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Gupta DK, Tiwari A, Yadav Y, Soni P, Joshi M. Ensuring safety and efficacy in combination products: regulatory challenges and best practices. FRONTIERS IN MEDICAL TECHNOLOGY 2024; 6:1377443. [PMID: 39050909 PMCID: PMC11266060 DOI: 10.3389/fmedt.2024.1377443] [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: 01/27/2024] [Accepted: 04/23/2024] [Indexed: 07/27/2024] Open
Abstract
Combination products, amalgamating drugs, biologics, and medical devices, have revolutionized the healthcare landscape with their potential for innovative therapies. However, the intersection of diverse components within these products presents a complex regulatory environment, demanding rigorous attention to safety and efficacy. This article delves into the intricate landscape of regulatory considerations, safety, and efficacy assessments pertaining to combination products-a category at the intersection of drugs, devices, and biologics. The regulatory framework, primarily governed by the U.S. Food and Drug Administration (FDA), necessitates a nuanced classification determining the regulatory pathway. Collaboration between diverse regulatory centers, such as the Center for Drug Evaluation and Research (CDER) and the Center for Devices and Radiological Health (CDRH), underscores the integrated approach required for these innovative healthcare solutions. Safety considerations unravel the potential risks and adverse events associated with combining diverse components, emphasizing the need for robust risk assessment and mitigation strategies. The evaluation of efficacy involves sophisticated methodologies, clinical trials, and post-market surveillance, with recent advancements incorporating digital technologies. This comprehensive exploration aims to contribute to the evolving understanding and best practices in the regulatory and scientific realms, fostering collaboration and innovation in the development and assessment of combination products.
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Affiliation(s)
- Deepak Kumar Gupta
- Department of Pharmacy, Indira Gandhi National Tribal University, Amarkantak, India
| | - Akhilesh Tiwari
- Department of Pharmacy, Indira Gandhi National Tribal University, Amarkantak, India
| | - Yashraj Yadav
- Department of Pharmacology, AcropolisInstitute of Pharmaceutical Education and Research, Indore, India
| | - Pranay Soni
- Department of Pharmacy, Indira Gandhi National Tribal University, Amarkantak, India
| | - Megha Joshi
- Institute of Pharmacy, Vikram University, Ujjain, India
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Cheungpasitporn W, Thongprayoon C, Kashani KB. Artificial intelligence and machine learning's role in sepsis-associated acute kidney injury. Kidney Res Clin Pract 2024; 43:417-432. [PMID: 38934028 PMCID: PMC11237333 DOI: 10.23876/j.krcp.23.298] [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: 11/13/2023] [Accepted: 05/08/2024] [Indexed: 06/28/2024] Open
Abstract
Sepsis-associated acute kidney injury (SA-AKI) is a serious complication in critically ill patients, resulting in higher mortality, morbidity, and cost. The intricate pathophysiology of SA-AKI requires vigilant clinical monitoring and appropriate, prompt intervention. While traditional statistical analyses have identified severe risk factors for SA-AKI, the results have been inconsistent across studies. This has led to growing interest in leveraging artificial intelligence (AI) and machine learning (ML) to predict SA-AKI better. ML can uncover complex patterns beyond human discernment by analyzing vast datasets. Supervised learning models like XGBoost and RNN-LSTM have proven remarkably accurate at predicting SA-AKI onset and subsequent mortality, often surpassing traditional risk scores. Meanwhile, unsupervised learning reveals clinically relevant sub-phenotypes among diverse SA-AKI patients, enabling more tailored care. In addition, it potentially optimizes sepsis treatment to prevent SA-AKI through continual refinement based on patient outcomes. However, utilizing AI/ML presents ethical and practical challenges regarding data privacy, algorithmic biases, and regulatory compliance. AI/ML allows early risk detection, personalized management, optimal treatment strategies, and collaborative learning for SA-AKI management. Future directions include real-time patient monitoring, simulated data generation, and predictive algorithms for timely interventions. However, a smooth transition to clinical practice demands continuous model enhancements and rigorous regulatory oversight. In this article, we outlined the conventional methods used to address SA-AKI and explore how AI and ML can be applied to diagnose and manage SA-AKI, highlighting their potential to revolutionize SA-AKI care.
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Affiliation(s)
- Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Kianoush B Kashani
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
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Marianti L, Saputra R, Arizona, Arifah S, Fitri HU, Putra BJ. Increasing the scope of virtual reality in palliative care: Insights and future directions. Palliat Support Care 2024:1-2. [PMID: 38826055 DOI: 10.1017/s147895152400097x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Affiliation(s)
- Lena Marianti
- Universitas Islam Negeri Raden Fatah Palembang, South Sumatra, Indonesia
| | - Rikas Saputra
- Universitas Islam Negeri Raden Fatah Palembang, South Sumatra, Indonesia
| | - Arizona
- Universitas PGRI Palembang, Palembang, Indonesia
| | - Siti Arifah
- Universitas Darul Ulum Jombang, Jawa Timur, Indonesia
| | | | - Bela Janare Putra
- Universitas Islam Negeri Raden Fatah Palembang, South Sumatra, Indonesia
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Leypold T, Lingens LF, Beier JP, Boos AM. Integrating AI in Lipedema Management: Assessing the Efficacy of GPT-4 as a Consultation Assistant. Life (Basel) 2024; 14:646. [PMID: 38792666 PMCID: PMC11122530 DOI: 10.3390/life14050646] [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: 04/24/2024] [Revised: 05/09/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024] Open
Abstract
The role of artificial intelligence (AI) in healthcare is evolving, offering promising avenues for enhancing clinical decision making and patient management. Limited knowledge about lipedema often leads to patients being frequently misdiagnosed with conditions like lymphedema or obesity rather than correctly identifying lipedema. Furthermore, patients with lipedema often present with intricate and extensive medical histories, resulting in significant time consumption during consultations. AI could, therefore, improve the management of these patients. This research investigates the utilization of OpenAI's Generative Pre-Trained Transformer 4 (GPT-4), a sophisticated large language model (LLM), as an assistant in consultations for lipedema patients. Six simulated scenarios were designed to mirror typical patient consultations commonly encountered in a lipedema clinic. GPT-4 was tasked with conducting patient interviews to gather medical histories, presenting its findings, making preliminary diagnoses, and recommending further diagnostic and therapeutic actions. Advanced prompt engineering techniques were employed to refine the efficacy, relevance, and accuracy of GPT-4's responses. A panel of experts in lipedema treatment, using a Likert Scale, evaluated GPT-4's responses across six key criteria. Scoring ranged from 1 (lowest) to 5 (highest), with GPT-4 achieving an average score of 4.24, indicating good reliability and applicability in a clinical setting. This study is one of the initial forays into applying large language models like GPT-4 in specific clinical scenarios, such as lipedema consultations. It demonstrates the potential of AI in supporting clinical practices and emphasizes the continuing importance of human expertise in the medical field, despite ongoing technological advancements.
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Affiliation(s)
- Tim Leypold
- Department of Plastic Surgery, Hand Surgery–Burn Center, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany; (L.F.L.); (J.P.B.); (A.M.B.)
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Jin H, Lin Q, Lu J, Hu C, Lu B, Jiang N, Wu S, Li X. Evaluating the Effectiveness of a Generative Pretrained Transformer-Based Dietary Recommendation System in Managing Potassium Intake for Hemodialysis Patients. J Ren Nutr 2024:S1051-2276(24)00059-1. [PMID: 38615701 DOI: 10.1053/j.jrn.2024.04.001] [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: 01/23/2024] [Revised: 03/31/2024] [Accepted: 04/03/2024] [Indexed: 04/16/2024] Open
Abstract
OBJECTIVE Despite adequate dialysis, the prevalence of hyperkalemia in Chinese hemodialysis (HD) patients remains elevated. This study aims to evaluate the effectiveness of a dietary recommendation system driven by generative pretrained transformers (GPTs) in managing potassium levels in HD patients. METHODS We implemented a bespoke dietary guidance tool utilizing GPT technology. Patients undergoing HD at our center were enrolled in the study from October 2023 to November 2023. The intervention comprised of two distinct phases. Initially, patients were provided with conventional dietary education focused on potassium management in HD. Subsequently, in the second phase, they were introduced to a novel GPT-based dietary guidance tool. This artificial intelligence (AI)-powered tool offered real-time insights into the potassium content of various foods and personalized dietary suggestions. The effectiveness of the AI tool was evaluated by assessing the precision of its dietary recommendations. Additionally, we compared predialysis serum potassium levels and the proportion of patients with hyperkalemia among patients before and after the implementation of the GPT-based dietary guidance system. RESULTS In our analysis of 324 food photographs uploaded by 88 HD patients, the GPTs system evaluated potassium content with an overall accuracy of 65%. Notably, the accuracy was higher for high-potassium foods at 85%, while it stood at 48% for low-potassium foods. Furthermore, the study examined the effect of GPT-based dietary advice on patients' serum potassium levels, revealing a significant reduction in those adhering to GPTs recommendations compared to recipients of traditional dietary guidance (4.57 ± 0.76 mmol/L vs. 4.84 ± 0.94 mmol/L, P = .004). Importantly, compared to traditional dietary education, dietary education based on the GPTs tool reduced the proportion of hyperkalemia in HD patients from 39.8% to 25% (P = .036). CONCLUSION These results underscore the promising role of AI in improving dietary management for HD patients. Nonetheless, the study also points out the need for enhanced accuracy in identifying low potassium foods. It paves the way for future research, suggesting the incorporation of extensive nutritional databases and the assessment of long-term outcomes. This could potentially lead to more refined and effective dietary management strategies in HD care.
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Affiliation(s)
- Haijiao Jin
- Department of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Nephrology, Ningbo Hangzhou Bay Hospital, China; Molecular Cell Lab for Kidney Disease, Shanghai, China; Shanghai Peritoneal Dialysis Research Center, Shanghai, China; Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qisheng Lin
- Department of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Molecular Cell Lab for Kidney Disease, Shanghai, China; Shanghai Peritoneal Dialysis Research Center, Shanghai, China; Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jifang Lu
- Department of Nephrology, Ningbo Hangzhou Bay Hospital, China
| | - Cuirong Hu
- Department of Nephrology, Ningbo Hangzhou Bay Hospital, China
| | - Bohan Lu
- Department of Nephrology, Ningbo Hangzhou Bay Hospital, China
| | - Na Jiang
- Department of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Nephrology, Ningbo Hangzhou Bay Hospital, China; Molecular Cell Lab for Kidney Disease, Shanghai, China; Shanghai Peritoneal Dialysis Research Center, Shanghai, China; Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shaun Wu
- WORK Medical Technology Group LTD, Hangzhou, China
| | - Xiaoyang Li
- Department of Medical Education, Ruijin Hospital Affifiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Miao J, Thongprayoon C, Suppadungsuk S, Krisanapan P, Radhakrishnan Y, Cheungpasitporn W. Chain of Thought Utilization in Large Language Models and Application in Nephrology. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:148. [PMID: 38256408 PMCID: PMC10819595 DOI: 10.3390/medicina60010148] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 12/31/2023] [Accepted: 01/11/2024] [Indexed: 01/24/2024]
Abstract
Chain-of-thought prompting enhances the abilities of large language models (LLMs) significantly. It not only makes these models more specific and context-aware but also impacts the wider field of artificial intelligence (AI). This approach broadens the usability of AI, increases its efficiency, and aligns it more closely with human thinking and decision-making processes. As we improve this method, it is set to become a key element in the future of AI, adding more purpose, precision, and ethical consideration to these technologies. In medicine, the chain-of-thought prompting is especially beneficial. Its capacity to handle complex information, its logical and sequential reasoning, and its suitability for ethically and context-sensitive situations make it an invaluable tool for healthcare professionals. Its role in enhancing medical care and research is expected to grow as we further develop and use this technique. Chain-of-thought prompting bridges the gap between AI's traditionally obscure decision-making process and the clear, accountable standards required in healthcare. It does this by emulating a reasoning style familiar to medical professionals, fitting well into their existing practices and ethical codes. While solving AI transparency is a complex challenge, the chain-of-thought approach is a significant step toward making AI more comprehensible and trustworthy in medicine. This review focuses on understanding the workings of LLMs, particularly how chain-of-thought prompting can be adapted for nephrology's unique requirements. It also aims to thoroughly examine the ethical aspects, clarity, and future possibilities, offering an in-depth view of the exciting convergence of these areas.
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Affiliation(s)
- Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.)
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.)
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.)
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
- Division of Nephrology, Department of Internal Medicine, Thammasat University Hospital, Pathum Thani 12120, Thailand
| | - Yeshwanter Radhakrishnan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.)
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Miao J, Thongprayoon C, Garcia Valencia OA, Krisanapan P, Sheikh MS, Davis PW, Mekraksakit P, Suarez MG, Craici IM, Cheungpasitporn W. Performance of ChatGPT on Nephrology Test Questions. Clin J Am Soc Nephrol 2024; 19:35-43. [PMID: 37851468 PMCID: PMC10843340 DOI: 10.2215/cjn.0000000000000330] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 10/12/2023] [Indexed: 10/19/2023]
Abstract
BACKGROUND ChatGPT is a novel tool that allows people to engage in conversations with an advanced machine learning model. ChatGPT's performance in the US Medical Licensing Examination is comparable with a successful candidate's performance. However, its performance in the nephrology field remains undetermined. This study assessed ChatGPT's capabilities in answering nephrology test questions. METHODS Questions sourced from Nephrology Self-Assessment Program and Kidney Self-Assessment Program were used, each with multiple-choice single-answer questions. Questions containing visual elements were excluded. Each question bank was run twice using GPT-3.5 and GPT-4. Total accuracy rate, defined as the percentage of correct answers obtained by ChatGPT in either the first or second run, and the total concordance, defined as the percentage of identical answers provided by ChatGPT during both runs, regardless of their correctness, were used to assess its performance. RESULTS A comprehensive assessment was conducted on a set of 975 questions, comprising 508 questions from Nephrology Self-Assessment Program and 467 from Kidney Self-Assessment Program. GPT-3.5 resulted in a total accuracy rate of 51%. Notably, the employment of Nephrology Self-Assessment Program yielded a higher accuracy rate compared with Kidney Self-Assessment Program (58% versus 44%; P < 0.001). The total concordance rate across all questions was 78%, with correct answers exhibiting a higher concordance rate (84%) compared with incorrect answers (73%) ( P < 0.001). When examining various nephrology subfields, the total accuracy rates were relatively lower in electrolyte and acid-base disorder, glomerular disease, and kidney-related bone and stone disorders. The total accuracy rate of GPT-4's response was 74%, higher than GPT-3.5 ( P < 0.001) but remained below the passing threshold and average scores of nephrology examinees (77%). CONCLUSIONS ChatGPT exhibited limitations regarding accuracy and repeatability when addressing nephrology-related questions. Variations in performance were evident across various subfields.
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Affiliation(s)
- Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota
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Miao J, Thongprayoon C, Suppadungsuk S, Garcia Valencia OA, Qureshi F, Cheungpasitporn W. Ethical Dilemmas in Using AI for Academic Writing and an Example Framework for Peer Review in Nephrology Academia: A Narrative Review. Clin Pract 2023; 14:89-105. [PMID: 38248432 PMCID: PMC10801601 DOI: 10.3390/clinpract14010008] [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: 11/15/2023] [Revised: 12/23/2023] [Accepted: 12/28/2023] [Indexed: 01/23/2024] Open
Abstract
The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI's capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an "AI scrutiny" phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI's role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI's involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI's capabilities while upholding stringent academic standards can be envisioned.
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Affiliation(s)
- Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.); (O.A.G.V.); (F.Q.); (W.C.)
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.); (O.A.G.V.); (F.Q.); (W.C.)
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.); (O.A.G.V.); (F.Q.); (W.C.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bang Phli 10540, Samut Prakan, Thailand
| | - Oscar A. Garcia Valencia
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.); (O.A.G.V.); (F.Q.); (W.C.)
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.); (O.A.G.V.); (F.Q.); (W.C.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.); (O.A.G.V.); (F.Q.); (W.C.)
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Miao J, Thongprayoon C, Suppadungsuk S, Garcia Valencia OA, Qureshi F, Cheungpasitporn W. Innovating Personalized Nephrology Care: Exploring the Potential Utilization of ChatGPT. J Pers Med 2023; 13:1681. [PMID: 38138908 PMCID: PMC10744377 DOI: 10.3390/jpm13121681] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/02/2023] [Accepted: 12/02/2023] [Indexed: 12/24/2023] Open
Abstract
The rapid advancement of artificial intelligence (AI) technologies, particularly machine learning, has brought substantial progress to the field of nephrology, enabling significant improvements in the management of kidney diseases. ChatGPT, a revolutionary language model developed by OpenAI, is a versatile AI model designed to engage in meaningful and informative conversations. Its applications in healthcare have been notable, with demonstrated proficiency in various medical knowledge assessments. However, ChatGPT's performance varies across different medical subfields, posing challenges in nephrology-related queries. At present, comprehensive reviews regarding ChatGPT's potential applications in nephrology remain lacking despite the surge of interest in its role in various domains. This article seeks to fill this gap by presenting an overview of the integration of ChatGPT in nephrology. It discusses the potential benefits of ChatGPT in nephrology, encompassing dataset management, diagnostics, treatment planning, and patient communication and education, as well as medical research and education. It also explores ethical and legal concerns regarding the utilization of AI in medical practice. The continuous development of AI models like ChatGPT holds promise for the healthcare realm but also underscores the necessity of thorough evaluation and validation before implementing AI in real-world medical scenarios. This review serves as a valuable resource for nephrologists and healthcare professionals interested in fully utilizing the potential of AI in innovating personalized nephrology care.
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Affiliation(s)
- Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (C.T.); (S.S.); (O.A.G.V.); (F.Q.)
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (C.T.); (S.S.); (O.A.G.V.); (F.Q.)
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (C.T.); (S.S.); (O.A.G.V.); (F.Q.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Oscar A. Garcia Valencia
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (C.T.); (S.S.); (O.A.G.V.); (F.Q.)
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (C.T.); (S.S.); (O.A.G.V.); (F.Q.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (C.T.); (S.S.); (O.A.G.V.); (F.Q.)
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Aiumtrakul N, Thongprayoon C, Suppadungsuk S, Krisanapan P, Miao J, Qureshi F, Cheungpasitporn W. Navigating the Landscape of Personalized Medicine: The Relevance of ChatGPT, BingChat, and Bard AI in Nephrology Literature Searches. J Pers Med 2023; 13:1457. [PMID: 37888068 PMCID: PMC10608326 DOI: 10.3390/jpm13101457] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 09/29/2023] [Accepted: 09/29/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Literature reviews are foundational to understanding medical evidence. With AI tools like ChatGPT, Bing Chat and Bard AI emerging as potential aids in this domain, this study aimed to individually assess their citation accuracy within Nephrology, comparing their performance in providing precise. MATERIALS AND METHODS We generated the prompt to solicit 20 references in Vancouver style in each 12 Nephrology topics, using ChatGPT, Bing Chat and Bard. We verified the existence and accuracy of the provided references using PubMed, Google Scholar, and Web of Science. We categorized the validity of the references from the AI chatbot into (1) incomplete, (2) fabricated, (3) inaccurate, and (4) accurate. RESULTS A total of 199 (83%), 158 (66%) and 112 (47%) unique references were provided from ChatGPT, Bing Chat and Bard, respectively. ChatGPT provided 76 (38%) accurate, 82 (41%) inaccurate, 32 (16%) fabricated and 9 (5%) incomplete references. Bing Chat provided 47 (30%) accurate, 77 (49%) inaccurate, 21 (13%) fabricated and 13 (8%) incomplete references. In contrast, Bard provided 3 (3%) accurate, 26 (23%) inaccurate, 71 (63%) fabricated and 12 (11%) incomplete references. The most common error type across platforms was incorrect DOIs. CONCLUSIONS In the field of medicine, the necessity for faultless adherence to research integrity is highlighted, asserting that even small errors cannot be tolerated. The outcomes of this investigation draw attention to inconsistent citation accuracy across the different AI tools evaluated. Despite some promising results, the discrepancies identified call for a cautious and rigorous vetting of AI-sourced references in medicine. Such chatbots, before becoming standard tools, need substantial refinements to assure unwavering precision in their outputs.
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Affiliation(s)
- Noppawit Aiumtrakul
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA;
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (P.K.); (J.M.); (F.Q.); (W.C.)
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (P.K.); (J.M.); (F.Q.); (W.C.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (P.K.); (J.M.); (F.Q.); (W.C.)
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (P.K.); (J.M.); (F.Q.); (W.C.)
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (P.K.); (J.M.); (F.Q.); (W.C.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (P.K.); (J.M.); (F.Q.); (W.C.)
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Qarajeh A, Tangpanithandee S, Thongprayoon C, Suppadungsuk S, Krisanapan P, Aiumtrakul N, Garcia Valencia OA, Miao J, Qureshi F, Cheungpasitporn W. AI-Powered Renal Diet Support: Performance of ChatGPT, Bard AI, and Bing Chat. Clin Pract 2023; 13:1160-1172. [PMID: 37887080 PMCID: PMC10605499 DOI: 10.3390/clinpract13050104] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 09/15/2023] [Accepted: 09/25/2023] [Indexed: 10/28/2023] Open
Abstract
Patients with chronic kidney disease (CKD) necessitate specialized renal diets to prevent complications such as hyperkalemia and hyperphosphatemia. A comprehensive assessment of food components is pivotal, yet burdensome for healthcare providers. With evolving artificial intelligence (AI) technology, models such as ChatGPT, Bard AI, and Bing Chat can be instrumental in educating patients and assisting professionals. To gauge the efficacy of different AI models in discerning potassium and phosphorus content in foods, four AI models-ChatGPT 3.5, ChatGPT 4, Bard AI, and Bing Chat-were evaluated. A total of 240 food items, curated from the Mayo Clinic Renal Diet Handbook for CKD patients, were input into each model. These items were characterized by their potassium (149 items) and phosphorus (91 items) content. Each model was tasked to categorize the items into high or low potassium and high phosphorus content. The results were juxtaposed with the Mayo Clinic Renal Diet Handbook's recommendations. The concordance between repeated sessions was also evaluated to assess model consistency. Among the models tested, ChatGPT 4 displayed superior performance in identifying potassium content, correctly classifying 81% of the foods. It accurately discerned 60% of low potassium and 99% of high potassium foods. In comparison, ChatGPT 3.5 exhibited a 66% accuracy rate. Bard AI and Bing Chat models had an accuracy rate of 79% and 81%, respectively. Regarding phosphorus content, Bard AI stood out with a flawless 100% accuracy rate. ChatGPT 3.5 and Bing Chat recognized 85% and 89% of the high phosphorus foods correctly, while ChatGPT 4 registered a 77% accuracy rate. Emerging AI models manifest a diverse range of accuracy in discerning potassium and phosphorus content in foods suitable for CKD patients. ChatGPT 4, in particular, showed a marked improvement over its predecessor, especially in detecting potassium content. The Bard AI model exhibited exceptional precision for phosphorus identification. This study underscores the potential of AI models as efficient tools in renal dietary planning, though refinements are warranted for optimal utility.
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Affiliation(s)
- Ahmad Qarajeh
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
- Faculty of Medicine, University of Jordan, Amman 11942, Jordan
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Noppawit Aiumtrakul
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA;
| | - Oscar A. Garcia Valencia
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
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Garcia Valencia OA, Thongprayoon C, Jadlowiec CC, Mao SA, Miao J, Cheungpasitporn W. Enhancing Kidney Transplant Care through the Integration of Chatbot. Healthcare (Basel) 2023; 11:2518. [PMID: 37761715 PMCID: PMC10530762 DOI: 10.3390/healthcare11182518] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 09/03/2023] [Accepted: 09/09/2023] [Indexed: 09/29/2023] Open
Abstract
Kidney transplantation is a critical treatment option for end-stage kidney disease patients, offering improved quality of life and increased survival rates. However, the complexities of kidney transplant care necessitate continuous advancements in decision making, patient communication, and operational efficiency. This article explores the potential integration of a sophisticated chatbot, an AI-powered conversational agent, to enhance kidney transplant practice and potentially improve patient outcomes. Chatbots and generative AI have shown promising applications in various domains, including healthcare, by simulating human-like interactions and generating contextually appropriate responses. Noteworthy AI models like ChatGPT by OpenAI, BingChat by Microsoft, and Bard AI by Google exhibit significant potential in supporting evidence-based research and healthcare decision making. The integration of chatbots in kidney transplant care may offer transformative possibilities. As a clinical decision support tool, it could provide healthcare professionals with real-time access to medical literature and guidelines, potentially enabling informed decision making and improved knowledge dissemination. Additionally, the chatbot has the potential to facilitate patient education by offering personalized and understandable information, addressing queries, and providing guidance on post-transplant care. Furthermore, under clinician or transplant pharmacist supervision, it has the potential to support post-transplant care and medication management by analyzing patient data, which may lead to tailored recommendations on dosages, monitoring schedules, and potential drug interactions. However, to fully ascertain its effectiveness and safety in these roles, further studies and validation are required. Its integration with existing clinical decision support systems may enhance risk stratification and treatment planning, contributing to more informed and efficient decision making in kidney transplant care. Given the importance of ethical considerations and bias mitigation in AI integration, future studies may evaluate long-term patient outcomes, cost-effectiveness, user experience, and the generalizability of chatbot recommendations. By addressing these factors and potentially leveraging AI capabilities, the integration of chatbots in kidney transplant care holds promise for potentially improving patient outcomes, enhancing decision making, and fostering the equitable and responsible use of AI in healthcare.
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Affiliation(s)
- Oscar A. Garcia Valencia
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (O.A.G.V.); (C.T.)
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (O.A.G.V.); (C.T.)
| | - Caroline C. Jadlowiec
- Division of Transplant Surgery, Department of Surgery, Mayo Clinic, Phoenix, AZ 85054, USA;
| | - Shennen A. Mao
- Division of Transplant Surgery, Department of Transplantation, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (O.A.G.V.); (C.T.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (O.A.G.V.); (C.T.)
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