tailored curriculum regarding AI in healthcare will help bridge the gap between the interest in use of AI and training.
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Affiliation(s)
| | - Zahra Hoodbhoy
- Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Pakistan
| | - M. A. Rehman Siddiqui
- Department of Ophthalmology and Visual Sciences, The Aga Khan University Hospital, Karachi, Pakistan
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14
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Ng JY, Cramer H, Lee MS, Moher D. Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare. Integr Med Res 2024; 13:101024. [PMID: 38384497 PMCID: PMC10879672 DOI: 10.1016/j.imr.2024.101024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/02/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
The convergence of traditional, complementary, and integrative medicine (TCIM) with artificial intelligence (AI) is a promising frontier in healthcare. TCIM is a patient-centric approach that combines conventional medicine with complementary therapies, emphasizing holistic well-being. AI can revolutionize healthcare through data-driven decision-making and personalized treatment plans. This article explores how AI technologies can complement and enhance TCIM, aligning with the shared objectives of researchers from both fields in improving patient outcomes, enhancing care quality, and promoting holistic wellness. This integration of TCIM and AI introduces exciting opportunities but also noteworthy challenges. AI may augment TCIM by assisting in early disease detection, providing personalized treatment plans, predicting health trends, and enhancing patient engagement. Challenges at the intersection of AI and TCIM include data privacy and security, regulatory complexities, maintaining the human touch in patient-provider relationships, and mitigating bias in AI algorithms. Patients' trust, informed consent, and legal accountability are all essential considerations. Future directions in AI-enhanced TCIM include advanced personalized medicine, understanding the efficacy of herbal remedies, and studying patient-provider interactions. Research on bias mitigation, patient acceptance, and trust in AI-driven TCIM healthcare is crucial. In this article, we outlined that the merging of TCIM and AI holds great promise in enhancing healthcare delivery, personalizing treatment plans, preventive care, and patient engagement. Addressing challenges and fostering collaboration between AI experts, TCIM practitioners, and policymakers, however, is vital to harnessing the full potential of this integration.
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Affiliation(s)
- Jeremy Y. Ng
- Centre for Journalology, Ottawa Hospital Research Institute, Ottawa, Canada
- Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany
- Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany
| | - Holger Cramer
- Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany
- Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany
| | - Myeong Soo Lee
- KM Science Research Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - David Moher
- Centre for Journalology, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
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15
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Adigwe OP, Onavbavba G, Sanyaolu SE. Exploring the matrix: knowledge, perceptions and prospects of artificial intelligence and machine learning in Nigerian healthcare. Front Artif Intell 2024; 6:1293297. [PMID: 38314120 PMCID: PMC10834749 DOI: 10.3389/frai.2023.1293297] [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: 09/12/2023] [Accepted: 11/21/2023] [Indexed: 02/06/2024] Open
Abstract
Background Artificial intelligence technology can be applied in several aspects of healthcare delivery and its integration into the Nigerian healthcare value chain is expected to bring about new opportunities. This study aimed at assessing the knowledge and perception of healthcare professionals in Nigeria regarding the application of artificial intelligence and machine learning in the health sector. Methods A cross-sectional study was undertaken amongst healthcare professionals in Nigeria with the use of a questionnaire. Data were collected across the six geopolitical zones in the Country using a stratified multistage sampling method. Descriptive and inferential statistical analyses were undertaken for the data obtained. Results Female participants (55.7%) were slightly higher in proportion compared to the male respondents (44.3%). Pharmacists accounted for 27.7% of the participants, and this was closely followed by medical doctors (24.5%) and nurses (19.3%). The majority of the respondents (57.2%) reported good knowledge regarding artificial intelligence and machine learning, about a third of the participants (32.2%) were of average knowledge, and 10.6% of the sample had poor knowledge. More than half of the respondents (57.8%) disagreed with the notion that the adoption of artificial intelligence in the Nigerian healthcare sector could result in job losses. Two-thirds of the participants (66.7%) were of the view that the integration of artificial intelligence in healthcare will augment human intelligence. Three-quarters (77%) of the respondents agreed that the use of machine learning in Nigerian healthcare could facilitate efficient service delivery. Conclusion This study provides novel insights regarding healthcare professionals' knowledge and perception with respect to the application of artificial intelligence and machine learning in healthcare. The emergent findings from this study can guide government and policymakers in decision-making as regards deployment of artificial intelligence and machine learning for healthcare delivery.
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Affiliation(s)
- Obi Peter Adigwe
- National Institute for Pharmaceutical Research and Development, Abuja, Nigeria
| | - Godspower Onavbavba
- National Institute for Pharmaceutical Research and Development, Abuja, Nigeria
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Rony MKK, Parvin MR, Wahiduzzaman M, Debnath M, Bala SD, Kayesh I. "I Wonder if my Years of Training and Expertise Will be Devalued by Machines": Concerns About the Replacement of Medical Professionals by Artificial Intelligence. SAGE Open Nurs 2024; 10:23779608241245220. [PMID: 38596508 PMCID: PMC11003342 DOI: 10.1177/23779608241245220] [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: 11/10/2023] [Revised: 03/08/2024] [Accepted: 03/15/2024] [Indexed: 04/11/2024] Open
Abstract
Background The rapid integration of artificial intelligence (AI) into healthcare has raised concerns among healthcare professionals about the potential displacement of human medical professionals by AI technologies. However, the apprehensions and perspectives of healthcare workers regarding the potential substitution of them with AI are unknown. Objective This qualitative research aimed to investigate healthcare workers' concerns about artificial intelligence replacing medical professionals. Methods A descriptive and exploratory research design was employed, drawing upon the Technology Acceptance Model (TAM), Technology Threat Avoidance Theory, and Sociotechnical Systems Theory as theoretical frameworks. Participants were purposively sampled from various healthcare settings, representing a diverse range of roles and backgrounds. Data were collected through individual interviews and focus group discussions, followed by thematic analysis. Results The analysis revealed seven key themes reflecting healthcare workers' concerns, including job security and economic concerns; trust and acceptance of AI; ethical and moral dilemmas; quality of patient care; workforce role redefinition and training; patient-provider relationships; healthcare policy and regulation. Conclusions This research underscores the multifaceted concerns of healthcare workers regarding the increasing role of AI in healthcare. Addressing job security, fostering trust, addressing ethical dilemmas, and redefining workforce roles are crucial factors to consider in the successful integration of AI into healthcare. Healthcare policy and regulation must be developed to guide this transformation while maintaining the quality of patient care and preserving patient-provider relationships. The study findings offer insights for policymakers and healthcare institutions to navigate the evolving landscape of AI in healthcare while addressing the concerns of healthcare professionals.
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Affiliation(s)
- Moustaq Karim Khan Rony
- Master of Public Health, Bangladesh Open University, Gazipur, Bangladesh
- Institute of Social Welfare and Research, University of Dhaka, Dhaka, Bangladesh
| | - Mst. Rina Parvin
- Armed Forces Nursing Service, Major at Bangladesh Army (AFNS Officer), Combined Military Hospital, Dhaka, Bangladesh
| | - Md. Wahiduzzaman
- School of Medical Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Mitun Debnath
- Master of Public Health, National Institute of Preventive and Social Medicine, Dhaka, Bangladesh
| | - Shuvashish Das Bala
- College of Nursing, International University of Business Agriculture and Technology, Dhaka, Bangladesh
| | - Ibne Kayesh
- Institute of Social Welfare and Research, University of Dhaka, Dhaka, Bangladesh
- Faculty of Graduate Studies, University of Kelaniya, Colombo, Sri Lanka
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17
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Oppermann I. Regulating AI for health. BMJ Health Care Inform 2023; 30:e100931. [PMID: 38135294 DOI: 10.1136/bmjhci-2023-100931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023] Open
Affiliation(s)
- Ian Oppermann
- University of Technology, Sydney, New South Wales, Australia
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18
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He X, Zheng X, Ding H. Existing Barriers Faced by and Future Design Recommendations for Direct-to-Consumer Health Care Artificial Intelligence Apps: Scoping Review. J Med Internet Res 2023; 25:e50342. [PMID: 38109173 PMCID: PMC10758939 DOI: 10.2196/50342] [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: 07/01/2023] [Revised: 09/20/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND Direct-to-consumer (DTC) health care artificial intelligence (AI) apps hold the potential to bridge the spatial and temporal disparities in health care resources, but they also come with individual and societal risks due to AI errors. Furthermore, the manner in which consumers interact directly with health care AI is reshaping traditional physician-patient relationships. However, the academic community lacks a systematic comprehension of the research overview for such apps. OBJECTIVE This paper systematically delineated and analyzed the characteristics of included studies, identified existing barriers and design recommendations for DTC health care AI apps mentioned in the literature and also provided a reference for future design and development. METHODS This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines and was conducted according to Arksey and O'Malley's 5-stage framework. Peer-reviewed papers on DTC health care AI apps published until March 27, 2023, in Web of Science, Scopus, the ACM Digital Library, IEEE Xplore, PubMed, and Google Scholar were included. The papers were analyzed using Braun and Clarke's reflective thematic analysis approach. RESULTS Of the 2898 papers retrieved, 32 (1.1%) covering this emerging field were included. The included papers were recently published (2018-2023), and most (23/32, 72%) were from developed countries. The medical field was mostly general practice (8/32, 25%). In terms of users and functionalities, some apps were designed solely for single-consumer groups (24/32, 75%), offering disease diagnosis (14/32, 44%), health self-management (8/32, 25%), and health care information inquiry (4/32, 13%). Other apps connected to physicians (5/32, 16%), family members (1/32, 3%), nursing staff (1/32, 3%), and health care departments (2/32, 6%), generally to alert these groups to abnormal conditions of consumer users. In addition, 8 barriers and 6 design recommendations related to DTC health care AI apps were identified. Some more subtle obstacles that are particularly worth noting and corresponding design recommendations in consumer-facing health care AI systems, including enhancing human-centered explainability, establishing calibrated trust and addressing overtrust, demonstrating empathy in AI, improving the specialization of consumer-grade products, and expanding the diversity of the test population, were further discussed. CONCLUSIONS The booming DTC health care AI apps present both risks and opportunities, which highlights the need to explore their current status. This paper systematically summarized and sorted the characteristics of the included studies, identified existing barriers faced by, and made future design recommendations for such apps. To the best of our knowledge, this is the first study to systematically summarize and categorize academic research on these apps. Future studies conducting the design and development of such systems could refer to the results of this study, which is crucial to improve the health care services provided by DTC health care AI apps.
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Affiliation(s)
- Xin He
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Xi Zheng
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Huiyuan Ding
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
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Rollwage M, Habicht J, Juechems K, Carrington B, Viswanathan S, Stylianou M, Hauser TU, Harper R. Using Conversational AI to Facilitate Mental Health Assessments and Improve Clinical Efficiency Within Psychotherapy Services: Real-World Observational Study. JMIR AI 2023; 2:e44358. [PMID: 38875569 PMCID: PMC11041479 DOI: 10.2196/44358] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 05/31/2023] [Accepted: 10/20/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Most mental health care providers face the challenge of increased demand for psychotherapy in the absence of increased funding or staffing. To overcome this supply-demand imbalance, care providers must increase the efficiency of service delivery. OBJECTIVE In this study, we examined whether artificial intelligence (AI)-enabled digital solutions can help mental health care practitioners to use their time more efficiently, and thus reduce strain on services and improve patient outcomes. METHODS In this study, we focused on the use of an AI solution (Limbic Access) to support initial patient referral and clinical assessment within the UK's National Health Service. Data were collected from 9 Talking Therapies services across England, comprising 64,862 patients. RESULTS We showed that the use of this AI solution improves clinical efficiency by reducing the time clinicians spend on mental health assessments. Furthermore, we found improved outcomes for patients using the AI solution in several key metrics, such as reduced wait times, reduced dropout rates, improved allocation to appropriate treatment pathways, and, most importantly, improved recovery rates. When investigating the mechanism by which the AI solution achieved these improvements, we found that the provision of clinically relevant information ahead of clinical assessment was critical for these observed effects. CONCLUSIONS Our results emphasize the utility of using AI solutions to support the mental health workforce, further highlighting the potential of AI solutions to increase the efficiency of care delivery and improve clinical outcomes for patients.
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Affiliation(s)
| | | | | | | | | | | | - Tobias U Hauser
- Limbic Limited, London, United Kingdom
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
- Department of Psychiatry and Psychotherapy, Medical School and University Hospital, Eberhard Karls University of Tübingen, Tubingen, Germany
- German Center for Mental Health (DZPG), Tubingen, Germany
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20
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Ennab F. Teaching clinical empathy skills in medical education: Can ChatGPT assist the educator? MEDICAL TEACHER 2023; 45:1440-1441. [PMID: 37591768 DOI: 10.1080/0142159x.2023.2247144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Affiliation(s)
- Farah Ennab
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
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21
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Wilkens U, Lupp D, Langholf V. Configurations of human-centered AI at work: seven actor-structure engagements in organizations. Front Artif Intell 2023; 6:1272159. [PMID: 38028670 PMCID: PMC10664146 DOI: 10.3389/frai.2023.1272159] [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: 08/03/2023] [Accepted: 09/29/2023] [Indexed: 12/01/2023] Open
Abstract
Purpose The discourse on the human-centricity of AI at work needs contextualization. The aim of this study is to distinguish prevalent criteria of human-centricity for AI applications in the scientific discourse and to relate them to the work contexts for which they are specifically intended. This leads to configurations of actor-structure engagements that foster human-centricity in the workplace. Theoretical foundation The study applies configurational theory to sociotechnical systems' analysis of work settings. The assumption is that different approaches to promote human-centricity coexist, depending on the stakeholders responsible for their application. Method The exploration of criteria indicating human-centricity and their synthesis into configurations is based on a cross-disciplinary literature review following a systematic search strategy and a deductive-inductive qualitative content analysis of 101 research articles. Results The article outlines eight criteria of human-centricity, two of which face challenges of human-centered technology development (trustworthiness and explainability), three challenges of human-centered employee development (prevention of job loss, health, and human agency and augmentation), and three challenges of human-centered organizational development (compensation of systems' weaknesses, integration of user-domain knowledge, accountability, and safety culture). The configurational theory allows contextualization of these criteria from a higher-order perspective and leads to seven configurations of actor-structure engagements in terms of engagement for (1) data and technostructure, (2) operational process optimization, (3) operators' employment, (4) employees' wellbeing, (5) proficiency, (6) accountability, and (7) interactive cross-domain design. Each has one criterion of human-centricity in the foreground. Trustworthiness does not build its own configuration but is proposed to be a necessary condition in all seven configurations. Discussion The article contextualizes the overall debate on human-centricity and allows us to specify stakeholder-related engagements and how these complement each other. This is of high value for practitioners bringing human-centricity to the workplace and allows them to compare which criteria are considered in transnational declarations, international norms and standards, or company guidelines.
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Affiliation(s)
- Uta Wilkens
- Institute of Work Science, Ruhr University Bochum, Bochum, Germany
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22
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Gala D, Makaryus AN. The Utility of Language Models in Cardiology: A Narrative Review of the Benefits and Concerns of ChatGPT-4. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6438. [PMID: 37568980 PMCID: PMC10419098 DOI: 10.3390/ijerph20156438] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/30/2023] [Accepted: 07/19/2023] [Indexed: 08/13/2023]
Abstract
Artificial intelligence (AI) and language models such as ChatGPT-4 (Generative Pretrained Transformer) have made tremendous advances recently and are rapidly transforming the landscape of medicine. Cardiology is among many of the specialties that utilize AI with the intention of improving patient care. Generative AI, with the use of its advanced machine learning algorithms, has the potential to diagnose heart disease and recommend management options suitable for the patient. This may lead to improved patient outcomes not only by recommending the best treatment plan but also by increasing physician efficiency. Language models could assist physicians with administrative tasks, allowing them to spend more time on patient care. However, there are several concerns with the use of AI and language models in the field of medicine. These technologies may not be the most up-to-date with the latest research and could provide outdated information, which may lead to an adverse event. Secondly, AI tools can be expensive, leading to increased healthcare costs and reduced accessibility to the general population. There is also concern about the loss of the human touch and empathy as AI becomes more mainstream. Healthcare professionals would need to be adequately trained to utilize these tools. While AI and language models have many beneficial traits, all healthcare providers need to be involved and aware of generative AI so as to assure its optimal use and mitigate any potential risks and challenges associated with its implementation. In this review, we discuss the various uses of language models in the field of cardiology.
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Affiliation(s)
- Dhir Gala
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands;
| | - Amgad N. Makaryus
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, 500 Hofstra Blvd., Hempstead, NY 11549, USA
- Department of Cardiology, Nassau University Medical Center, Hempstead, NY 11554, USA
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23
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Zhao J. Nursing in a posthuman era: Towards a technology-integrated ecosystem of care. Int J Nurs Sci 2023; 10:398-402. [PMID: 37545768 PMCID: PMC10401335 DOI: 10.1016/j.ijnss.2023.06.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/17/2023] [Accepted: 06/17/2023] [Indexed: 08/08/2023] Open
Abstract
The healthcare sector has undergone significant transformation due to the rapid advancements in artificial intelligence and biotechnologies, presenting both opportunities and threats to the nursing profession. Posthumanism, as a critical perspective challenging anthropocentrism and emphasizing the increasingly blurred boundaries between humans and nonhumans, provides a novel lens to comprehend these technological advancements. In this commentary paper, I draw on the posthuman discourse to argue that in light of these technological forces, we need to contemplate the core values and fundamental patterns of knowing within the nursing discipline, reconfigure nursing scope, redefine its relations with other agents, and embrace a technology-integrated ecosystem of care.
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Affiliation(s)
- Junqiang Zhao
- Waypoint Research Institute, Waypoint Centre for Mental Health Care, Ontario, Canada
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24
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Morrow E, Ross F, Mason C. Editorial: Education and learning for digital health. Front Digit Health 2023; 5:1165504. [PMID: 37051379 PMCID: PMC10084883 DOI: 10.3389/fdgth.2023.1165504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/09/2023] [Indexed: 03/29/2023] Open
Affiliation(s)
- Elizabeth Morrow
- Research Support Northern Ireland, Downpatrick, United Kingdom
- Correspondence: Elizabeth Morrow
| | - Fiona Ross
- Faculty of Health, Science, Social Care and Education, Kingston University London, London, United Kingdom
| | - Cindy Mason
- AI Researcher (Independent), Palo Alto, CA, United States
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