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Crowson MG, Nwosu OI. The Integration and Impact of Artificial Intelligence in Otolaryngology-Head and Neck Surgery: Navigating the Last Mile. Otolaryngol Clin North Am 2024:S0030-6665(24)00058-6. [PMID: 38705741 DOI: 10.1016/j.otc.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Incorporating artificial Intelligence and machine learning into otolaryngology requires careful data handling, security, and ethical considerations. Success depends on interdisciplinary cooperation, consistent innovation, and regulatory compliance to improve clinical outcomes, provider experience, and operational effectiveness.
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Affiliation(s)
- Matthew G Crowson
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear Hospital, Boston, MA, USA; Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, MA, USA.
| | - Obinna I Nwosu
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear Hospital, Boston, MA, USA; Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, MA, USA
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Vaira LA, Lechien JR, Abbate V, Allevi F, Audino G, Beltramini GA, Bergonzani M, Boscolo-Rizzo P, Califano G, Cammaroto G, Chiesa-Estomba CM, Committeri U, Crimi S, Curran NR, di Bello F, di Stadio A, Frosolini A, Gabriele G, Gengler IM, Lonardi F, Maglitto F, Mayo-Yáñez M, Petrocelli M, Pucci R, Saibene AM, Saponaro G, Tel A, Trabalzini F, Trecca EMC, Vellone V, Salzano G, De Riu G. Validation of the Quality Analysis of Medical Artificial Intelligence (QAMAI) tool: a new tool to assess the quality of health information provided by AI platforms. Eur Arch Otorhinolaryngol 2024:10.1007/s00405-024-08710-0. [PMID: 38703195 DOI: 10.1007/s00405-024-08710-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 04/27/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND The widespread diffusion of Artificial Intelligence (AI) platforms is revolutionizing how health-related information is disseminated, thereby highlighting the need for tools to evaluate the quality of such information. This study aimed to propose and validate the Quality Assessment of Medical Artificial Intelligence (QAMAI), a tool specifically designed to assess the quality of health information provided by AI platforms. METHODS The QAMAI tool has been developed by a panel of experts following guidelines for the development of new questionnaires. A total of 30 responses from ChatGPT4, addressing patient queries, theoretical questions, and clinical head and neck surgery scenarios were assessed by 27 reviewers from 25 academic centers worldwide. Construct validity, internal consistency, inter-rater and test-retest reliability were assessed to validate the tool. RESULTS The validation was conducted on the basis of 792 assessments for the 30 responses given by ChatGPT4. The results of the exploratory factor analysis revealed a unidimensional structure of the QAMAI with a single factor comprising all the items that explained 51.1% of the variance with factor loadings ranging from 0.449 to 0.856. Overall internal consistency was high (Cronbach's alpha = 0.837). The Interclass Correlation Coefficient was 0.983 (95% CI 0.973-0.991; F (29,542) = 68.3; p < 0.001), indicating excellent reliability. Test-retest reliability analysis revealed a moderate-to-strong correlation with a Pearson's coefficient of 0.876 (95% CI 0.859-0.891; p < 0.001). CONCLUSIONS The QAMAI tool demonstrated significant reliability and validity in assessing the quality of health information provided by AI platforms. Such a tool might become particularly important/useful for physicians as patients increasingly seek medical information on AI platforms.
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Affiliation(s)
- Luigi Angelo Vaira
- Maxillofacial Surgery Operative Unit, Department of Medicine, Surgery and Pharmacy, University of Sassari, Viale San Pietro 43/B, 07100, Sassari, Italy.
- PhD School of Biomedical Science, Biomedical Sciences Department, University of Sassari, Sassari, Italy.
| | - Jerome R Lechien
- Department of Laryngology and Bronchoesophagology, EpiCURA Hospital, Mons School of Medicine, UMONS. Research Institute for Health Sciences and Technology, University of Mons (UMons), Mons, Belgium
- Department of Otolaryngology-Head Neck Surgery, Elsan Polyclinic of Poitiers, Poitiers, France
| | - Vincenzo Abbate
- Head and Neck Section, Department of Neurosciences, Reproductive and Odontostomatological Science, Federico II University of Naples, Naples, Italy
| | - Fabiana Allevi
- Maxillofacial Surgery Department, ASSt Santi Paolo e Carlo, University of Milan, Milan, Italy
| | - Giovanni Audino
- Head and Neck Section, Department of Neurosciences, Reproductive and Odontostomatological Science, Federico II University of Naples, Naples, Italy
| | - Giada Anna Beltramini
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Maxillofacial and Dental Unit, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Michela Bergonzani
- Maxillo-Facial Surgery Division, Head and Neck Department, University Hospital of Parma, Parma, USA
| | - Paolo Boscolo-Rizzo
- Department of Medical, Surgical and Health Sciences, Section of Otolaryngology, University of Trieste, Trieste, Italy
| | - Gianluigi Califano
- Department of Neurosciences, Reproductive and Odontostomatological Science, Federico II University of Naples, Naples, Italy
| | - Giovanni Cammaroto
- ENT Department, Morgagni Pierantoni Hospital, AUSL Romagna, Forlì, Italy
| | - Carlos M Chiesa-Estomba
- Department of Otorhinolaryngology-Head and Neck Surgery, Hospital Universitario Donostia, San Sebastian, Spain
| | - Umberto Committeri
- Head and Neck Section, Department of Neurosciences, Reproductive and Odontostomatological Science, Federico II University of Naples, Naples, Italy
| | - Salvatore Crimi
- Operative Unit of Maxillofacial Surgery, Policlinico San Marco, University of Catania, Catania, Italy
| | - Nicholas R Curran
- Department of Otolaryngology-Head and Neck Surgery, University of Cincinnati Medical Center, Cincinnati, OH, USA
| | - Francesco di Bello
- Department of Neurosciences, Reproductive and Odontostomatological Science, Federico II University of Naples, Naples, Italy
| | - Arianna di Stadio
- Otolaryngology Unit, GF Ingrassia Department, University of Catania, Catania, Italy
| | - Andrea Frosolini
- Department of Maxillofacial Surgery, University of Siena, Siena, Italy
| | - Guido Gabriele
- Department of Maxillofacial Surgery, University of Siena, Siena, Italy
| | - Isabelle M Gengler
- Department of Otolaryngology-Head and Neck Surgery, University of Cincinnati Medical Center, Cincinnati, OH, USA
| | - Fabio Lonardi
- Department of Maxillofacial Surgery, University of Verona, Verona, Italy
| | - Fabio Maglitto
- Maxillo-Facial Surgery Unit, University of Bari "Aldo Moro", Bari, Italy
| | - Miguel Mayo-Yáñez
- Otorhinolaryngology, Head and Neck Surgery Department, Complexo Hospitalario Universitario A Coruña (CHUAC), A Coruña, Galicia, Spain
| | - Marzia Petrocelli
- Maxillofacial Surgery Operative Unit, Bellaria and Maggiore Hospital, Bologna, Italy
| | - Resi Pucci
- Maxillofacial Surgery Unit, San Camillo-Forlanini Hospital, Rome, Italy
| | - Alberto Maria Saibene
- Otolaryngology Unit, Santi Paolo e Carlo Hospital, Department of Health Sciences, University of Milan, Milan, Italy
| | - Gianmarco Saponaro
- Maxillo-Facial Surgery Unit, IRCSS "A. Gemelli" Foundation-Catholic University of the Sacred Heart, Rome, Italy
| | - Alessandro Tel
- Clinic of Maxillofacial Surgery, Department of Head and Neck Surgery and Neuroscience, University Hospital of Udine, Udine, Italy
| | - Franco Trabalzini
- Department of Otorhinolaryngology, Head and Neck Surgery, Meyer Children's Hospital, Florence, Italy
| | - Eleonora M C Trecca
- Department of Otorhinolaryngology and Maxillofacial Surgery, IRCCS Hospital Casa Sollievo Della Sofferenza, San Giovanni Rotondo, Foggia, Italy
- Department of Otorhinolaryngology, University Hospital of Foggia, Foggia, Italy
| | | | - Giovanni Salzano
- Head and Neck Section, Department of Neurosciences, Reproductive and Odontostomatological Science, Federico II University of Naples, Naples, Italy
| | - Giacomo De Riu
- Maxillofacial Surgery Operative Unit, Department of Medicine, Surgery and Pharmacy, University of Sassari, Viale San Pietro 43/B, 07100, Sassari, Italy
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Schoenhof R, Schoenhof R, Blumenstock G, Lethaus B, Hoefert S. Synthetic, non-person related panoramic radiographs created by Generative Adversarial Networks in research, clinical, and teaching applications. J Dent 2024:105042. [PMID: 38710314 DOI: 10.1016/j.jdent.2024.105042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 05/03/2024] [Indexed: 05/08/2024] Open
Abstract
OBJECTIVES Generative Adversarial Networks (GANs) can produce synthetic images free from personal data. They hold significant value in medical research, where data protection is increasingly regulated. Panoramic radiographs (PRs) are a well-suited modality due to their significant level of standardization while simultaneously displaying a high degree of personally identifiable data. METHODS We produced synthetic PRs (syPRs) out of real PRs (rePRs) using StyleGAN2-ADA by NVIDIA©. A survey was performed on 54 medical professionals and 33 dentistry students. They assessed 45 radiological images (20 rePRs, 20 syPRs, and 5 syPRcontrols) as real or synthetic and interpreted a single-image syPR according to the image quality (0-10) and 14 different items (agreement/disagreement). They also rated the importance for the profession (0-10). A follow-up was performed for test-retest reliability with >10% of all participants. RESULTS Overall, the sensitivity was 78.2% and the specificity was 82.5%. For professionals, the sensitivity was 79.9% and the specificity was 82.3%. For students, the sensitivity was 75.5% and the specificity was 82.7%. In the single syPR-interpretation image quality was rated at a median of 6 and 11 items were considered as agreement. The importance for the profession was rated at a median score of 7. The Test-retest reliability yielded a value of 0.23 (Cohen's kappa). CONCLUSIONS The study marks a comprehensive testing to demonstrate that GANs can produce synthetic radiological images that even health professionals can sometimes not differentiate from real radiological images, thereby being genuinely considered authentic. This enables their utilization and/or modification free from personally identifiable information. CLINICAL SIGNIFICANCE Synthetic images can be used for university teaching and patient education without relying on patient-related data. They can also be utilized to upscale existing training datasets to improve the accuracy of AI-based diagnostic systems. The study thereby supports clinical teaching as well as diagnostic and therapeutic decision-making.
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Affiliation(s)
- Rouven Schoenhof
- Department of Oral and Maxillofacial Surgery (Head: Prof. Dr. Dr. B. Lethaus), University Hospital Tuebingen, Osianderstrasse 2-8, 72076 Tuebingen, Germany.
| | - Raoul Schoenhof
- Fraunhofer Society for the Advancement of Applied Research, Hansastraße 27c, 80686 München
| | - Gunnar Blumenstock
- Institute for Clinical Epidemiology and Applied Biometry (Head: Prof. Dr. rer. nat. P. Martus), University Hospital Tuebingen, Silcherstrasse 5, 72076 Tuebingen, Germany
| | - Bernd Lethaus
- Department of Oral and Maxillofacial Surgery (Head: Prof. Dr. Dr. B. Lethaus), University Hospital Tuebingen, Osianderstrasse 2-8, 72076 Tuebingen, Germany
| | - Sebastian Hoefert
- Department of Oral and Maxillofacial Surgery (Head: Prof. Dr. Dr. B. Lethaus), University Hospital Tuebingen, Osianderstrasse 2-8, 72076 Tuebingen, Germany
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Karimi AH, Langberg J, Malige A, Rahman O, Abboud JA, Stone MA. Accuracy of machine learning to predict the outcomes of shoulder arthroplasty: a systematic review. Arthroplasty 2024; 6:26. [PMID: 38702749 PMCID: PMC11069283 DOI: 10.1186/s42836-024-00244-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 02/26/2024] [Indexed: 05/06/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) uses computer systems to simulate cognitive capacities to accomplish goals like problem-solving and decision-making. Machine learning (ML), a branch of AI, makes algorithms find connections between preset variables, thereby producing prediction models. ML can aid shoulder surgeons in determining which patients may be susceptible to worse outcomes and complications following shoulder arthroplasty (SA) and align patient expectations following SA. However, limited literature is available on ML utilization in total shoulder arthroplasty (TSA) and reverse TSA. METHODS A systematic literature review in accordance with PRISMA guidelines was performed to identify primary research articles evaluating ML's ability to predict SA outcomes. With duplicates removed, the initial query yielded 327 articles, and after applying inclusion and exclusion criteria, 12 articles that had at least 1 month follow-up time were included. RESULTS ML can predict 30-day postoperative complications with a 90% accuracy, postoperative range of motion with a higher-than-85% accuracy, and clinical improvement in patient-reported outcome measures above minimal clinically important differences with a 93%-99% accuracy. ML can predict length of stay, operative time, discharge disposition, and hospitalization costs. CONCLUSION ML can accurately predict outcomes and complications following SA and healthcare utilization. Outcomes are highly dependent on the type of algorithms used, data input, and features selected for the model. LEVEL OF EVIDENCE III.
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Affiliation(s)
- Amir H Karimi
- Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
| | - Joshua Langberg
- Herbert Wertheim College of Medicine, Miami, FL, 33199, USA
- Department of Orthopaedic Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Ajith Malige
- Department of Orthopaedic Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Omar Rahman
- Department of Orthopaedic Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Joseph A Abboud
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Michael A Stone
- Department of Orthopaedic Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
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Seetharam K, Thyagaturu H, Ferreira GL, Patel A, Patel C, Elahi A, Pachulski R, Shah J, Mir P, Thodimela A, Pala M, Thet Z, Hamirani Y. Broadening Perspectives of Artificial Intelligence in Echocardiography. Cardiol Ther 2024:10.1007/s40119-024-00368-3. [PMID: 38703292 DOI: 10.1007/s40119-024-00368-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 04/11/2024] [Indexed: 05/06/2024] Open
Abstract
Echocardiography frequently serves as the first-line treatment of diagnostic imaging for several pathological entities in cardiology. Artificial intelligence (AI) has been growing substantially in information technology and various commercial industries. Machine learning (ML), a branch of AI, has been shown to expand the capabilities and potential of echocardiography. ML algorithms expand the field of echocardiography by automated assessment of the ejection fraction and left ventricular function, integrating novel approaches such as speckle tracking or tissue Doppler echocardiography or vector flow mapping, improved phenotyping, distinguishing between cardiac conditions, and incorporating information from mobile health and genomics. In this review article, we assess the impact of AI and ML in echocardiography.
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Affiliation(s)
- Karthik Seetharam
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA.
- Wyckoff Heights Medical Center, Brooklyn, NY, USA.
| | - Harshith Thyagaturu
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | | | - Aditya Patel
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Chinmay Patel
- University of Pittsburg Medical Center, Harrisburg, PA, USA
| | - Asim Elahi
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Roman Pachulski
- St. John's Episcopal Hospital - South Shore, New York, NY, USA
| | - Jilan Shah
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Parvez Mir
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | | | - Manya Pala
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Zeyar Thet
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Yasmin Hamirani
- Robert Woods Johnson University Hospital/Rutgers University, New Brusnwick, NJ, USA
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Abstract
Introduction The subject of this article is the discovery of dento-dental disharmony (DDD) at the end of treatment. Lack of diagnosis is the source of this type of disappointment. Material and Method The diagnosis of DDD is not easily accessible on clinical examination and the compensations it generates mask it, especially if it is associated with other dysmorphoses. The use of indices, the best-known of which is Bolton's, enables diagnosis with the setup, a pre-treatment model which also has many other prognostic interests. Results Once DDD has been considered, it can be resolved by adapting dental volumes, either by subtraction or addition. Conclusion Advances in computerized diagnosis with artificial intelligence are opening up new avenues for the systematic diagnosis of DDD.
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107
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Solanki S, Sinha S, Seth CS, Tyagi S, Goyal A, Singh R. Enhanced adsorption of Bismark Brown R dye by chitosan conjugated magnetic pectin loaded filter mud: A comprehensive study on modeling and mechanisms. Int J Biol Macromol 2024:131987. [PMID: 38705337 DOI: 10.1016/j.ijbiomac.2024.131987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 04/11/2024] [Accepted: 04/28/2024] [Indexed: 05/07/2024]
Abstract
Herein, a polymer-based bioadsorbent was prepared by cross-linking chitosan to filter mud and magnetic pectin (Ch-mPC@FM) for the removal of Bismark Brown R dye (BB-R) from wastewater. Morphological characterization analysis indicated that Ch-mPC@FM had a higher surface area and better pore structure than its components. The Artificial Neuron Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were employed to evaluate the simulation and prediction of the adsorption process based on input variables like temperature, pH, dosage, initial BB-R dye concentration, and contact time. ANFIS and ANN demonstrated significant modeling and predictive accuracy, with R2 > 0.93 and R2 > 0.96, and root mean square error < 0.023 and <0.020, respectively. The Langmuir isotherm and the pseudo-second-order kinetic models provided the best fits to the equilibrium and kinetic data. The thermodynamic assessment showed spontaneous and endothermic adsorption with average entropy and enthalpy changes of 119.32 kJ mol-1 K and 403.47 kJ mol-1, respectively. The study of BB-R dye adsorption on Ch-mPC@FM revealed multiple mechanisms, including electrostatic, complexation, pore filling, cation-π interaction, hydrogen bonding, and π-π interactions. The approximate production cost of US$ 5.809 Kg-1 and excellent adsorption capability render Ch-mPC@FM an inexpensive, pragmatic, and ecologically safe bioadsorbent for BB-R dye removal from wastewater.
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Affiliation(s)
- Swati Solanki
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida 201313, India
| | - Surbhi Sinha
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida 201313, India.
| | | | - Shivanshi Tyagi
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida 201313, India
| | - Aarushi Goyal
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida 201313, India
| | - Rachana Singh
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida 201313, India.
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Eresen A. Leveraging Artificial Intelligence to Revolutionize Ovarian Cancer Diagnosis. Acad Radiol 2024:S1076-6332(24)00241-1. [PMID: 38704287 DOI: 10.1016/j.acra.2024.04.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 04/17/2024] [Indexed: 05/06/2024]
Affiliation(s)
- Aydin Eresen
- Department of Radiological Sciences, University of California Irvine, Irvine, California, USA.
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van der Sar IG, Moor CC, Wijsenbeek MS. Classifying Interstitial Lung Disease: Omics Are in the Air. Am J Respir Crit Care Med 2024. [PMID: 38701488 DOI: 10.1164/rccm.202404-0748le] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 05/01/2024] [Indexed: 05/05/2024] Open
Affiliation(s)
- Iris G van der Sar
- Erasmus Medical Center, 6993, Respiratory Medicine, Rotterdam, Zuid-Holland, Netherlands
| | - Catharina C Moor
- Erasmus MC, 6993, Pulmonary Medicine , Rotterdam, Zuid-Holland, Netherlands
| | - Marlies S Wijsenbeek
- Erasmus MC, 6993, Academic Centre for Interstitial Lung Diseases, Department of Respiratory Medicine, Rotterdam, Netherlands;
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Stephan S, Galland S, Labbani Narsis O, Shoji K, Vachenc S, Gerart S, Nicolle C. Agent-based approaches for biological modeling in oncology: A literature review. Artif Intell Med 2024; 152:102884. [PMID: 38703466 DOI: 10.1016/j.artmed.2024.102884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/06/2024]
Abstract
CONTEXT Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions. METHODOLOGY This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023. RESULTS Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.
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Affiliation(s)
- Simon Stephan
- UTBM, CIAD UMR 7533, Belfort, F-90010, France; Université de Bourgogne, CIAD UMR 7533, Dijon, F-21000, France.
| | | | | | - Kenji Shoji
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
| | - Sébastien Vachenc
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
| | - Stéphane Gerart
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
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Mashoudy KD, Perez SM, Nouri K. From diagnosis to intervention: a review of telemedicine's role in skin cancer care. Arch Dermatol Res 2024; 316:139. [PMID: 38696032 PMCID: PMC11065900 DOI: 10.1007/s00403-024-02884-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/03/2024] [Accepted: 04/14/2024] [Indexed: 05/05/2024]
Abstract
Skin cancer treatment is a core aspect of dermatology that relies on accurate diagnosis and timely interventions. Teledermatology has emerged as a valuable asset across various stages of skin cancer care including triage, diagnosis, management, and surgical consultation. With the integration of traditional dermoscopy and store-and-forward technology, teledermatology facilitates the swift sharing of high-resolution images of suspicious skin lesions with consulting dermatologists all-over. Both live video conference and store-and-forward formats have played a pivotal role in bridging the care access gap between geographically isolated patients and dermatology providers. Notably, teledermatology demonstrates diagnostic accuracy rates that are often comparable to those achieved through traditional face-to-face consultations, underscoring its robust clinical utility. Technological advancements like artificial intelligence and reflectance confocal microscopy continue to enhance image quality and hold potential for increasing the diagnostic accuracy of virtual dermatologic care. While teledermatology serves as a valuable clinical tool for all patient populations including pediatric patients, it is not intended to fully replace in-person procedures like Mohs surgery and other necessary interventions. Nevertheless, its role in facilitating the evaluation of skin malignancies is gaining recognition within the dermatologic community and fostering high approval rates from patients due to its practicality and ability to provide timely access to specialized care.
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Affiliation(s)
- Kayla D Mashoudy
- University of Miami Miller School of Medicine, 1600 NW 10th Ave #1140, Miami, FL, 33136, USA.
| | - Sofia M Perez
- University of Miami Miller School of Medicine, 1600 NW 10th Ave #1140, Miami, FL, 33136, USA
| | - Keyvan Nouri
- Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, 1150 NW 14th Street, Miami, FL, 33136, USA
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112
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Baldini C, Azam MA, Sampieri C, Ioppi A, Ruiz-Sevilla L, Vilaseca I, Alegre B, Tirrito A, Pennacchi A, Peretti G, Moccia S, Mattos LS. An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning. Eur Arch Otorhinolaryngol 2024:10.1007/s00405-024-08676-z. [PMID: 38698163 DOI: 10.1007/s00405-024-08676-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 04/08/2024] [Indexed: 05/05/2024]
Abstract
PURPOSE Informative image selection in laryngoscopy has the potential for improving automatic data extraction alone, for selective data storage and a faster review process, or in combination with other artificial intelligence (AI) detection or diagnosis models. This paper aims to demonstrate the feasibility of AI in providing automatic informative laryngoscopy frame selection also capable of working in real-time providing visual feedback to guide the otolaryngologist during the examination. METHODS Several deep learning models were trained and tested on an internal dataset (n = 5147 images) and then tested on an external test set (n = 646 images) composed of both white light and narrow band images. Four videos were used to assess the real-time performance of the best-performing model. RESULTS ResNet-50, pre-trained with the pretext strategy, reached a precision = 95% vs. 97%, recall = 97% vs, 89%, and the F1-score = 96% vs. 93% on the internal and external test set respectively (p = 0.062). The four testing videos are provided in the supplemental materials. CONCLUSION The deep learning model demonstrated excellent performance in identifying diagnostically relevant frames within laryngoscopic videos. With its solid accuracy and real-time capabilities, the system is promising for its development in a clinical setting, either autonomously for objective quality control or in conjunction with other algorithms within a comprehensive AI toolset aimed at enhancing tumor detection and diagnosis.
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Affiliation(s)
- Chiara Baldini
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
- Departement of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
| | - Muhammad Adeel Azam
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
- Departement of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
| | - Claudio Sampieri
- Department of Experimental Medicine (DIMES), University of Genoa, Genoa, Italy.
- Department of Otolaryngology, Hospital Clínic, C. de Villarroel, 170, 08029, Barcelona, Spain.
- Unit of Head and Neck Tumors, Hospital Clínic, Barcelona, Spain.
| | | | - Laura Ruiz-Sevilla
- Otorhinolaryngology Head-Neck Surgery Department, Hospital Universitari Joan XXIII de Tarragona, Tarragona, Spain
| | - Isabel Vilaseca
- Department of Otolaryngology, Hospital Clínic, C. de Villarroel, 170, 08029, Barcelona, Spain
- Unit of Head and Neck Tumors, Hospital Clínic, Barcelona, Spain
- Translational Genomics and Target Therapies in Solid Tumors Group, Institut d́Investigacions Biomèdiques August Pi i Sunyer, IDIBAPS, Barcelona, Spain
- Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Berta Alegre
- Department of Otolaryngology, Hospital Clínic, C. de Villarroel, 170, 08029, Barcelona, Spain
- Unit of Head and Neck Tumors, Hospital Clínic, Barcelona, Spain
| | - Alessandro Tirrito
- Unit of Otorhinolaryngology-Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Alessia Pennacchi
- Unit of Otorhinolaryngology-Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Giorgio Peretti
- Unit of Otorhinolaryngology-Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Sara Moccia
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Leonardo S Mattos
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
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Wang F, Song P, Wang J, Wang S, Liu Y, Bai L, Su J. Organoid bioinks: construction and application. Biofabrication 2024. [PMID: 38697093 DOI: 10.1088/1758-5090/ad467c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2024]
Abstract
Organoids have emerged as crucial platforms in tissue engineering and regenerative medicine but confront challenges in faithfully mimicking native tissue structures and functions. Bioprinting technologies offer a significant advancement, especially when combined with organoid bioinks-engineered formulations designed to encapsulate both the architectural and functional elements of specific tissues. This review provides a rigorous, focused examination of the evolution and impact of organoid bioprinting. It emphasizes the role of organoid bioinks that integrate key cellular components and microenvironmental cues to more accurately replicate native tissue complexity. Furthermore, this review anticipates a transformative landscape invigorated by the integration of artificial intelligence with bioprinting techniques. Such fusion promises to refine organoid bioink formulations and optimize bioprinting parameters, thus catalyzing unprecedented advancements in regenerative medicine. In summary, this review accentuates the pivotal role and transformative potential of organoid bioinks and bioprinting in advancing regenerative therapies, deepening our understanding of organ development, and clarifying disease mechanisms.
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Affiliation(s)
- Fuxiao Wang
- Shanghai University, Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai 200444, China, Shanghai, 200444, CHINA
| | - Peiran Song
- Shanghai University, Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai 200444, China, Shanghai, 200444, CHINA
| | - Jian Wang
- Shanghai University, Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai 200444, China, Shanghai, Shanghai, 200444, CHINA
| | - Sicheng Wang
- Shanghai University, Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai 200444, China, Shanghai, 200444, CHINA
| | - Yuanyuan Liu
- Shanghai University, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China, Shanghai, 200444, CHINA
| | - Long Bai
- Shanghai University, Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai 200444, China, Shanghai, 200444, CHINA
| | - Jiacan Su
- Shanghai University, Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai 200444, China, Shanghai, 200444, CHINA
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Somyanonthanakul R, Warin K, Chaowchuen S, Jinaporntham S, Panichkitkosolkul W, Suebnukarn S. Survival estimation of oral cancer using fuzzy deep learning. BMC Oral Health 2024; 24:519. [PMID: 38698358 PMCID: PMC11067185 DOI: 10.1186/s12903-024-04279-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 04/19/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND Oral cancer is a deadly disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop a fuzzy deep learning (FDL)-based model to estimate the survival time based on clinicopathologic data of oral cancer. METHODS Electronic medical records of 581 oral squamous cell carcinoma (OSCC) patients, treated with surgery with or without radiochemotherapy, were collected retrospectively from the Oral and Maxillofacial Surgery Clinic and the Regional Cancer Center from 2011 to 2019. The deep learning (DL) model was trained to classify survival time classes based on clinicopathologic data. Fuzzy logic was integrated into the DL model and trained to create FDL-based models to estimate the survival time classes. RESULTS The performance of the models was evaluated on a test dataset. The performance of the DL and FDL models for estimation of survival time achieved an accuracy of 0.74 and 0.97 and an area under the receiver operating characteristic (AUC) curve of 0.84 to 1.00 and 1.00, respectively. CONCLUSIONS The integration of fuzzy logic into DL models could improve the accuracy to estimate survival time based on clinicopathologic data of oral cancer.
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Affiliation(s)
| | - Kritsasith Warin
- Faculty of Dentistry, Thammasat University, Pathum Thani, Thailand.
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Achaal B, Adda M, Berger M, Ibrahim H, Awde A. Study of smart grid cyber-security, examining architectures, communication networks, cyber-attacks, countermeasure techniques, and challenges. Cybersecur (Singap) 2024; 7:10. [PMID: 38707764 PMCID: PMC11062904 DOI: 10.1186/s42400-023-00200-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 12/12/2023] [Indexed: 05/07/2024]
Abstract
Smart Grid (SG) technology utilizes advanced network communication and monitoring technologies to manage and regulate electricity generation and transport. However, this increased reliance on technology and connectivity also introduces new vulnerabilities, making SG communication networks susceptible to large-scale attacks. While previous surveys have mainly provided high-level overviews of SG architecture, our analysis goes further by presenting a comprehensive architectural diagram encompassing key SG components and communication links. This holistic view enhances understanding of potential cyber threats and enables systematic cyber risk assessment for SGs. Additionally, we propose a taxonomy of various cyberattack types based on their targets and methods, offering detailed insights into vulnerabilities. Unlike other reviews focused narrowly on protection and detection, our proposed categorization covers all five functions of the National Institute of Standards and Technology cybersecurity framework. This delivers a broad perspective to help organizations implement balanced and robust security. Consequently, we have identified critical research gaps, especially regarding response and recovery mechanisms. This underscores the need for further investigation to bolster SG cybersecurity. These research needs, among others, are highlighted as open issues in our concluding section.
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Affiliation(s)
- Batoul Achaal
- Département de Mathématique, Informatique et Génie, Université du Québec à Rimouski, Allée des Ursulines, Rimouski, G5L 3A1 Canada
| | - Mehdi Adda
- Département de Mathématique, Informatique et Génie, Université du Québec à Rimouski, Allée des Ursulines, Rimouski, G5L 3A1 Canada
| | - Maxime Berger
- Département de Mathématique, Informatique et Génie, Université du Québec à Rimouski, Allée des Ursulines, Rimouski, G5L 3A1 Canada
| | - Hussein Ibrahim
- Centre de Recherche et d’innovation en Intelligence énergétique (CR2ie), Rue De La Vérendrye, Sept-Îles, G4R 5B7 Canada
| | - Ali Awde
- Centre de Recherche et d’innovation en Intelligence énergétique (CR2ie), Rue De La Vérendrye, Sept-Îles, G4R 5B7 Canada
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Kuwabara M, Ikawa F, Nakazawa S, Koshino S, Ishii D, Kondo H, Hara T, Maeda Y, Sato R, Kaneko T, Maeyama S, Shimahara Y, Horie N. Artificial intelligence for volumetric measurement of cerebral white matter hyperintensities on thick-slice fluid-attenuated inversion recovery (FLAIR) magnetic resonance images from multiple centers. Sci Rep 2024; 14:10104. [PMID: 38698152 PMCID: PMC11065995 DOI: 10.1038/s41598-024-60789-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/26/2024] [Indexed: 05/05/2024] Open
Abstract
We aimed to develop a new artificial intelligence software that can automatically extract and measure the volume of white matter hyperintensities (WMHs) in head magnetic resonance imaging (MRI) using only thick-slice fluid-attenuated inversion recovery (FLAIR) sequences from multiple centers. We enrolled 1092 participants in Japan, comprising the thick-slice Private Dataset. Based on 207 randomly selected participants, neuroradiologists annotated WMHs using predefined guidelines. The annotated images of participants were divided into training (n = 138) and test (n = 69) datasets. The WMH segmentation model comprised a U-Net ensemble and was trained using the Private Dataset. Two other models were trained for validation using either both thin- and thick-slice MRI datasets or the thin-slice dataset alone. The voxel-wise Dice similarity coefficient (DSC) was used as the evaluation metric. The model trained using only thick-slice MRI showed a DSC of 0.820 for the test dataset, which is comparable to the accuracy of human readers. The model trained with the additional thin-slice dataset showed only a slightly improved DSC of 0.822. This automatic WMH segmentation model comprising a U-Net ensemble trained on a thick-slice FLAIR MRI dataset is a promising new method. Despite some limitations, this model may be applicable in clinical practice.
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Affiliation(s)
- Masashi Kuwabara
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Fusao Ikawa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, Hiroshima, 734-8551, Japan.
- Department of Neurosurgery, Shimane Prefectural Central Hospital, 4-1-1 Himebara, Izumo, Shimane, 693-0068, Japan.
| | - Shinji Nakazawa
- LPIXEL Inc, 1-6-1 Otemachi, Chiyoda-Ku, Tokyo, 100-0004, Japan
| | - Saori Koshino
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Daizo Ishii
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Hiroshi Kondo
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Takeshi Hara
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Yuyo Maeda
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Ryo Sato
- LPIXEL Inc, 1-6-1 Otemachi, Chiyoda-Ku, Tokyo, 100-0004, Japan
| | - Taiki Kaneko
- LPIXEL Inc, 1-6-1 Otemachi, Chiyoda-Ku, Tokyo, 100-0004, Japan
| | - Shiyuki Maeyama
- LPIXEL Inc, 1-6-1 Otemachi, Chiyoda-Ku, Tokyo, 100-0004, Japan
| | - Yuki Shimahara
- LPIXEL Inc, 1-6-1 Otemachi, Chiyoda-Ku, Tokyo, 100-0004, Japan
| | - Nobutaka Horie
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, Hiroshima, 734-8551, Japan
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Glielmo P, Fusco S, Gitto S, Zantonelli G, Albano D, Messina C, Sconfienza LM, Mauri G. Artificial intelligence in interventional radiology: state of the art. Eur Radiol Exp 2024; 8:62. [PMID: 38693468 PMCID: PMC11063019 DOI: 10.1186/s41747-024-00452-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 02/26/2024] [Indexed: 05/03/2024] Open
Abstract
Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. Research in AI is in its early stages as current literature is based on pilot or proof of concept studies. The full range of possibilities is yet to be explored.Relevance statement Exploring AI's transformative potential, this article assesses its current applications and challenges in IR, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care.Key points• AI adoption in IR is more complex compared to diagnostic radiology.• Current literature about AI in IR is in its early stages.• AI has the potential to revolutionise every aspect of IR.
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Affiliation(s)
- Pierluigi Glielmo
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy.
| | - Stefano Fusco
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
| | - Salvatore Gitto
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
| | - Giulia Zantonelli
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
- Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Via della Commenda, 10, 20122, Milan, Italy
| | - Carmelo Messina
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
| | - Luca Maria Sconfienza
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
| | - Giovanni Mauri
- Divisione di Radiologia Interventistica, IEO, IRCCS Istituto Europeo di Oncologia, Milan, Italy
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Ozturk N, Yakak I, Ağ MB, Aksoy N. Is ChatGPT reliable and accurate in answering pharmacotherapy-related inquiries in both Turkish and English? Curr Pharm Teach Learn 2024:S1877-1297(24)00120-5. [PMID: 38702261 DOI: 10.1016/j.cptl.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 04/23/2024] [Accepted: 04/26/2024] [Indexed: 05/06/2024]
Abstract
INTRODUCTION Artificial intelligence (AI), particularly ChatGPT, is becoming more and more prevalent in the healthcare field for tasks such as disease diagnosis and medical record analysis. The objective of this study is to evaluate the proficiency and accuracy of ChatGPT in different domains of clinical pharmacy cases and queries. METHODS The study NAPLEX® Review Questions, 4th edition, pertaining to 10 different chronic conditions compared ChatGPT's responses to pharmacotherapy cases and questions obtained from McGraw Hill's, alongside the answers provided by the book's authors. The proportion of correct responses was collected and analyzed using the Statistical Package for the Social Sciences (SPSS) version 29. RESULTS When tested in English, ChatGPT had substantially higher mean scores than when tested in Turkish. The average accurate score for English and Turkish was 0.41 ± 0.49 and 0.32 ± 0.46, respectively, p = 0.18. Responses to queries beginning with "Which of the following is correct?" are considerably more precise than those beginning with "Mark all the incorrect answers?" 0.66 ± 0.47 as opposed to 0.16 ± 0.36; p = 0.01 in English language and 0.50 ± 0.50 as opposed to 0.14 ± 0.34; p < 0.05in Turkish language. CONCLUSION ChatGPT displayed a moderate level of accuracy while responding to English inquiries, but it displayed a slight level of accuracy when responding to Turkish inquiries, contingent upon the question format. Improving the accuracy of ChatGPT in languages other than English requires the incorporation of several components. The integration of the English version of ChatGPT into clinical practice has the potential to improve the effectiveness, precision, and standard of patient care provision by supplementing personal expertise and professional judgment. However, it is crucial to utilize technology as an adjunct and not a replacement for human decision-making and critical thinking.
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Affiliation(s)
- Nur Ozturk
- Altinbas University, School of Pharmacy, Department of Clinical Pharmacy, Istanbul, Turkey; Istanbul Medipol University, Graduate School of Health Sciences, Clinical Pharmacy PhD Program, Istanbul, Turkey.
| | - Irem Yakak
- Istanbul Medipol University, Graduate School of Health Sciences, Clinical Pharmacy PhD Program, Istanbul, Turkey.
| | - Melih Buğra Ağ
- Istanbul Medipol University, Graduate School of Health Sciences, Clinical Pharmacy PhD Program, Istanbul, Turkey; Istanbul Medipol University, School of Pharmacy, Department of Clinical Pharmacy, Istanbul, Turkey.
| | - Nilay Aksoy
- Altinbas University, School of Pharmacy, Department of Clinical Pharmacy, Istanbul, Turkey.
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Oh H, Kim C. Fairness-aware recommendation with meta learning. Sci Rep 2024; 14:10125. [PMID: 38698202 PMCID: PMC11066081 DOI: 10.1038/s41598-024-60808-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 04/26/2024] [Indexed: 05/05/2024] Open
Abstract
Fairness has become a critical value online, and the latest studies consider it in many problems. In recommender systems, fairness is important since the visibility of items is controlled by systems. Previous fairness-aware recommender systems assume that sufficient relationship data between users and items are available. However, it is common that new users and items are frequently introduced, and they have no relationship data yet. In this paper, we study recommendation methods to enhance fairness in a cold-start state. Fairness is more significant when the preference of a user or the popularity of an item is unknown. We propose a meta-learning-based cold-start recommendation framework called FaRM to alleviate the unfairness of recommendations. The proposed framework consists of three steps. We first propose a fairness-aware meta-path generation method to eliminate bias in sensitive attributes. In addition, we construct fairness-aware user representations through the meta-path aggregation approach. Then, we propose a novel fairness objective function and introduce a joint learning method to minimize the trade-off between relevancy and fairness. In extensive experiments with various cold-start scenarios, it is shown that FaRM is significantly superior in fairness performance while preserving relevance accuracy over previous work.
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Affiliation(s)
- Hyeji Oh
- Department of IT Engineering, Sookmyung Women's University, 100 Cheongpa-ro 47-gil, Yongsan-gu, Seoul, 04310, Korea
| | - Chulyun Kim
- Department of IT Engineering, Sookmyung Women's University, 100 Cheongpa-ro 47-gil, Yongsan-gu, Seoul, 04310, Korea.
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Newlands R, Bruhn H, Díaz MR, Lip G, Anderson LA, Ramsay C. A stakeholder analysis to prepare for real-world evaluation of integrating artificial intelligent algorithms into breast screening (PREP-AIR study): a qualitative study using the WHO guide. BMC Health Serv Res 2024; 24:569. [PMID: 38698386 PMCID: PMC11067265 DOI: 10.1186/s12913-024-10926-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 03/28/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND The national breast screening programme in the United Kingdom is under pressure due to workforce shortages and having been paused during the COVID-19 pandemic. Artificial intelligence has the potential to transform how healthcare is delivered by improving care processes and patient outcomes. Research on the clinical and organisational benefits of artificial intelligence is still at an early stage, and numerous concerns have been raised around its implications, including patient safety, acceptance, and accountability for decisions. Reforming the breast screening programme to include artificial intelligence is a complex endeavour because numerous stakeholders influence it. Therefore, a stakeholder analysis was conducted to identify relevant stakeholders, explore their views on the proposed reform (i.e., integrating artificial intelligence algorithms into the Scottish National Breast Screening Service for breast cancer detection) and develop strategies for managing 'important' stakeholders. METHODS A qualitative study (i.e., focus groups and interviews, March-November 2021) was conducted using the stakeholder analysis guide provided by the World Health Organisation and involving three Scottish health boards: NHS Greater Glasgow & Clyde, NHS Grampian and NHS Lothian. The objectives included: (A) Identify possible stakeholders (B) Explore stakeholders' perspectives and describe their characteristics (C) Prioritise stakeholders in terms of importance and (D) Develop strategies to manage 'important' stakeholders. Seven stakeholder characteristics were assessed: their knowledge of the targeted reform, position, interest, alliances, resources, power and leadership. RESULTS Thirty-two participants took part from 14 (out of 17 identified) sub-groups of stakeholders. While they were generally supportive of using artificial intelligence in breast screening programmes, some concerns were raised. Stakeholder knowledge, influence and interests in the reform varied. Key advantages mentioned include service efficiency, quicker results and reduced work pressure. Disadvantages included overdiagnosis or misdiagnosis of cancer, inequalities in detection and the self-learning capacity of the algorithms. Five strategies (with considerations suggested by stakeholders) were developed to maintain and improve the support of 'important' stakeholders. CONCLUSIONS Health services worldwide face similar challenges of workforce issues to provide patient care. The findings of this study will help others to learn from Scottish experiences and provide guidance to conduct similar studies targeting healthcare reform. STUDY REGISTRATION researchregistry6579, date of registration: 16/02/2021.
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Affiliation(s)
- Rumana Newlands
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK.
| | - Hanne Bruhn
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK
| | | | - Gerald Lip
- North East Scotland Breast Screening Programme, NHS Grampian, Aberdeen, UK
| | - Lesley A Anderson
- Centre for Health Data Science, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Craig Ramsay
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK
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Hein K, Conkey-Morrison C, Burleigh TL, Poulus D, Stavropoulos V. Examining how gamers connect with their avatars to assess their anxiety: A novel artificial intelligence approach. Acta Psychol (Amst) 2024; 246:104298. [PMID: 38701623 DOI: 10.1016/j.actpsy.2024.104298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/29/2024] [Accepted: 04/29/2024] [Indexed: 05/05/2024] Open
Abstract
Research has supported that a gamer's attachment to their avatar can offer significant insights about their mental health, including anxiety. To assess this hypothesis, longitudinal data from 565 adult and adolescent participants (Mage = 29.3 years, SD = 10.6) was analyzed at two points, six months apart. Respondents were assessed using the User-Avatar Bond (UAB) scale and the Depression Anxiety Stress Scale (DASS) to measure their connection with their avatar and their risk for anxiety. The records were processed using both untuned and tuned artificial intelligence [AI] classifiers to evaluate present and future anxiety. The findings indicated that AI models are capable of accurately and autonomously discerning cases of anxiety risk based on the gamers' self-reported UAB, age, and duration of gaming, both at present and after six months. Notably, random forest algorithms surpassed other AI models in effectiveness, with avatar compensation emerging as the most significant factor in model training for prospective anxiety. The implications for assessment, prevention, and clinical practice are discussed.
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Affiliation(s)
- Kaiden Hein
- School of Health and Biomedical Sciences, RMIT University, Melbourne, Australia
| | - Connor Conkey-Morrison
- School of Health and Biomedical Sciences, RMIT University, Melbourne, Australia; College of Health and Biomedicine, Victoria University, Melbourne, Victoria, Australia
| | - Tyrone L Burleigh
- School of Health and Biomedical Sciences, RMIT University, Melbourne, Australia.
| | - Dylan Poulus
- Faculty of Health, Southern Cross University, Queensland, Australia
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Lechien JR, Carroll TL, Huston MN, Naunheim MR. ChatGPT-4 accuracy for patient education in laryngopharyngeal reflux. Eur Arch Otorhinolaryngol 2024; 281:2547-2552. [PMID: 38492008 DOI: 10.1007/s00405-024-08560-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 02/13/2024] [Indexed: 03/18/2024]
Abstract
INTRODUCTION Chatbot Generative Pre-trained Transformer (ChatGPT) is an artificial intelligence-powered language model chatbot able to help otolaryngologists in practice and research. The ability of ChatGPT in generating patient-centered information related to laryngopharyngeal reflux disease (LPRD) was evaluated. METHODS Twenty-five questions dedicated to definition, clinical presentation, diagnosis, and treatment of LPRD were developed from the Dubai definition and management of LPRD consensus and recent reviews. Questions about the four aforementioned categories were entered into ChatGPT-4. Four board-certified laryngologists evaluated the accuracy of ChatGPT-4 with a 5-point Likert scale. Interrater reliability was evaluated. RESULTS The mean scores (SD) of ChatGPT-4 answers for definition, clinical presentation, additional examination, and treatments were 4.13 (0.52), 4.50 (0.72), 3.75 (0.61), and 4.18 (0.47), respectively. Experts reported high interrater reliability for sub-scores (ICC = 0.973). The lowest performances of ChatGPT-4 were on answers about the most prevalent LPR signs, the most reliable objective tool for the diagnosis (hypopharyngeal-esophageal multichannel intraluminal impedance-pH monitoring (HEMII-pH)), and the criteria for the diagnosis of LPR using HEMII-pH. CONCLUSION ChatGPT-4 may provide adequate information on the definition of LPR, differences compared to GERD (gastroesophageal reflux disease), and clinical presentation. Information provided upon extra-laryngeal manifestations and HEMII-pH may need further optimization. Regarding the recent trends identifying increasing patient use of internet sources for self-education, the findings of the present study may help draw attention to ChatGPT-4's accuracy on the topic of LPR.
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Affiliation(s)
- Jerome R Lechien
- Research Committee, Young Otolaryngologists of the International Federation of Otorhinolaryngological Societies (IFOS), Paris, France.
- Division of Laryngology and Broncho-Esophagology, Department of Otolaryngology-Head Neck Surgery, EpiCURA Hospital, UMONS Research Institute for Health Sciences and Technology, University of Mons (UMons), Mons, Belgium.
- Department of Otorhinolaryngology and Head and Neck Surgery, Foch Hospital, School of Medicine, Phonetics and Phonology Laboratory (UMR 7018 CNRS, Université Sorbonne Nouvelle/Paris 3), Paris, France.
- Polyclinique Elsan de Poitiers, Poitiers, France.
| | - Thomas L Carroll
- Division of Otolaryngology-Head and Neck Surgery, Brigham and Women's Hospital, Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Molly N Huston
- Department of Otolaryngology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Matthew R Naunheim
- Research Committee, Young Otolaryngologists of the International Federation of Otorhinolaryngological Societies (IFOS), Paris, France
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
- Division of Laryngology, Massachusetts Eye and Ear, Boston, MA, USA
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Raj M, Ayub A, Pal AK, Pradhan J, Varish N, Kumar S, Varikasuvu SR. Diagnostic Accuracy of Artificial Intelligence-Based Algorithms in Automated Detection of Neck of Femur Fracture on a Plain Radiograph: A Systematic Review and Meta-analysis. Indian J Orthop 2024; 58:457-469. [PMID: 38694696 PMCID: PMC11058182 DOI: 10.1007/s43465-024-01130-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 02/27/2024] [Indexed: 05/04/2024]
Abstract
Objectives To evaluate the diagnostic accuracy of artificial intelligence-based algorithms in identifying neck of femur fracture on a plain radiograph. Design Systematic review and meta-analysis. Data sources PubMed, Web of science, Scopus, IEEE, and the Science direct databases were searched from inception to 30 July 2023. Eligibility criteria for study selection Eligible article types were descriptive, analytical, or trial studies published in the English language providing data on the utility of artificial intelligence (AI) based algorithms in the detection of the neck of the femur (NOF) fracture on plain X-ray. Main outcome measures The prespecified primary outcome was to calculate the sensitivity, specificity, accuracy, Youden index, and positive and negative likelihood ratios. Two teams of reviewers (each consisting of two members) extracted the data from available information in each study. The risk of bias was assessed using a mix of the CLAIM (the Checklist for AI in Medical Imaging) and QUADAS-2 (A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies) criteria. Results Of the 437 articles retrieved, five were eligible for inclusion, and the pooled sensitivity of AIs in diagnosing the fracture NOF was 85%, with a specificity of 87%. For all studies, the pooled Youden index (YI) was 0.73. The average positive likelihood ratio (PLR) was 19.88, whereas the negative likelihood ratio (NLR) was 0.17. The random effects model showed an overall odds of 1.16 (0.84-1.61) in the forest plot, comparing the AI system with those of human diagnosis. The overall heterogeneity of the studies was marginal (I2 = 51%). The CLAIM criteria for risk of bias assessment had an overall >70% score. Conclusion Artificial intelligence (AI)-based algorithms can be used as a diagnostic adjunct, benefiting clinicians by taking less time and effort in neck of the femur (NOF) fracture diagnosis. Study registration PROSPERO CRD42022375449. Supplementary Information The online version contains supplementary material available at 10.1007/s43465-024-01130-6.
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Affiliation(s)
- Manish Raj
- Department of Orthopaedic, All India Institute of Medical Sciences, Deoghar, Jharkhand India
| | - Arshad Ayub
- Department of Community and Family Medicine, All India Institute of Medical Sciences, Deoghar, Jharkhand India
| | - Arup Kumar Pal
- Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand India
| | - Jitesh Pradhan
- Department of Computer Science and Engineering, National Institute of Technology (NIT), Jamshedpur, Jharkhand India
| | - Naushad Varish
- Department of Computer Science and Engineering, GITAM University, Hyderabad Campus, Telangana, India
| | - Sumit Kumar
- Informatics Cluster, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand India
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Fournier A, Fallet C, Sadeghipour F, Perrottet N. Assessing the applicability and appropriateness of ChatGPT in answering clinical pharmacy questions. Ann Pharm Fr 2024; 82:507-513. [PMID: 37992892 DOI: 10.1016/j.pharma.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 11/16/2023] [Accepted: 11/16/2023] [Indexed: 11/24/2023]
Abstract
OBJECTIVES Clinical pharmacists rely on different scientific references to ensure appropriate, safe, and cost-effective drug use. Tools based on artificial intelligence (AI) such as ChatGPT (Generative Pre-trained Transformer) could offer valuable support. The objective of this study was to assess ChatGPT's capacity to correctly respond to clinical pharmacy questions asked by healthcare professionals in our university hospital. MATERIAL AND METHODS ChatGPT's capacity to respond correctly to the last 100 consecutive questions recorded in our clinical pharmacy database was assessed. Questions were copied from our FileMaker Pro database and pasted into ChatGPT March 14 version online platform. The generated answers were then copied verbatim into an Excel file. Two blinded clinical pharmacists reviewed all the questions and the answers given by the software. In case of disagreements, a third blinded pharmacist intervened to decide. RESULTS Documentation-related issues (n=36) and drug administration mode (n=30) were preponderantly recorded. Among 69 applicable questions, the rate of correct answers varied from 30 to 57.1% depending on questions type with a global rate of 44.9%. Regarding inappropriate answers (n=38), 20 were incorrect, 18 gave no answers and 8 were incomplete with 8 answers belonging to 2 different categories. No better answers than the pharmacists were observed. CONCLUSIONS ChatGPT demonstrated a mitigated performance in answering clinical pharmacy questions. It should not replace human expertise as a high rate of inappropriate answers was highlighted. Future studies should focus on the optimization of ChatGPT for specific clinical pharmacy questions and explore the potential benefits and limitations of integrating this technology into clinical practice.
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Affiliation(s)
- A Fournier
- Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - C Fallet
- Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - F Sadeghipour
- Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland; Center for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - N Perrottet
- Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland.
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Zhou K, Gattinger G. The Evolving Regulatory Paradigm of AI in MedTech: A Review of Perspectives and Where We Are Today. Ther Innov Regul Sci 2024; 58:456-464. [PMID: 38528278 PMCID: PMC11043174 DOI: 10.1007/s43441-024-00628-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/04/2024] [Indexed: 03/27/2024]
Abstract
Artificial intelligence (AI)-enabled technologies in the MedTech sector hold the promise to transform healthcare delivery by improving access, quality, and outcomes. As the regulatory contours of these technologies are being defined, there is a notable lack of literature on the key stakeholders such as the organizations and interest groups that have a significant input in shaping the regulatory framework. This article explores the perspectives and contributions of these stakeholders in shaping the regulatory paradigm of AI-enabled medical technologies. The formation of an AI regulatory framework requires the convergence of ethical, regulatory, technical, societal, and practical considerations. These multiple perspectives contribute to the various dimensions of an evolving regulatory paradigm. From the global governance guidelines set by the World Health Organization (WHO) to national regulations, the article sheds light not just on these multiple perspectives but also on their interconnectedness in shaping the regulatory landscape of AI.
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Affiliation(s)
- Karen Zhou
- Northeastern University, Toronto, ON, Canada.
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Kuhn S, Knitza J. [Orthopedics and trauma surgery in the digital age]. Orthopadie (Heidelb) 2024; 53:327-335. [PMID: 38538858 DOI: 10.1007/s00132-024-04496-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/06/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND Digital transformation is shaping the future of orthopedics and trauma surgery. Telemedicine, digital health applications, electronic patient records and artificial intelligence play a central role in this. These technologies have the potential to improve medical care, enable individualized patient treatment plans and reduce the burden on the treatment process. However, there are currently challenges in the areas of infrastructure, regulation, reimbursement and data protection. REALISING THE TRANSFORMATION Effective transformation requires a deep understanding of both technology and clinical practice. Orthopedic and trauma surgeons need to take a leadership role by actively engaging with new technologies, designing new treatment processes and enhancing their medical skills with digital and AI competencies. The integration of digital skills into medical education and specialist training will be crucial for actively shaping the digital transformation and exploiting its full potential.
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Affiliation(s)
- Sebastian Kuhn
- Institut für Digitale Medizin, Philipps Universität Marburg und Universitätsklinikum Gießen und Marburg, 35042, Marburg, Deutschland.
| | - Johannes Knitza
- Institut für Digitale Medizin, Philipps Universität Marburg und Universitätsklinikum Gießen und Marburg, 35042, Marburg, Deutschland
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Ma Y, Ma D, Xu X, Li J, Guan Z. Progress of MRI in predicting the circumferential resection margin of rectal cancer: A narrative review. Asian J Surg 2024; 47:2122-2131. [PMID: 38331609 DOI: 10.1016/j.asjsur.2024.01.131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/02/2024] [Accepted: 01/19/2024] [Indexed: 02/10/2024] Open
Abstract
Rectal cancer (RC) is the third most frequently diagnosed cancer worldwide, and the status of its circumferential resection margin (CRM) is of paramount significance for treatment strategies and prognosis. CRM involvement is defined as tumor touching or within 1 mm from the outermost part of tumor or outer border of the mesorectal or lymph node deposits to the resection margin. The incidence of involved CRM varied from 5.4 % to 36 %, which may associate with an in consistent definition of CRM, the quality of surgeries, and the different examination modalities. Although T and N status are essential factors in determining whether a patient should receive neoadjuvant therapy before surgery, CRM status is a powerful predictor of local and distant recurrence as well as survival rate. This review explores the significance of CRM, the various assessment methods, and the role of magnetic resonance imaging (MRI) and artificial intelligence-based MRI in predicting CRM status. MRI showed potential advantage in predicting CRM status with a high sensitivity and specificity compared to computed tomography (CT). We also discuss MRI advancements in RC imaging, including conventional MRI with body coil, high-resolution MRI with phased-array coil, and endorectal MRI. Along with a discussion of artificial intelligence-based MRI techniques to predict the CRM status of RCs before and after treatments.
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Affiliation(s)
- Yanqing Ma
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, 310014, China.
| | - Dongnan Ma
- Yangming College of Ningbo University, Ningbo, Zhejiang, 315010, China.
| | - Xiren Xu
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, 310014, China.
| | - Jie Li
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, 310014, China.
| | - Zheng Guan
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, 310014, China.
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Lacoste-Collin L. [What contribution can make artificial intelligence to urinary cytology?]. Ann Pathol 2024; 44:195-203. [PMID: 38614871 DOI: 10.1016/j.annpat.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/30/2024] [Accepted: 03/24/2024] [Indexed: 04/15/2024]
Abstract
Urinary cytology using the Paris system is still the method of choice for screening high-grade urothelial carcinomas. However, the use of the objective criteria described in this terminology shows a lack of inter- and intra-observer reproducibility. Moreover, if its sensitivity is excellent on instrumented urine, it remains insufficient on voided urine samples. Urinary cytology appears to be an excellent model for the application of artificial intelligence to improve performance, since the objective criteria of the Paris system are defined at cellular level, and the resulting diagnostic approach is presented in a highly "algorithmic" way. Nevertheless, there is no commercially available morphological diagnostic aid, and very few predictive devices are still undergoing clinical validation. The analysis of different systems using artificial intelligence in urinary cytology rises clear prospects for mutual contributions.
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129
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Depeweg S, Rothkopf CA, Jäkel F. Solving Bongard Problems With a Visual Language and Pragmatic Constraints. Cogn Sci 2024; 48:e13432. [PMID: 38700123 DOI: 10.1111/cogs.13432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 02/15/2024] [Accepted: 02/26/2024] [Indexed: 05/05/2024]
Abstract
More than 50 years ago, Bongard introduced 100 visual concept learning problems as a challenge for artificial vision systems. These problems are now known as Bongard problems. Although they are well known in cognitive science and artificial intelligence, only very little progress has been made toward building systems that can solve a substantial subset of them. In the system presented here, visual features are extracted through image processing and then translated into a symbolic visual vocabulary. We introduce a formal language that allows representing compositional visual concepts based on this vocabulary. Using this language and Bayesian inference, concepts can be induced from the examples that are provided in each problem. We find a reasonable agreement between the concepts with high posterior probability and the solutions formulated by Bongard himself for a subset of 35 problems. While this approach is far from solving Bongard problems like humans, it does considerably better than previous approaches. We discuss the issues we encountered while developing this system and their continuing relevance for understanding visual cognition. For instance, contrary to other concept learning problems, the examples are not random in Bongard problems; instead they are carefully chosen to ensure that the concept can be induced, and we found it helpful to take the resulting pragmatic constraints into account.
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Affiliation(s)
| | - Contantin A Rothkopf
- Centre for Cognitive Science & Institute of Psychology, Technische Universität Darmstadt
- Frankfurt Institute for Advanced Studies, Frankfurt am Main
| | - Frank Jäkel
- Centre for Cognitive Science & Institute of Psychology, Technische Universität Darmstadt
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130
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Papastratis I, Stergioulas A, Konstantinidis D, Daras P, Dimitropoulos K. Can ChatGPT provide appropriate meal plans for NCD patients? Nutrition 2024; 121:112291. [PMID: 38359704 DOI: 10.1016/j.nut.2023.112291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 10/30/2023] [Indexed: 02/17/2024]
Abstract
OBJECTIVES Dietary habits significantly affect health conditions and are closely related to the onset and progression of non-communicable diseases (NCDs). Consequently, a well-balanced diet plays an important role in lessening the effects of various disorders, including NCDs. Several artificial intelligence recommendation systems have been developed to propose healthy and nutritious diets. Most of these systems use expert knowledge and guidelines to provide tailored diets and encourage healthier eating habits. However, new advances in large language models such as ChatGPT, with their ability to produce human-like responses, have led individuals to search for advice in several tasks, including diet recommendations. This study aimed to determine the ability of ChatGPT models to generate appropriate personalized meal plans for patients with obesity, cardiovascular diseases, and type 2 diabetes. METHODS Using a state-of-the-art knowledge-based recommendation system as a reference, we assessed the meal plans generated by two large language models in terms of energy intake, nutrient accuracy, and meal variability. RESULTS Experimental results with different user profiles revealed the potential of ChatGPT models to provide personalized nutritional advice. CONCLUSION Additional supervision and guidance by nutrition experts or knowledge-based systems are required to ensure meal appropriateness for users with NCDs.
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Affiliation(s)
- Ilias Papastratis
- The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece.
| | - Andreas Stergioulas
- The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece
| | - Dimitrios Konstantinidis
- The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece
| | - Petros Daras
- The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece
| | - Kosmas Dimitropoulos
- The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece
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González Bermúdez A, Carramiñana D, Bernardos AM, Bergesio L, Besada JA. A fusion architecture to deliver multipurpose mobile health services. Comput Biol Med 2024; 173:108344. [PMID: 38574531 DOI: 10.1016/j.compbiomed.2024.108344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/13/2024] [Accepted: 03/17/2024] [Indexed: 04/06/2024]
Abstract
Mobile Health (mHealth) services typically make use of customized software architectures, leading to development-dependent fragmentation. Nevertheless, irrespective of their specific purpose, most mHealth services share common functionalities, where standard pieces could be reused or adapted to expedite service deployment and even extend the follow-up of appearing conditions under the same service. To harness compatibility and reuse, this article presents a data fusion architecture proposing a common design framework for mHealth services. An exhaustive mapping of mHealth functionalities identified in the literature serves as starting point. The architecture is then conceptualized making use of the Joint Directors of Laboratories (JDL) data fusion model. The aim of the architecture is to exploit the multi-source data acquisition capabilities supported by smartphones and Internet of Things devices, and artificial intelligence-enabled feature fusion. A series of interconnected fusion layers ensure streamlined data management; each layer is composed of microservices which may be implemented or omitted depending on the specific goals of the healthcare service. Moreover, the architecture considers essential features related to authentication mechanisms, data sharing protocols, practitioner-patient communication, context-based notifications and tailored visualization interfaces. The effectiveness of the architecture is underscored by its instantiation for four real cases, encompassing risk assessment for youth with mental health issues, remote monitoring for SARS-CoV-2 patients, liquid intake control for kidney disease patients, and peritoneal dialysis treatment support. This breadth of applications exemplifies how the architecture can effectively serve as a guidance framework to accelerate the design of mHealth services.
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Affiliation(s)
- Ana González Bermúdez
- Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, Spain.
| | - David Carramiñana
- Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, Spain
| | - Ana M Bernardos
- Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, Spain
| | - Luca Bergesio
- Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, Spain
| | - Juan A Besada
- Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, Spain
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Noda R, Izaki Y, Kitano F, Komatsu J, Ichikawa D, Shibagaki Y. Performance of ChatGPT and Bard in self-assessment questions for nephrology board renewal. Clin Exp Nephrol 2024; 28:465-469. [PMID: 38353783 DOI: 10.1007/s10157-023-02451-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 12/25/2023] [Indexed: 04/23/2024]
Abstract
BACKGROUND Large language models (LLMs) have impacted advances in artificial intelligence. While LLMs have demonstrated high performance in general medical examinations, their performance in specialized areas such as nephrology is unclear. This study aimed to evaluate ChatGPT and Bard in their potential nephrology applications. METHODS Ninety-nine questions from the Self-Assessment Questions for Nephrology Board Renewal from 2018 to 2022 were presented to two versions of ChatGPT (GPT-3.5 and GPT-4) and Bard. We calculated the correct answer rates for the five years, each year, and question categories and checked whether they exceeded the pass criterion. The correct answer rates were compared with those of the nephrology residents. RESULTS The overall correct answer rates for GPT-3.5, GPT-4, and Bard were 31.3% (31/99), 54.5% (54/99), and 32.3% (32/99), respectively, thus GPT-4 significantly outperformed GPT-3.5 (p < 0.01) and Bard (p < 0.01). GPT-4 passed in three years, barely meeting the minimum threshold in two. GPT-4 demonstrated significantly higher performance in problem-solving, clinical, and non-image questions than GPT-3.5 and Bard. GPT-4's performance was between third- and fourth-year nephrology residents. CONCLUSIONS GPT-4 outperformed GPT-3.5 and Bard and met the Nephrology Board renewal standards in specific years, albeit marginally. These results highlight LLMs' potential and limitations in nephrology. As LLMs advance, nephrologists should understand their performance for future applications.
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Affiliation(s)
- Ryunosuke Noda
- Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan.
| | - Yuto Izaki
- Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Fumiya Kitano
- Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Jun Komatsu
- Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Daisuke Ichikawa
- Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Yugo Shibagaki
- Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan
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Tournois L, Trousset V, Hatsch D, Delabarde T, Ludes B, Lefèvre T. Artificial intelligence in the practice of forensic medicine: a scoping review. Int J Legal Med 2024; 138:1023-1037. [PMID: 38087052 PMCID: PMC11003914 DOI: 10.1007/s00414-023-03140-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 11/21/2023] [Indexed: 04/11/2024]
Abstract
Forensic medicine is a thriving application field for artificial intelligence (AI). Indeed, AI applications intended to forensic pathologists or forensic physicians have emerged since the last decade. For example, AI models were developed to help estimate the biological age of migrants or human remains. However, the uses of AI applications by forensic pathologists or physicians and their levels of integration in medicolegal practices are not well described yet. Therefore, a scoping review was conducted on PubMed, ScienceDirect, and Scopus databases. This review included articles that mention any AI application used by forensic pathologists or physicians in practice or any AI model applied in one expertise field of the forensic pathologist or physician. Articles in other languages than English or French or dealing mainly with complementary analyses handled by experts who are not forensic pathologists or physicians or with AI to analyze data for research purposes in forensic medicine were excluded from this review. All the relevant information was retrieved in each article from a grid analysis derived and adapted from the TRIPOD checklist. This review included 35 articles and revealed that AI applications are developed in thanatology and in clinical forensic medicine. However, those applications seem to mainly remain in research and development stages. Indeed, the use of AI applications by forensic pathologists or physicians is not actual due to issues discussed in this article. Finally, the integration of AI in daily medicolegal practice involves not only forensic pathologists or physicians but also legal professionals.
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Affiliation(s)
- Laurent Tournois
- Université Paris Cité, CNRS UMR 8045, 75006, Paris, France.
- BioSilicium, Riom, France.
| | - Victor Trousset
- IRIS Institut de Recherche Interdisciplinaire Sur Les Enjeux Sociaux, UMR8156 CNRS - U997 Inserm - EHESS - Université Sorbonne Paris Nord, Paris, France
- Department of Forensic and Social Medicine, AP-HP, Jean Verdier Hospital, Bondy, France
| | | | - Tania Delabarde
- Université Paris Cité, CNRS UMR 8045, 75006, Paris, France
- Institut Médico-Légal de Paris, Paris, France
| | - Bertrand Ludes
- Université Paris Cité, CNRS UMR 8045, 75006, Paris, France
- Institut Médico-Légal de Paris, Paris, France
| | - Thomas Lefèvre
- IRIS Institut de Recherche Interdisciplinaire Sur Les Enjeux Sociaux, UMR8156 CNRS - U997 Inserm - EHESS - Université Sorbonne Paris Nord, Paris, France
- Department of Forensic and Social Medicine, AP-HP, Jean Verdier Hospital, Bondy, France
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González R, Poenaru D, Woo R, Trappey AF, Carter S, Darcy D, Encisco E, Gulack B, Miniati D, Tombash E, Huang EY. ChatGPT: What Every Pediatric Surgeon Should Know About Its Potential Uses and Pitfalls. J Pediatr Surg 2024; 59:941-947. [PMID: 38336588 DOI: 10.1016/j.jpedsurg.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/30/2023] [Accepted: 01/09/2024] [Indexed: 02/12/2024]
Abstract
ChatGPT - currently the most popular generative artificial intelligence system - has been revolutionizing the world and healthcare since its release in November 2022. ChatGPT is a conversational chatbot that uses machine learning algorithms to enhance its replies based on user interactions and is a part of a broader effort to develop natural language processing that can assist people in their daily lives by understanding and responding to human language in a useful and engaging way. Thus far, many potential applications within healthcare have been described, despite its relatively recent release. This manuscript offers the pediatric surgical community a primer on this new technology and discusses some initial observations about its potential uses and pitfalls. Moreover, it introduces the perspectives of medical journals and surgical societies regarding the use of this artificial intelligence chatbot. As ChatGPT and other large language models continue to evolve, it is the responsibility of the pediatric surgery community to stay abreast of these changes and play an active role in safely incorporating them into our field for the benefit of our patients. LEVEL OF EVIDENCE: V.
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Affiliation(s)
- Raquel González
- Division of Pediatric Surgery, Johns Hopkins All Children's Hospital, 501 6th Avenue S, Saint Petersburg, FL, 33701, USA.
| | - Dan Poenaru
- McGill University, 5252 Boul. De Maissonneuve O. rm. 3E.05, Montréal, QC, H4a 3S5, Canada
| | - Russell Woo
- Department of Surgery, Division of Pediatric Surgery, University of Hawai'i, John A. Burns School of Medicine, 1319 Punahou Street, Suite 600, Honolulu, HI, 96826, USA
| | - A Francois Trappey
- Pediatric General and Thoracic Surgery, Brooke Army Medical Center, 3551 Roger Brooke Dr, Fort Sam Houston, TX, 78234, USA
| | - Stewart Carter
- Division of Pediatric Surgery, University of Louisville, Norton Children's Hospital, 315 East Broadway, Suite 565, Louisville, KY, 40202, USA
| | - David Darcy
- Golisano Children's Hospital, University of Rochester Medical Center, 601 Elmwood Avenue, Box SURG, Rochester, NY, 14642, USA
| | - Ellen Encisco
- Division of Pediatric General and Thoracic Surgery, Cincinnati Children's Hospital, 3333 Burnet Ave, Cincinnati, OH, 45229, USA
| | - Brian Gulack
- Rush University Medical Center, 1653 W Congress Parkway, Kellogg, Chicago, IL, 60612, USA
| | - Doug Miniati
- Department of Pediatric Surgery, Kaiser Permanente Roseville, 1600 Eureka Road, Building C, Suite C35, Roseville, CA, 95661, USA
| | - Edzhem Tombash
- Division of Pediatric General and Thoracic Surgery, Cincinnati Children's Hospital, 3333 Burnet Ave, Cincinnati, OH, 45229, USA
| | - Eunice Y Huang
- Vanderbilt University Medical Center, Monroe Carell Jr. Children's Hospital, 2200 Children's Way, Suite 7100, Nashville, TN, 37232, USA
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Garcés-Jiménez A, Polo-Luque ML, Gómez-Pulido JA, Rodríguez-Puyol D, Gómez-Pulido JM. Predictive health monitoring: Leveraging artificial intelligence for early detection of infectious diseases in nursing home residents through discontinuous vital signs analysis. Comput Biol Med 2024; 174:108469. [PMID: 38636331 DOI: 10.1016/j.compbiomed.2024.108469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 04/20/2024]
Abstract
This research addresses the problem of detecting acute respiratory, urinary tract, and other infectious diseases in elderly nursing home residents using machine learning algorithms. The study analyzes data extracted from multiple vital signs and other contextual information for diagnostic purposes. The daily data collection process encounters sampling constraints due to weekends, holidays, shift changes, staff turnover, and equipment breakdowns, resulting in numerous nulls, repeated readings, outliers, and meaningless values. The short time series generated also pose a challenge to analysis, preventing the extraction of seasonal information or consistent trends. Blind data collection results in most of the data coming from periods when residents are healthy, resulting in excessively imbalanced data. This study proposes a data cleaning process and then builds a mechanism that reproduces the basal activity of the residents to improve the classification of the disease. The results show that the proposed basal module-assisted machine learning techniques allow anticipating diagnostics 2, 3 or 4 days before doctors decide to start treatment with antibiotics, achieving a performance measured by the area-under-the-curve metric of 0.857. The contributions of this work are: (1) a new data cleaning process; (2) the analysis of contextual information to improve data quality; (3) the generation of a baseline measure for relative comparison; and (4) the use of either binary (disease/no disease) or multiclass classification, differentiating among types of infections and showing the advantages of multiclass versus binary classification. From a medical point of view, the anticipated detection of infectious diseases in institutionalized individuals is brand new.
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Affiliation(s)
- Alberto Garcés-Jiménez
- Department of Computer Science, Universidad de Alcalá, Politechnic School, Alcala de Henares, 28805, Spain
| | - María-Luz Polo-Luque
- Department of Nursing and Physiotherapy, Universidad de Alcalá, Faculty of Medicine and Health Sciences, Alcala de Henares, 28805, Spain
| | - Juan A Gómez-Pulido
- Department of Technologies of Computers and Communications, Universidad de Extremadura, School of Technology, Cáceres, 10003, Spain.
| | - Diego Rodríguez-Puyol
- Department of Medicine and Medical Specialties, Research Foundation of the University Hospital Príncipe de Asturias, Campus Científico Tecnológico, Alcala de Henares, 28805, Spain
| | - José M Gómez-Pulido
- Department of Computer Science, Universidad de Alcalá, Politechnic School, Alcala de Henares, 28805, Spain
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Wu S, Shen R, Hong G, Luo Y, Wan H, Feng J, Chen Z, Jiang F, Wang Y, Liao C, Li X, Liu B, Huang X, Liu K, Qin P, Wang Y, Xie Y, Ouyang N, Huang J, Lin T. Development and validation of an artificial intelligence-based model for detecting urothelial carcinoma using urine cytology images: a multicentre, diagnostic study with prospective validation. EClinicalMedicine 2024; 71:102566. [PMID: 38686219 PMCID: PMC11056596 DOI: 10.1016/j.eclinm.2024.102566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 03/01/2024] [Accepted: 03/12/2024] [Indexed: 05/02/2024] Open
Abstract
Background Urine cytology is an important non-invasive examination for urothelial carcinoma (UC) diagnosis and follow-up. We aimed to explore whether artificial intelligence (AI) can enhance the sensitivity of urine cytology and help avoid unnecessary endoscopy. Methods In this multicentre diagnostic study, consecutive patients who underwent liquid-based urine cytology examinations at four hospitals in China were included for model development and validation. Patients who declined surgery and lacked associated histopathology results, those diagnosed with rare subtype tumours of the urinary tract, or had low-quality images were excluded from the study. All liquid-based cytology slides were scanned into whole-slide images (WSIs) at 40 × magnification and the WSI-labels were derived from the corresponding histopathology results. The Precision Urine Cytology AI Solution (PUCAS) was composed of three distinct stages (patch extraction, features extraction, and classification diagnosis) and was trained to identify important WSI features associated with UC diagnosis. The diagnostic sensitivity was mainly used to validate the performance of PUCAS in retrospective and prospective validation cohorts. This study is registered with the ChiCTR, ChiCTR2300073192. Findings Between January 1, 2018 and October 31, 2022, 2641 patients were retrospectively recruited in the training cohort, and 2335 in retrospective validation cohorts; 400 eligible patients were enrolled in the prospective validation cohort between July 7, 2023 and September 15, 2023. The sensitivity of PUCAS ranged from 0.922 (95% CI: 0.811-0.978) to 1.000 (0.782-1.000) in retrospective validation cohorts, and was 0.896 (0.837-0.939) in prospective validation cohort. The PUCAS model also exhibited a good performance in detecting malignancy within atypical urothelial cells cases, with a sensitivity of over 0.84. In the recurrence detection scenario, PUCAS could reduce 57.5% of endoscopy use with a negative predictive value of 96.4%. Interpretation PUCAS may help to improve the sensitivity of urine cytology, reduce misdiagnoses of UC, avoid unnecessary endoscopy, and reduce the clinical burden in resource-limited areas. The further validation in other countries is needed. Funding National Natural Science Foundation of China; Key Program of the National Natural Science Foundation of China; the National Science Foundation for Distinguished Young Scholars; the Science and Technology Planning Project of Guangdong Province; the National Key Research and Development Programme of China; Guangdong Provincial Clinical Research Centre for Urological Diseases.
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Affiliation(s)
- Shaoxu Wu
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Clinical Research Centre for Urological Diseases, Guangzhou, China
| | - Runnan Shen
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Guibin Hong
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yun Luo
- Department of Urology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Huan Wan
- Department of Pathology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiahao Feng
- CellsVision Medical Technology Services Co., Ltd., Guangzhou, China
| | - Zeshi Chen
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Fan Jiang
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yun Wang
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Chengxiao Liao
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiaoyang Li
- Department of Urology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Bohao Liu
- Department of Urology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiaowei Huang
- CellsVision Medical Technology Services Co., Ltd., Guangzhou, China
| | - Kai Liu
- CellsVision Medical Technology Services Co., Ltd., Guangzhou, China
| | - Ping Qin
- Department of Pathology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yahui Wang
- Department of Urology, The Shen-Shan Central Hospital, Shanwei, China
| | - Ye Xie
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Nengtai Ouyang
- Department of Pathology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jian Huang
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Clinical Research Centre for Urological Diseases, Guangzhou, China
| | - Tianxin Lin
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Clinical Research Centre for Urological Diseases, Guangzhou, China
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Adams LC, Bressem KK, Ziegeler K, Vahldiek JL, Poddubnyy D. Artificial intelligence to analyze magnetic resonance imaging in rheumatology. Joint Bone Spine 2024; 91:105651. [PMID: 37797827 DOI: 10.1016/j.jbspin.2023.105651] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 08/29/2023] [Accepted: 09/26/2023] [Indexed: 10/07/2023]
Abstract
Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management.
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Affiliation(s)
- Lisa C Adams
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Keno K Bressem
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Katharina Ziegeler
- Department of Hematology, Oncology , and Cancer Immunology, Campus Charité Mitte, Charité Universitätsmedizin Berlin, Germany; Evidia Radiologie am Rheumazentrum Ruhrgebiet, Germany
| | - Janis L Vahldiek
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Denis Poddubnyy
- Department of Gastroenterology, Infectious Diseases and Rheumatology (including Nutrition Medicine), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
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Seitz J, Mohr Durdez T, Lotteau S, Bars C, Pisapia A, Gitenay E, Monteau J, Reist M, Serdi M, Dayot A, Bremondy M, Benadel M, Siame S, Appetiti A, Milpied P, Kalifa J. Artificial intelligence-adjudicated spatiotemporal dispersion: A patient-unique fingerprint of persistent atrial fibrillation. Heart Rhythm 2024; 21:540-552. [PMID: 38215808 DOI: 10.1016/j.hrthm.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 12/27/2023] [Accepted: 01/06/2024] [Indexed: 01/14/2024]
Abstract
BACKGROUND Spatiotemporal dispersion-guided ablation is a tailored approach for patients in persistent atrial fibrillation (PsAF). The characterization of dispersion extent and distribution and its association with common clinical descriptors of PsAF patients has not been studied. OBJECTIVES Artificial intelligence-adjudicated dispersion extent and distribution (AI-DED) was obtained with a machine/deep learning classifier (VX1 Software, Volta Medical) in PsAF patients undergoing ablation. The purpose of this study was to test the hypothesis that AI-DED is unique to each patient and independent of common procedural and clinical parameters. METHODS In a subanalysis of the Ev-AIFib study (NCT03434964), spatiotemporal dispersion maps were built with VX1 software in 78 consecutive persistent and long-standing PsAF patients. AI-DED was quantified using 2 distinct approaches (visual regional characterization or automated global quantification of AI-DED). RESULTS AI-DED paired-subregion Euclidean distance measurements between 78 patients (average distance 5.07 ± 0.60; min 2.23; max 9.75) demonstrate that AI-DED is a patient-unique characteristic of PsAF. Importantly, both AF type and AF history do not correlate with AI-DED levels (R2 = 0.006, P = .53; and R2 = 0.03, P = .25, respectively). The most extensive AI-DED levels are not associated with poorer procedural (83%, 81%, and 83% of AF termination in low, medium, and high dispersion groups, respectively; P = .954) and long-term (88%, 75%, and 91% of freedom from AF/atrial tachycardia after multiple procedures; P = .517) outcomes. CONCLUSION The atrial distribution and extent of multipolar electrogram spatiotemporal dispersion follow a nonrandom, albeit patient-unique, distribution in PsAF patients. AI-DED may represent a procedure-implementable fingerprint of the PsAF substrate.
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Cho JS, Park JH. Application of artificial intelligence in hypertension. Clin Hypertens 2024; 30:11. [PMID: 38689376 PMCID: PMC11061896 DOI: 10.1186/s40885-024-00266-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 02/13/2024] [Indexed: 05/02/2024] Open
Abstract
Hypertension is an important modifiable risk factor for morbidity and mortality associated with cardiovascular disease. The incidence of hypertension is increasing not only in Korea but also in many Western countries due to the aging of the population and the increase in unhealthy lifestyles. However, hypertension control rates remain low due to poor adherence to antihypertensive medications, low awareness of hypertension, and numerous factors that contribute to hypertension, including diet, environment, lifestyle, obesity, and genetics. Because artificial intelligence (AI) involves data-driven algorithms, AI is an asset to understanding chronic diseases that are influenced by multiple factors, such as hypertension. Although several hypertension studies using AI have been published recently, most are exploratory descriptive studies that are often difficult for clinicians to understand and have little clinical relevance. This review aims to provide a clinician-centered perspective on AI by showing recent studies on the relevance of AI for patients with hypertension. The review is organized into sections on blood pressure measurement and hypertension diagnosis, prognosis, and management.
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Affiliation(s)
- Jung Sun Cho
- Division of Cardiology, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Catholic Research Institute for Intractable Cardiovascular Disease, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jae-Hyeong Park
- Department of Cardiology in Internal Medicine, Chungnam National University, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, 35015, Daejeon, Republic of Korea.
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Hoon Yun B, Yu HY, Kim H, Myoung S, Yeo N, Choi J, Sook Chun H, Kim H, Ahn S. Geographical discrimination of Asian red pepper powders using 1H NMR spectroscopy and deep learning-based convolution neural networks. Food Chem 2024; 439:138082. [PMID: 38070234 DOI: 10.1016/j.foodchem.2023.138082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/24/2023] [Accepted: 11/24/2023] [Indexed: 01/10/2024]
Abstract
This study investigated an innovative approach to discriminate the geographical origins of Asian red pepper powders by analyzing one-dimensional 1H NMR spectra through a deep learning-based convolution neural network (CNN). 1H NMR spectra were collected from 300 samples originating from China, Korea, and Vietnam and used as input data. Principal component analysis - linear discriminant analysis and support vector machine models were employed for comparison. Bayesian optimization was used for hyperparameter optimization, and cross-validation was performed to prevent overfitting. As a result, all three models discriminated the origins of the test samples with over 95 % accuracy. Specifically, the CNN models achieved a 100 % accuracy rate. Gradient-weighted class activation mapping analysis verified that the CNN models recognized the origins of the samples based on variations in metabolite distributions. This research demonstrated the potential of deep learning-based classification of 1H NMR spectra as an accurate and reliable approach for determining the geographical origins of various foods.
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Affiliation(s)
- Byung Hoon Yun
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Hyo-Yeon Yu
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Hyeongmin Kim
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Sangki Myoung
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Neulhwi Yeo
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Jongwon Choi
- Department of Advanced Imaging, Chung-Ang University, Seoul 06974, South Korea.
| | - Hyang Sook Chun
- Department of Food Science & Technology, Chung-Ang University, Anseong 17546, South Korea.
| | - Hyeonjin Kim
- Department of Medical Sciences, Seoul National University, Seoul 03080, South Korea; Department of Radiology, Seoul National University Hospital, Seoul 03080, South Korea.
| | - Sangdoo Ahn
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
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Chen J, Huang Z, Jiang Y, Wu H, Tian H, Cui C, Shi S, Tang S, Xu J, Xu D, Dong F. Diagnostic Performance of Deep Learning in Video-Based Ultrasonography for Breast Cancer: A Retrospective Multicentre Study. Ultrasound Med Biol 2024; 50:722-728. [PMID: 38369431 DOI: 10.1016/j.ultrasmedbio.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/08/2024] [Accepted: 01/16/2024] [Indexed: 02/20/2024]
Abstract
OBJECTIVE Although ultrasound is a common tool for breast cancer screening, its accuracy is often operator-dependent. In this study, we proposed a new automated deep-learning framework that extracts video-based ultrasound data for breast cancer screening. METHODS Our framework incorporates DenseNet121, MobileNet, and Xception as backbones for both video- and image-based models. We used data from 3907 patients to train and evaluate the models, which were tested using video- and image-based methods, as well as reader studies with human experts. RESULTS This study evaluated 3907 female patients aged 22 to 86 years. The results indicated that the MobileNet video model achieved an AUROC of 0.961 in prospective data testing, surpassing the DenseNet121 video model. In real-world data testing, it demonstrated an accuracy of 92.59%, outperforming both the DenseNet121 and Xception video models, and exceeding the 76.00% to 85.60% accuracy range of human experts. Additionally, the MobileNet video model exceeded the performance of image models and other video models across all evaluation metrics, including accuracy, sensitivity, specificity, F1 score, and AUC. Its exceptional performance, particularly suitable for resource-limited clinical settings, demonstrates its potential for clinical application in breast cancer screening. CONCLUSIONS The level of expertise reached by the video models was greater than that achieved by image-based models. We have developed an artificial intelligence framework based on videos that may be able to aid breast cancer diagnosis and alleviate the shortage of experienced experts.
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Affiliation(s)
- Jing Chen
- Ultrasound Department, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong, China
| | | | - Yitao Jiang
- Research and development department, Illuminate, LLC, Shenzhen, Guangdong, China
| | - Huaiyu Wu
- Ultrasound Department, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong, China
| | - Hongtian Tian
- Ultrasound Department, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong, China
| | - Chen Cui
- Research and development department, Illuminate, LLC, Shenzhen, Guangdong, China
| | - Siyuan Shi
- Research and development department, Illuminate, LLC, Shenzhen, Guangdong, China
| | | | - Jinfeng Xu
- Ultrasound Department, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong, China
| | - Dong Xu
- Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Fajin Dong
- Ultrasound Department, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong, China; Jinan University, Guangzhou, Guangdong, China.
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Ho YS, Fülöp T, Krisanapan P, Soliman KM, Cheungpasitporn W. Artificial intelligence and machine learning trends in kidney care. Am J Med Sci 2024; 367:281-295. [PMID: 38281623 DOI: 10.1016/j.amjms.2024.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 12/12/2023] [Accepted: 01/23/2024] [Indexed: 01/30/2024]
Abstract
BACKGROUND The integration of artificial intelligence (AI) and machine learning (ML) in kidney care has seen a significant rise in recent years. This study specifically analyzed AI and ML research publications related to kidney care to identify leading authors, institutions, and countries in this area. It aimed to examine publication trends and patterns, and to explore the impact of collaborative efforts on citation metrics. METHODS The study used the Science Citation Index Expanded (SCI-EXPANDED) of Clarivate Analytics Web of Science Core Collection to search for AI and machine learning publications related to nephrology from 1992 to 2021. The authors used quotation marks and Boolean operator "or" to search for keywords in the title, abstract, author keywords, and Keywords Plus. In addition, the 'front page' filter was applied. A total of 5425 documents were identified and analyzed. RESULTS The results showed that articles represent 75% of the analyzed documents, with an average author to publications ratio of 7.4 and an average number of citations per publication in 2021 of 18. English articles had a higher citation rate than non-English articles. The USA dominated in all publication indicators, followed by China. Notably, the research also showed that collaborative efforts tend to result in higher citation rates. A significant portion of the publications were found in urology journals, emphasizing the broader scope of kidney care beyond traditional nephrology. CONCLUSIONS The findings underscore the importance of AI and ML in enhancing kidney care, offering a roadmap for future research and implementation in this expanding field.
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Affiliation(s)
- Yuh-Shan Ho
- Trend Research Centre, Asia University, Wufeng, Taichung, Taiwan
| | - Tibor Fülöp
- Medical Services, Ralph H. Johnson VA Medical Center, Charleston, SC, USA; Department of Medicine, Division of Nephrology, Medical University of South Carolina, Charleston, SC, USA.
| | - Pajaree Krisanapan
- Division of Nephrology, Department of Internal Medicine, Thammasat University, Pathum Thani, Thailand, 12120
| | - Karim M Soliman
- Medical Services, Ralph H. Johnson VA Medical Center, Charleston, SC, USA; Department of Medicine, Division of Nephrology, Medical University of South Carolina, Charleston, SC, USA
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Bose S, Banerjee S, Kumar S, Saha A, Nandy D, Hazra S. Review of applications of artificial intelligence (AI) methods in crop research. J Appl Genet 2024; 65:225-240. [PMID: 38216788 DOI: 10.1007/s13353-023-00826-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 12/23/2023] [Accepted: 12/26/2023] [Indexed: 01/14/2024]
Abstract
Sophisticated and modern crop improvement techniques can bridge the gap for feeding the ever-increasing population. Artificial intelligence (AI) refers to the simulation of human intelligence in machines, which refers to the application of computational algorithms, machine learning (ML) and deep learning (DL) techniques. This is aimed to generalise patterns and relationships from historical data, employing various mathematical optimisation techniques thus making prediction models for facilitating selection of superior genotypes. These techniques are less resource intensive and can solve the problem based on the analysis of large-scale phenotypic datasets. ML for genomic selection (GS) uses high-throughput genotyping technologies to gather genetic information on a large number of markers across the genome. The prediction of GS models is based on the mathematical relation between genotypic and phenotypic data from the training population. ML techniques have emerged as powerful tools for genome editing through analysing large-scale genomic data and facilitating the development of accurate prediction models. Precise phenotyping is a prerequisite to advance crop breeding for solving agricultural production-related issues. ML algorithms can solve this problem through generating predictive models, based on the analysis of large-scale phenotypic datasets. DL models also have the potential reliability of precise phenotyping. This review provides a comprehensive overview on various ML and DL models, their applications, potential to enhance the efficiency, specificity and safety towards advanced crop improvement protocols such as genomic selection, genome editing, along with phenotypic prediction to promote accelerated breeding.
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Affiliation(s)
- Suvojit Bose
- Department of Vegetables and Spice Crops, Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, 736165, West Bengal, India
| | | | - Soumya Kumar
- School of Agricultural Sciences, JIS University, Kolkata, 700109, West Bengal, India
| | - Akash Saha
- School of Agricultural Sciences, JIS University, Kolkata, 700109, West Bengal, India
| | - Debalina Nandy
- School of Agricultural Sciences, JIS University, Kolkata, 700109, West Bengal, India
| | - Soham Hazra
- Department of Agriculture, Brainware University, Barasat, 700125, West Bengal, India.
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Medina-Franco JL, López-López E. What is the plausibility that all drugs will be designed by computers by the end of the decade? Expert Opin Drug Discov 2024; 19:507-510. [PMID: 38501288 DOI: 10.1080/17460441.2024.2331734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 03/13/2024] [Indexed: 03/20/2024]
Affiliation(s)
- José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico, Mexico
| | - Edgar López-López
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico, Mexico
- Department of Chemistry and Graduate Program in Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico, Mexico
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145
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Wu S, Wang Y, Hong G, Luo Y, Lin Z, Shen R, Zeng H, Xu A, Wu P, Xiao M, Li X, Rao P, Yang Q, Feng Z, He Q, Jiang F, Xie Y, Liao C, Huang X, Chen R, Lin T. An artificial intelligence model for detecting pathological lymph node metastasis in prostate cancer using whole slide images: a retrospective, multicentre, diagnostic study. EClinicalMedicine 2024; 71:102580. [PMID: 38618206 PMCID: PMC11015342 DOI: 10.1016/j.eclinm.2024.102580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/17/2024] [Accepted: 03/18/2024] [Indexed: 04/16/2024] Open
Abstract
Background The pathological examination of lymph node metastasis (LNM) is crucial for treating prostate cancer (PCa). However, the limitations with naked-eye detection and pathologist workload contribute to a high missed-diagnosis rate for nodal micrometastasis. We aimed to develop an artificial intelligence (AI)-based, time-efficient, and high-precision PCa LNM detector (ProCaLNMD) and evaluate its clinical application value. Methods In this multicentre, retrospective, diagnostic study, consecutive patients with PCa who underwent radical prostatectomy and pelvic lymph node dissection at five centres between Sep 2, 2013 and Apr 28, 2023 were included, and histopathological slides of resected lymph nodes were collected and digitised as whole-slide images for model development and validation. ProCaLNMD was trained at a dataset from a single centre (the Sun Yat-sen Memorial Hospital of Sun Yat-sen University [SYSMH]), and externally validated in the other four centres. A bladder cancer dataset from SYSMH was used to further validate ProCaLNMD, and an additional validation (human-AI comparison and collaboration study) containing consecutive patients with PCa from SYSMH was implemented to evaluate the application value of integrating ProCaLNMD into the clinical workflow. The primary endpoint was the area under the receiver operating characteristic curve (AUROC) of ProCaLNMD. In addition, the performance measures for pathologists with ProCaLNMD assistance was also assessed. Findings In total, 8225 slides from 1297 patients with PCa were collected and digitised. Overall, 8158 slides (18,761 lymph nodes) from 1297 patients with PCa (median age 68 years [interquartile range 64-73]; 331 [26%] with LNM) were used to train and validate ProCaLNMD. The AUROC of ProCaLNMD ranged from 0.975 (95% confidence interval 0.953-0.998) to 0.992 (0.982-1.000) in the training and validation datasets, with sensitivities > 0.955 and specificities > 0.921. ProCaLNMD also demonstrated an AUROC of 0.979 in the cross-cancer dataset. ProCaLNMD use triggered true reclassification in 43 (4.3%) slides in which micrometastatic tumour regions were initially missed by pathologists, thereby correcting 28 (8.5%) missed-diagnosed cases of previous routine pathological reports. In the human-AI comparison and collaboration study, the sensitivity of ProCaLNMD (0.983 [0.908-1.000]) surpassed that of two junior pathologists (0.862 [0.746-0.939], P = 0.023; 0.879 [0.767-0.950], P = 0.041) by 10-12% and showed no difference to that of two senior pathologists (both 0.983 [0.908-1.000], both P > 0.99). Furthermore, ProCaLNMD significantly boosted the diagnostic sensitivity of two junior pathologists (both P = 0.041) to the level of senior pathologists (both P > 0.99), and substantially reduced the four pathologists' slide reviewing time (-31%, P < 0.0001; -34%, P < 0.0001; -29%, P < 0.0001; and -27%, P = 0.00031). Interpretation ProCaLNMD demonstrated high diagnostic capabilities for identifying LNM in prostate cancer, reducing the likelihood of missed diagnoses by pathologists and decreasing the slide reviewing time, highlighting its potential for clinical application. Funding National Natural Science Foundation of China, the Science and Technology Planning Project of Guangdong Province, the National Key Research and Development Programme of China, the Guangdong Provincial Clinical Research Centre for Urological Diseases, and the Science and Technology Projects in Guangzhou.
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Affiliation(s)
- Shaoxu Wu
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Clinical Research Centre for Urological Diseases, Guangzhou, China
| | - Yun Wang
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Guibin Hong
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yun Luo
- Department of Urology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhen Lin
- CellsVision Medical Technology Services Co., Ltd., Guangzhou, China
| | - Runnan Shen
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Hong Zeng
- Department of Pathology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Abai Xu
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Peng Wu
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Mingzhao Xiao
- Department of Urology, First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China
| | - Xiaoyang Li
- Department of Urology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Peng Rao
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Qishen Yang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zhengyuan Feng
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Quanhao He
- Department of Urology, First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China
| | - Fan Jiang
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ye Xie
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Chengxiao Liao
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiaowei Huang
- CellsVision Medical Technology Services Co., Ltd., Guangzhou, China
| | - Rui Chen
- CellsVision Medical Technology Services Co., Ltd., Guangzhou, China
| | - Tianxin Lin
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Clinical Research Centre for Urological Diseases, Guangzhou, China
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146
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Ruffle JK, Gray RJ, Mohinta S, Pombo G, Kaul C, Hyare H, Rees G, Nachev P. Computational limits to the legibility of the imaged human brain. Neuroimage 2024; 291:120600. [PMID: 38569979 DOI: 10.1016/j.neuroimage.2024.120600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 03/08/2024] [Accepted: 03/31/2024] [Indexed: 04/05/2024] Open
Abstract
Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted an individual's psychology better than the coincidence of common chronic disease (p < 0.05). Serology predicted chronic disease (p < 0.05) and was best predicted by it (p < 0.001), followed by structural neuroimaging (p < 0.05). Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the human brain. We make our models and code openly available.
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Affiliation(s)
- James K Ruffle
- Queen Square Institute of Neurology, University College London, London, United Kingdom.
| | - Robert J Gray
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Samia Mohinta
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Guilherme Pombo
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Chaitanya Kaul
- School of Computing Science, University of Glasgow, Glasgow, United Kingdom
| | - Harpreet Hyare
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Geraint Rees
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Parashkev Nachev
- Queen Square Institute of Neurology, University College London, London, United Kingdom.
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147
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Makhoul M, Melkane AE, Khoury PE, Hadi CE, Matar N. A cross-sectional comparative study: ChatGPT 3.5 versus diverse levels of medical experts in the diagnosis of ENT diseases. Eur Arch Otorhinolaryngol 2024; 281:2717-2721. [PMID: 38365990 DOI: 10.1007/s00405-024-08509-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Accepted: 01/24/2024] [Indexed: 02/18/2024]
Abstract
PURPOSE With recent advances in artificial intelligence (AI), it has become crucial to thoroughly evaluate its applicability in healthcare. This study aimed to assess the accuracy of ChatGPT in diagnosing ear, nose, and throat (ENT) pathology, and comparing its performance to that of medical experts. METHODS We conducted a cross-sectional comparative study where 32 ENT cases were presented to ChatGPT 3.5, ENT physicians, ENT residents, family medicine (FM) specialists, second-year medical students (Med2), and third-year medical students (Med3). Each participant provided three differential diagnoses. The study analyzed diagnostic accuracy rates and inter-rater agreement within and between participant groups and ChatGPT. RESULTS The accuracy rate of ChatGPT was 70.8%, being not significantly different from ENT physicians or ENT residents. However, a significant difference in correctness rate existed between ChatGPT and FM specialists (49.8%, p < 0.001), and between ChatGPT and medical students (Med2 47.5%, p < 0.001; Med3 47%, p < 0.001). Inter-rater agreement for the differential diagnosis between ChatGPT and each participant group was either poor or fair. In 68.75% of cases, ChatGPT failed to mention the most critical diagnosis. CONCLUSIONS ChatGPT demonstrated accuracy comparable to that of ENT physicians and ENT residents in diagnosing ENT pathology, outperforming FM specialists, Med2 and Med3. However, it showed limitations in identifying the most critical diagnosis.
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Affiliation(s)
- Mikhael Makhoul
- Department of Otolaryngology-Head and Neck Surgery, Hotel Dieu de France Hospital, Saint Joseph University, Alfred Naccache Boulevard, Ashrafieh, PO Box: 166830, Beirut, Lebanon.
| | - Antoine E Melkane
- Department of Otolaryngology-Head and Neck Surgery, Hotel Dieu de France Hospital, Saint Joseph University, Alfred Naccache Boulevard, Ashrafieh, PO Box: 166830, Beirut, Lebanon
| | - Patrick El Khoury
- Department of Otolaryngology-Head and Neck Surgery, Hotel Dieu de France Hospital, Saint Joseph University, Alfred Naccache Boulevard, Ashrafieh, PO Box: 166830, Beirut, Lebanon
| | - Christopher El Hadi
- Department of Otolaryngology-Head and Neck Surgery, Hotel Dieu de France Hospital, Saint Joseph University, Alfred Naccache Boulevard, Ashrafieh, PO Box: 166830, Beirut, Lebanon
| | - Nayla Matar
- Department of Otolaryngology-Head and Neck Surgery, Hotel Dieu de France Hospital, Saint Joseph University, Alfred Naccache Boulevard, Ashrafieh, PO Box: 166830, Beirut, Lebanon
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148
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Abu-Ashour W, Emil S, Poenaru D. Using Artificial Intelligence to Label Free-Text Operative and Ultrasound Reports for Grading Pediatric Appendicitis. J Pediatr Surg 2024; 59:783-790. [PMID: 38383177 DOI: 10.1016/j.jpedsurg.2024.01.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 01/22/2024] [Indexed: 02/23/2024]
Abstract
PURPOSE Data science approaches personalizing pediatric appendicitis management are hampered by small datasets and unstructured electronic medical records (EMR). Artificial intelligence (AI) chatbots based on large language models can structure free-text EMR data. We compare data extraction quality between ChatGPT-4 and human data collectors. METHODS To train AI models to grade pediatric appendicitis preoperatively, several data collectors extracted detailed preoperative and operative data from 2100 children operated for acute appendicitis. Collectors were trained for the task based on satisfactory Kappa scores. ChatGPT-4 was prompted to structure free text from 103 random anonymized ultrasound and operative records in the dataset using the set variables and coding options, and to estimate appendicitis severity grade from the operative report. A pediatric surgeon then adjudicated all data, identifying errors in each method. RESULTS Within the 44 ultrasound (42.7%) and 32 operative reports (31.1%) discordant in at least one field, 98% of the errors were found in the manual data extraction. The appendicitis grade was erroneously assigned manually in 29 patients (28.2%), and by ChatGPT-4 in 3 (2.9%). Across datasets, the use of the AI chatbot was able to avoid misclassification in 59.2% of the records including both reports and extracted data approximately 40 times faster. CONCLUSION AI chatbot significantly outperformed manual data extraction in accuracy for ultrasound and operative reports, and correctly assigned the appendicitis grade. While wider validation is required and data safety concerns must be addressed, these AI tools show significant promise in improving the accuracy and efficiency of research data collection. LEVELS OF EVIDENCE Level III.
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Affiliation(s)
- Waseem Abu-Ashour
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada; McGill University Health Centre Research Institute, Montreal, Quebec, Canada.
| | - Sherif Emil
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada; McGill University Health Centre Research Institute, Montreal, Quebec, Canada
| | - Dan Poenaru
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada; McGill University Health Centre Research Institute, Montreal, Quebec, Canada
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Abu Arqub S, Al-Moghrabi D, Allareddy V, Upadhyay M, Vaid N, Yadav S. Content analysis of AI-generated (ChatGPT) responses concerning orthodontic clear aligners. Angle Orthod 2024; 94:263-272. [PMID: 38195060 PMCID: PMC11050467 DOI: 10.2319/071123-484.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 11/01/2023] [Indexed: 01/11/2024] Open
Abstract
OBJECTIVES To assess the accuracy of ChatGPT answers concerning orthodontic clear aligners. MATERIALS AND METHODS A cross-sectional content analysis of ChatGPT generated responses to queries related to clear aligner treatment (CAT) was undertaken. A total of 111 questions were generated by three orthodontists based on a set of predefined domains and subdomains. The artificial intelligence (AI)-generated (ChatGPT) answers were extracted and their accuracy was determined independently by five orthodontists. The accuracy of answers was assessed using a prepiloted four-point scale scoring rubric. Descriptive statistics were performed. RESULTS The total mean accuracy score for the entire set was 2.6 ± 1.1. It was noted that 58% of the AI-generated answers were scored as objectively true, 18% were selected facts, 9% were minimal facts, and 15% were false. False claims included the ability of CAT to reduce the need for orthognathic surgery (4.0 ± 0.0), improve airway function (3.8 ± 0.5), achieve root parallelism (3.6 ± 0.5), alleviate sleep apnea (3.8 ± 0.5), and produce more stable results compared to fixed appliances (3.8 ± 0.5). CONCLUSIONS The overall level of accuracy of ChatGPT responses to questions concerning CAT was suboptimal and lacked citations to relevant literature. Ability of the software to offer current and precise information was limited. Therefore, clinicians and patients must be mindful of false claims and relevant facts omitted in the answers generated by ChatGPT.
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150
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Wu L, Xu J, Thakkar S, Gray M, Qu Y, Li D, Tong W. A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document. Regul Toxicol Pharmacol 2024; 149:105613. [PMID: 38570021 DOI: 10.1016/j.yrtph.2024.105613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 03/18/2024] [Accepted: 03/26/2024] [Indexed: 04/05/2024]
Abstract
Regulatory agencies consistently deal with extensive document reviews, ranging from product submissions to both internal and external communications. Large Language Models (LLMs) like ChatGPT can be invaluable tools for these tasks, however present several challenges, particularly the proprietary information, combining customized function with specific review needs, and transparency and explainability of the model's output. Hence, a localized and customized solution is imperative. To tackle these challenges, we formulated a framework named askFDALabel on FDA drug labeling documents that is a crucial resource in the FDA drug review process. AskFDALabel operates within a secure IT environment and comprises two key modules: a semantic search and a Q&A/text-generation module. The Module S built on word embeddings to enable comprehensive semantic queries within labeling documents. The Module T utilizes a tuned LLM to generate responses based on references from Module S. As the result, our framework enabled small LLMs to perform comparably to ChatGPT with as a computationally inexpensive solution for regulatory application. To conclude, through AskFDALabel, we have showcased a pathway that harnesses LLMs to support agency operations within a secure environment, offering tailored functions for the needs of regulatory research.
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Affiliation(s)
- Leihong Wu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US FDA, 3900 NCTR Rd, Jefferson AR, 72211, USA.
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US FDA, 3900 NCTR Rd, Jefferson AR, 72211, USA
| | - Shraddha Thakkar
- Office of Translational Sciences, Center for Drug Evaluation and Research (CDER), US FDA, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA
| | - Magnus Gray
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US FDA, 3900 NCTR Rd, Jefferson AR, 72211, USA
| | - Yanyan Qu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US FDA, 3900 NCTR Rd, Jefferson AR, 72211, USA
| | - Dongying Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US FDA, 3900 NCTR Rd, Jefferson AR, 72211, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US FDA, 3900 NCTR Rd, Jefferson AR, 72211, USA.
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