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Mehari M, Sibih Y, Dada A, Chang SM, Wen PY, Molinaro AM, Chukwueke UN, Budhu JA, Jackson S, McFaline-Figueroa JR, Porter A, Hervey-Jumper SL. Enhancing neuro-oncology care through equity-driven applications of artificial intelligence. Neuro Oncol 2024:noae127. [PMID: 39159285 DOI: 10.1093/neuonc/noae127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/21/2024] Open
Abstract
The disease course and clinical outcome for brain tumor patients depend not only on the molecular and histological features of the tumor but also on the patient's demographics and social determinants of health. While current investigations in neuro-oncology have broadly utilized artificial intelligence (AI) to enrich tumor diagnosis and more accurately predict treatment response, postoperative complications, and survival, equity-driven applications of AI have been limited. However, AI applications to advance health equity in the broader medical field have the potential to serve as practical blueprints to address known disparities in neuro-oncologic care. In this consensus review, we will describe current applications of AI in neuro-oncology, postulate viable AI solutions for the most pressing inequities in neuro-oncology based on broader literature, propose a framework for the effective integration of equity into AI-based neuro-oncology research, and close with the limitations of AI.
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Affiliation(s)
- Mulki Mehari
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Youssef Sibih
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Abraham Dada
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Susan M Chang
- Division of Neuro-Oncology, University of California San Francisco and Weill Institute for Neurosciences, San Francisco, California, USA
| | - Patrick Y Wen
- Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Annette M Molinaro
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Ugonma N Chukwueke
- Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Joshua A Budhu
- Department of Neurology, Memorial Sloan Kettering Cancer Center, Department of Neurology, Weill Cornell Medicine, Joan & Sanford I. Weill Medical College of Cornell University, New York, New York, USA
| | - Sadhana Jackson
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, Pediatric Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - J Ricardo McFaline-Figueroa
- Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Alyx Porter
- Division of Neuro-Oncology, Department of Neurology, Mayo Clinic, Phoenix, Arizona, USA
| | - Shawn L Hervey-Jumper
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
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Sallam M, Al-Mahzoum K, Alshuaib O, Alhajri H, Alotaibi F, Alkhurainej D, Al-Balwah MY, Barakat M, Egger J. Language discrepancies in the performance of generative artificial intelligence models: an examination of infectious disease queries in English and Arabic. BMC Infect Dis 2024; 24:799. [PMID: 39118057 PMCID: PMC11308449 DOI: 10.1186/s12879-024-09725-y] [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: 01/02/2024] [Accepted: 08/06/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND Assessment of artificial intelligence (AI)-based models across languages is crucial to ensure equitable access and accuracy of information in multilingual contexts. This study aimed to compare AI model efficiency in English and Arabic for infectious disease queries. METHODS The study employed the METRICS checklist for the design and reporting of AI-based studies in healthcare. The AI models tested included ChatGPT-3.5, ChatGPT-4, Bing, and Bard. The queries comprised 15 questions on HIV/AIDS, tuberculosis, malaria, COVID-19, and influenza. The AI-generated content was assessed by two bilingual experts using the validated CLEAR tool. RESULTS In comparing AI models' performance in English and Arabic for infectious disease queries, variability was noted. English queries showed consistently superior performance, with Bard leading, followed by Bing, ChatGPT-4, and ChatGPT-3.5 (P = .012). The same trend was observed in Arabic, albeit without statistical significance (P = .082). Stratified analysis revealed higher scores for English in most CLEAR components, notably in completeness, accuracy, appropriateness, and relevance, especially with ChatGPT-3.5 and Bard. Across the five infectious disease topics, English outperformed Arabic, except for flu queries in Bing and Bard. The four AI models' performance in English was rated as "excellent", significantly outperforming their "above-average" Arabic counterparts (P = .002). CONCLUSIONS Disparity in AI model performance was noticed between English and Arabic in response to infectious disease queries. This language variation can negatively impact the quality of health content delivered by AI models among native speakers of Arabic. This issue is recommended to be addressed by AI developers, with the ultimate goal of enhancing health outcomes.
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Affiliation(s)
- Malik Sallam
- Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman, 11942, Jordan.
- Department of Translational Medicine, Faculty of Medicine, Lund University, Malmö, 22184, Sweden.
- Department of Clinical Laboratories and Forensic Medicine, Jordan University Hospital, Queen Rania Al-Abdullah Street-Aljubeiha, P.O. Box: 13046, Amman, Jordan.
| | | | - Omaima Alshuaib
- School of Medicine, The University of Jordan, Amman, 11942, Jordan
| | - Hawajer Alhajri
- School of Medicine, The University of Jordan, Amman, 11942, Jordan
| | - Fatmah Alotaibi
- School of Medicine, The University of Jordan, Amman, 11942, Jordan
| | | | | | - Muna Barakat
- Department of Clinical Pharmacy and Therapeutics, Faculty of Pharmacy, Applied Science Private University, Amman, 11931, Jordan
- MEU Research Unit, Middle East University, Amman, 11831, Jordan
| | - Jan Egger
- Institute for AI in Medicine (IKIM), University Medicine Essen (AöR), Essen, Germany
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Syed W, Babelghaith SD, Al-Arifi MN. Assessment of Saudi Public Perceptions and Opinions towards Artificial Intelligence in Health Care. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:938. [PMID: 38929555 PMCID: PMC11205650 DOI: 10.3390/medicina60060938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 05/31/2024] [Accepted: 06/02/2024] [Indexed: 06/28/2024]
Abstract
Background and Objectives: The healthcare system in Saudi Arabia is growing rapidly with the utilization of advanced technologies. Therefore, this study aimed to assess the Saudi public perceptions and opinions towards artificial intelligence (AI) in health care. Materials and Methods: This cross-sectional web-based questionnaire study was conducted between January and April 2024. Data were analyzed from 830 participants. The perceptions of the public towards AI were assessed using 21-item questionnaires. Results: Among the respondents, 69.4% were males and 46% of them were aged above 41 years old. A total of 84.1% of the participants knew about AI, while 61.1% of them believed that AI is a tool that helps healthcare professionals, and 12.5% of them thought that AI may replace the physician, pharmacist, or nurse in the healthcare system. With regard to opinion on the widespread use of AI, 45.8% of the study population believed that healthcare professionals will be improved with the widespread use of artificial intelligence. The mean perception score of AI among males was 38.4 (SD = 6.1) and this was found to be higher than for females at 37.7 (SD = 5.3); however, no significant difference was observed (p = 0.072). Similarly, the mean perception score was higher among young adults aged between 20 and 25 years at 38.9 (SD = 6.1) compared to other age groups, but indicating no significant association between them (p = 0.198). Conclusions: The results showed that the Saudi public had a favorable opinion and perceptions of AI in health care. This suggests that health management recommendations should be made regarding how to successfully integrate and use medical AI while maintaining patient safety.
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Affiliation(s)
- Wajid Syed
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia; (S.D.B.); (M.N.A.-A.)
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Şenoymak MC, Erbatur NH, Şenoymak İ, Fırat SN. The Role of Artificial Intelligence in Endocrine Management: Assessing ChatGPT's Responses to Prolactinoma Queries. J Pers Med 2024; 14:330. [PMID: 38672957 PMCID: PMC11051052 DOI: 10.3390/jpm14040330] [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: 02/21/2024] [Revised: 03/15/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024] Open
Abstract
This research investigates the utility of Chat Generative Pre-trained Transformer (ChatGPT) in addressing patient inquiries related to hyperprolactinemia and prolactinoma. A set of 46 commonly asked questions from patients with prolactinoma were presented to ChatGPT and responses were evaluated for accuracy with a 6-point Likert scale (1: completely inaccurate to 6: completely accurate) and adequacy with a 5-point Likert scale (1: completely inadequate to 5: completely adequate). Two independent endocrinologists assessed the responses, based on international guidelines. Questions were categorized into groups including general information, diagnostic process, treatment process, follow-up, and pregnancy period. The median accuracy score was 6.0 (IQR, 5.4-6.0), and the adequacy score was 4.5 (IQR, 3.5-5.0). The lowest accuracy and adequacy score assigned by both evaluators was two. Significant agreement was observed between the evaluators, demonstrated by a weighted κ of 0.68 (p = 0.08) for accuracy and a κ of 0.66 (p = 0.04) for adequacy. The Kruskal-Wallis tests revealed statistically significant differences among the groups for accuracy (p = 0.005) and adequacy (p = 0.023). The pregnancy period group had the lowest accuracy score and both pregnancy period and follow-up groups had the lowest adequacy score. In conclusion, ChatGPT demonstrated commendable responses in addressing prolactinoma queries; however, certain limitations were observed, particularly in providing accurate information related to the pregnancy period, emphasizing the need for refining its capabilities in medical contexts.
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Affiliation(s)
- Mustafa Can Şenoymak
- Department of Endocrinology and Metabolism, University of Health Sciences Sultan, Abdulhamid Han Training and Research Hospital, Istanbul 34668, Turkey
| | - Nuriye Hale Erbatur
- Department of Endocrinology and Metabolism, University of Health Sciences Sultan, Abdulhamid Han Training and Research Hospital, Istanbul 34668, Turkey
| | - İrem Şenoymak
- Family Medicine Department, Usküdar State Hospital, Istanbul 34662, Turkey
| | - Sevde Nur Fırat
- Department of Endocrinology and Metabolism, University of Health Sciences, Ankara Training and Research Hospital, Ankara 06230, Turkey
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Larson HJ, Lin L. Generative artificial intelligence can have a role in combating vaccine hesitancy. BMJ 2024; 384:q69. [PMID: 38228351 PMCID: PMC10789191 DOI: 10.1136/bmj.q69] [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] [Indexed: 01/18/2024]
Affiliation(s)
- Heidi J Larson
- London School of Hygiene and Tropical Medicine, London, UK
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA
| | - Leesa Lin
- London School of Hygiene and Tropical Medicine, London, UK
- Laboratory of Data Discovery for Health, Science Park, Hong Kong
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