1
|
Aden D, Zaheer S, Khan S. Possible benefits, challenges, pitfalls, and future perspective of using ChatGPT in pathology. REVISTA ESPANOLA DE PATOLOGIA : PUBLICACION OFICIAL DE LA SOCIEDAD ESPANOLA DE ANATOMIA PATOLOGICA Y DE LA SOCIEDAD ESPANOLA DE CITOLOGIA 2024; 57:198-210. [PMID: 38971620 DOI: 10.1016/j.patol.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/22/2024] [Accepted: 04/16/2024] [Indexed: 07/08/2024]
Abstract
The much-hyped artificial intelligence (AI) model called ChatGPT developed by Open AI can have great benefits for physicians, especially pathologists, by saving time so that they can use their time for more significant work. Generative AI is a special class of AI model, which uses patterns and structures learned from existing data and can create new data. Utilizing ChatGPT in Pathology offers a multitude of benefits, encompassing the summarization of patient records and its promising prospects in Digital Pathology, as well as its valuable contributions to education and research in this field. However, certain roadblocks need to be dealt like integrating ChatGPT with image analysis which will act as a revolution in the field of pathology by increasing diagnostic accuracy and precision. The challenges with the use of ChatGPT encompass biases from its training data, the need for ample input data, potential risks related to bias and transparency, and the potential adverse outcomes arising from inaccurate content generation. Generation of meaningful insights from the textual information which will be efficient in processing different types of image data, such as medical images, and pathology slides. Due consideration should be given to ethical and legal issues including bias.
Collapse
Affiliation(s)
- Durre Aden
- Department of Pathology, Hamdard Institute of Medical Sciences and Research, Jamia Hamdard, New Delhi, India
| | - Sufian Zaheer
- Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.
| | - Sabina Khan
- Department of Pathology, Hamdard Institute of Medical Sciences and Research, Jamia Hamdard, New Delhi, India
| |
Collapse
|
2
|
Palacio C, Hovorka M, Acosta M, Bautista R, Chen C, Hovorka J. Predicting factors for extremity fracture among border-fall patients using machine learning computing. Heliyon 2024; 10:e32185. [PMID: 38961975 PMCID: PMC11219316 DOI: 10.1016/j.heliyon.2024.e32185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 05/29/2024] [Indexed: 07/05/2024] Open
Abstract
Background The factors causing the injuries sustained from falls at US-Mexican border include falls from border wall or fence, fleeing from border patrols, ejecting from vehicle, and others. This study aimed to determine the factors leading to anatomical injuries and to identify the importance of factors leading to limb fracture and internal organ injuries. Methods A total of 178 patients who sustained musculoskeletal injuries or internal organ injuries and were admitted to our hospital were included in this retrospective study. Factors indexed for analysis included demographics, comorbidities, and falling mechanic factors. Correlations between anatomical injuries and mechanical injuries were analyzed. Multilayer perceptron neural network (MPNN) was used to identify predictive factors and to stratify the importance of these factors leading to injuries. The SPSS software was used for statistical analysis and predictive factor analysis. Results The extremity fracture was associated with border wall/fence fall (p = 0.001) and fleeing (p = 0.002). The spine fracture was correlated with bridge jump/fall (p = 0.007), fence jump/fall (p = 0.026). The vehicle ejecting/MVA was correlated with head injury (P < 0.001), chest injury (P < 0.001), and abdominal injury p < 0.001). MNPP stratify the importance of factor causing injury with multiple factor considered. Conclusion The various injury factors caused different anatomical injuries. Multifactorial assessment associated with these injuries can improve the accuracy of diagnosis and develop a predictive model for clinical applications.
Collapse
Affiliation(s)
- Carlos Palacio
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
| | - Maximillian Hovorka
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
| | - Marie Acosta
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
| | - Ruby Bautista
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
| | - Chaoyang Chen
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
- Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, 48201, USA
| | - John Hovorka
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
| |
Collapse
|
3
|
Hou H, Zhang R, Li J. Artificial intelligence in the clinical laboratory. Clin Chim Acta 2024; 559:119724. [PMID: 38734225 DOI: 10.1016/j.cca.2024.119724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 05/07/2024] [Accepted: 05/08/2024] [Indexed: 05/13/2024]
Abstract
Laboratory medicine has become a highly automated medical discipline. Nowadays, artificial intelligence (AI) applied to laboratory medicine is also gaining more and more attention, which can optimize the entire laboratory workflow and even revolutionize laboratory medicine in the future. However, only a few commercially available AI models are currently approved for use in clinical laboratories and have drawbacks such as high cost, lack of accuracy, and the need for manual review of model results. Furthermore, there are a limited number of literature reviews that comprehensively address the research status, challenges, and future opportunities of AI applications in laboratory medicine. Our article begins with a brief introduction to AI and some of its subsets, then reviews some AI models that are currently being used in clinical laboratories or that have been described in emerging studies, and explains the existing challenges associated with their application and possible solutions, finally provides insights into the future opportunities of the field. We highlight the current status of implementation and potential applications of AI models in different stages of the clinical testing process.
Collapse
Affiliation(s)
- Hanjing Hou
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.
| |
Collapse
|
4
|
Huang Y, Zhu W, Zhou J, Huang Q, Zeng G. Navigating the Evolving Landscape of Primary Hyperoxaluria: Traditional Management Defied by the Rise of Novel Molecular Drugs. Biomolecules 2024; 14:511. [PMID: 38785918 PMCID: PMC11117870 DOI: 10.3390/biom14050511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/10/2024] [Accepted: 04/12/2024] [Indexed: 05/25/2024] Open
Abstract
Primary hyperoxalurias (PHs) are inherited metabolic disorders marked by enzymatic cascade disruption, leading to excessive oxalate production that is subsequently excreted in the urine. Calcium oxalate deposition in the renal tubules and interstitium triggers renal injury, precipitating systemic oxalate build-up and subsequent secondary organ impairment. Recent explorations of novel therapeutic strategies have challenged and necessitated the reassessment of established management frameworks. The execution of diverse clinical trials across various medication classes has provided new insights and knowledge. With the evolution of PH treatments reaching a new milestone, prompt and accurate diagnosis is increasingly critical. Developing early, effective management and treatment plans is essential to improve the long-term quality of life for PH patients.
Collapse
Affiliation(s)
- Yueqi Huang
- The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China; (Y.H.); (J.Z.)
| | - Wei Zhu
- Department of Urology and Guangdong Key Laboratory of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510230, China;
| | - Jia Zhou
- The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China; (Y.H.); (J.Z.)
| | - Qiulin Huang
- The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China; (Y.H.); (J.Z.)
| | - Guohua Zeng
- The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China; (Y.H.); (J.Z.)
- Department of Urology and Guangdong Key Laboratory of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510230, China;
| |
Collapse
|
5
|
Malik S, Zaheer S. ChatGPT as an aid for pathological diagnosis of cancer. Pathol Res Pract 2024; 253:154989. [PMID: 38056135 DOI: 10.1016/j.prp.2023.154989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/08/2023]
Abstract
Diagnostic workup of cancer patients is highly reliant on the science of pathology using cytopathology, histopathology, and other ancillary techniques like immunohistochemistry and molecular cytogenetics. Data processing and learning by means of artificial intelligence (AI) has become a spearhead for the advancement of medicine, with pathology and laboratory medicine being no exceptions. ChatGPT, an artificial intelligence (AI)-based chatbot, that was recently launched by OpenAI, is currently a talk of the town, and its role in cancer diagnosis is also being explored meticulously. Pathology workflow by integration of digital slides, implementation of advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enables effective integration, assimilation, and utilization of knowledge that is beyond human limits and boundaries. Despite of it's numerous advantages in the pathological diagnosis of cancer, it comes with several challenges like integration of digital slides with input language parameters, problems of bias, and legal issues which have to be addressed and worked up soon so that we as a pathologists diagnosing malignancies are on the same band wagon and don't miss the train.
Collapse
Affiliation(s)
- Shaivy Malik
- Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
| | - Sufian Zaheer
- Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.
| |
Collapse
|
6
|
Alanzi T, Alanazi F, Mashhour B, Altalhi R, Alghamdi A, Al Shubbar M, Alamro S, Alshammari M, Almusmili L, Alanazi L, Alzahrani S, Alalouni R, Alanzi N, Alsharifa A. Surveying Hematologists' Perceptions and Readiness to Embrace Artificial Intelligence in Diagnosis and Treatment Decision-Making. Cureus 2023; 15:e49462. [PMID: 38152821 PMCID: PMC10751460 DOI: 10.7759/cureus.49462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/23/2023] [Indexed: 12/29/2023] Open
Abstract
AIM This study aims to explore the critical dimension of assessing the perceptions and readiness of hematologists to embrace artificial intelligence (AI) technologies in their diagnostic and treatment decision-making processes. METHODS This study used a cross-sectional design for collecting data related to the perceptions and readiness of hematologists using a validated online questionnaire-based survey. Both hematologists (MD) and postgraduate MD students in hematology were included in the study. A total of 188 participants, including 35 hematologists (MD) and 153 MD hematology students, completed the survey. RESULTS Major challenges include "AI's level of autonomy" and "the complexity in the field of medicine." Major barriers and risks identified include "lack of trust," "management's level of understanding," "dehumanization of healthcare," and "reduction in physicians' skills." Statistically significant differences in perceptions of benefits including resources (p=0.0326, p<0.05) and knowledge (p=0.0262, p<0.05) were observed between genders. Older physicians were observed to be more concerned about the use of AI compared to younger physicians (p<0.05). CONCLUSION While AI use in hematology diagnosis and treatment decision-making is positively perceived, issues such as lack of trust, transparency, regulations, and poor AI awareness can affect the adoption of AI.
Collapse
Affiliation(s)
- Turki Alanzi
- Department of Health Information Management and Technology, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | - Fehaid Alanazi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakakah, SAU
| | | | | | | | | | - Saud Alamro
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | | | | | - Lena Alanazi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakakah, SAU
| | | | - Raneem Alalouni
- College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | - Nouf Alanzi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakakah, SAU
| | | |
Collapse
|
7
|
Katsenou A, O’Farrell R, Dowling P, Heckman CA, O’Gorman P, Bazou D. Using Proteomics Data to Identify Personalized Treatments in Multiple Myeloma: A Machine Learning Approach. Int J Mol Sci 2023; 24:15570. [PMID: 37958554 PMCID: PMC10650823 DOI: 10.3390/ijms242115570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/11/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023] Open
Abstract
This paper describes a machine learning (ML) decision support system to provide a list of chemotherapeutics that individual multiple myeloma (MM) patients are sensitive/resistant to, based on their proteomic profile. The methodology used in this study involved understanding the parameter space and selecting the dominant features (proteomics data), identifying patterns of proteomic profiles and their association to the recommended treatments, and defining the decision support system of personalized treatment as a classification problem. During the data analysis, we compared several ML algorithms, such as linear regression, Random Forest, and support vector machines, to classify patients as sensitive/resistant to therapeutics. A further analysis examined data-balancing techniques that emerged due to the small cohort size. The results suggest that utilizing proteomics data is a promising approach for identifying effective treatment options for patients with MM (reaching on average an accuracy of 81%). Although this pilot study was limited by the small patient cohort (39 patients), which restricted the training and validation of the explored ML solutions to identify complex associations between proteins, it holds great promise for developing personalized anti-MM treatments using ML approaches.
Collapse
Affiliation(s)
- Angeliki Katsenou
- Department of Electronics and Electrical Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland;
- School of Computer Science, University of Bristol, Bristol BS1 8UB, UK
| | - Roisin O’Farrell
- Department of Electronics and Electrical Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland;
| | - Paul Dowling
- Department of Biology, Maynooth University, W23 F2K8 Kildare, Ireland;
| | - Caroline A. Heckman
- Institute for Molecular Medicine Finland-FIMM, HiLIFE-Helsinki Institute of Life Science, iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, 00290 Helsinki, Finland;
| | - Peter O’Gorman
- Department of Haematology, Mater Misericordiae University Hospital, D07 R2WY Dublin, Ireland;
| | - Despina Bazou
- School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
| |
Collapse
|
8
|
Iqbal J, Cortés Jaimes DC, Makineni P, Subramani S, Hemaida S, Thugu TR, Butt AN, Sikto JT, Kaur P, Lak MA, Augustine M, Shahzad R, Arain M. Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine. Cureus 2023; 15:e44658. [PMID: 37799217 PMCID: PMC10549955 DOI: 10.7759/cureus.44658] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.
Collapse
Affiliation(s)
| | - Diana Carolina Cortés Jaimes
- Epidemiology, Universidad Autónoma de Bucaramanga, Bucaramanga, COL
- Medicine, Pontificia Universidad Javeriana, Bogotá, COL
| | - Pallavi Makineni
- Medicine, All India Institute of Medical Sciences, Bhubaneswar, Bhubaneswar, IND
| | - Sachin Subramani
- Medicine and Surgery, Employees' State Insurance Corporation (ESIC) Medical College, Gulbarga, IND
| | - Sarah Hemaida
- Internal Medicine, Istanbul Okan University, Istanbul, TUR
| | - Thanmai Reddy Thugu
- Internal Medicine, Sri Padmavathi Medical College for Women, Sri Venkateswara Institute of Medical Sciences (SVIMS), Tirupati, IND
| | - Amna Naveed Butt
- Medicine/Internal Medicine, Allama Iqbal Medical College, Lahore, PAK
| | | | - Pareena Kaur
- Medicine, Punjab Institute of Medical Sciences, Jalandhar, IND
| | | | | | - Roheen Shahzad
- Medicine, Combined Military Hospital (CMH) Lahore Medical College and Institute of Dentistry, Lahore, PAK
| | - Mustafa Arain
- Internal Medicine, Civil Hospital Karachi, Karachi, PAK
| |
Collapse
|
9
|
Giarnieri E, Scardapane S. Towards Artificial Intelligence Applications in Next Generation Cytopathology. Biomedicines 2023; 11:2225. [PMID: 37626721 PMCID: PMC10452064 DOI: 10.3390/biomedicines11082225] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/04/2023] [Accepted: 08/05/2023] [Indexed: 08/27/2023] Open
Abstract
Over the last 20 years we have seen an increase in techniques in the field of computational pathology and machine learning, improving our ability to analyze and interpret imaging. Neural networks, in particular, have been used for more than thirty years, starting with the computer assisted smear test using early generation models. Today, advanced machine learning, working on large image data sets, has been shown to perform classification, detection, and segmentation with remarkable accuracy and generalization in several domains. Deep learning algorithms, as a branch of machine learning, are thus attracting attention in digital pathology and cytopathology, providing feasible solutions for accurate and efficient cytological diagnoses, ranging from efficient cell counts to automatic classification of anomalous cells and queries over large clinical databases. The integration of machine learning with related next-generation technologies powered by AI, such as augmented/virtual reality, metaverse, and computational linguistic models are a focus of interest in health care digitalization, to support education, diagnosis, and therapy. In this work we will consider how all these innovations can help cytopathology to go beyond the microscope and to undergo a hyper-digitalized transformation. We also discuss specific challenges to their applications in the field, notably, the requirement for large-scale cytopathology datasets, the necessity of new protocols for sharing information, and the need for further technological training for pathologists.
Collapse
Affiliation(s)
- Enrico Giarnieri
- Cytopathology Unit, Department of Clinical and Molecular Medicine, Sant’Andrea Hospital, Sapienza University of Rome, Piazzale Aldo Moro 5, 00189 Rome, Italy
| | - Simone Scardapane
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Via Eudossiana 18, 00196 Rome, Italy;
| |
Collapse
|