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Yang J, Walker KC, Bekar-Cesaretli AA, Hao B, Bhadelia N, Joseph-McCarthy D, Paschalidis IC. Automating biomedical literature review for rapid drug discovery: Leveraging GPT-4 to expedite pandemic response. Int J Med Inform 2024; 189:105500. [PMID: 38815316 DOI: 10.1016/j.ijmedinf.2024.105500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/13/2024] [Accepted: 05/21/2024] [Indexed: 06/01/2024]
Abstract
OBJECTIVE The rapid expansion of the biomedical literature challenges traditional review methods, especially during outbreaks of emerging infectious diseases when quick action is critical. Our study aims to explore the potential of ChatGPT to automate the biomedical literature review for rapid drug discovery. MATERIALS AND METHODS We introduce a novel automated pipeline helping to identify drugs for a given virus in response to a potential future global health threat. Our approach can be used to select PubMed articles identifying a drug target for the given virus. We tested our approach on two known pathogens: SARS-CoV-2, where the literature is vast, and Nipah, where the literature is sparse. Specifically, a panel of three experts reviewed a set of PubMed articles and labeled them as either describing a drug target for the given virus or not. The same task was given to the automated pipeline and its performance was based on whether it labeled the articles similarly to the human experts. We applied a number of prompt engineering techniques to improve the performance of ChatGPT. RESULTS Our best configuration used GPT-4 by OpenAI and achieved an out-of-sample validation performance with accuracy/F1-score/sensitivity/specificity of 92.87%/88.43%/83.38%/97.82% for SARS-CoV-2 and 87.40%/73.90%/74.72%/91.36% for Nipah. CONCLUSION These results highlight the utility of ChatGPT in drug discovery and development and reveal their potential to enable rapid drug target identification during a pandemic-level health emergency.
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Affiliation(s)
- Jingmei Yang
- Department of Electrical & Computer Engineering and Division of Systems Engineering, Boston University, Boston, MA, United States of America
| | - Kenji C Walker
- Department of Biomedical Engineering, Boston University, Boston, MA, United States of America
| | | | - Boran Hao
- Department of Electrical & Computer Engineering and Division of Systems Engineering, Boston University, Boston, MA, United States of America
| | - Nahid Bhadelia
- Chobanian & Avedisian School of Medicine and Center for Emerging Infectious Diseases Policy and Research, Boston University, Boston, MA, United States of America
| | - Diane Joseph-McCarthy
- Department of Biomedical Engineering, Boston University, Boston, MA, United States of America
| | - Ioannis Ch Paschalidis
- Department of Electrical & Computer Engineering and Division of Systems Engineering, Boston University, Boston, MA, United States of America; Department of Biomedical Engineering, Boston University, Boston, MA, United States of America; Faculty of Computing & Data Sciences, Boston University, Boston, MA, United States of America.
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2
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Chang J, Lee KJ, Wang TH, Chen CM. Utilizing ChatGPT for Curriculum Learning in Developing a Clinical Grade Pneumothorax Detection Model: A Multisite Validation Study. J Clin Med 2024; 13:4042. [PMID: 39064082 PMCID: PMC11277936 DOI: 10.3390/jcm13144042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/05/2024] [Accepted: 07/07/2024] [Indexed: 07/28/2024] Open
Abstract
Background: Pneumothorax detection is often challenging, particularly when radiographic features are subtle. This study introduces a deep learning model that integrates curriculum learning and ChatGPT to enhance the detection of pneumothorax in chest X-rays. Methods: The model training began with large, easily detectable pneumothoraces, gradually incorporating smaller, more complex cases to prevent performance plateauing. The training dataset comprised 6445 anonymized radiographs, validated across multiple sites, and further tested for generalizability in diverse clinical subgroups. Performance metrics were analyzed using descriptive statistics. Results: The model achieved a sensitivity of 0.97 and a specificity of 0.97, with an area under the curve (AUC) of 0.98, demonstrating a performance comparable to that of many FDA-approved devices. Conclusions: This study suggests that a structured approach to training deep learning models, through curriculum learning and enhanced data extraction via natural language processing, can facilitate and improve the training of AI models for pneumothorax detection.
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Affiliation(s)
- Joseph Chang
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei 100, Taiwan
- EverFortune.AI Co., Ltd., Taichung 403, Taiwan
| | | | - Ti-Hao Wang
- EverFortune.AI Co., Ltd., Taichung 403, Taiwan
- Department of Medicine, China Medical University, Taichung 404, Taiwan
- Department of Radiation Oncology, China Medical University Hospital, Taichung 404, Taiwan
| | - Chung-Ming Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei 100, Taiwan
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3
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Campion TR, Craven CK, Dorr DA, Bernstam EV, Knosp BM. Understanding enterprise data warehouses to support clinical and translational research: impact, sustainability, demand management, and accessibility. J Am Med Inform Assoc 2024; 31:1522-1528. [PMID: 38777803 PMCID: PMC11187432 DOI: 10.1093/jamia/ocae111] [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: 02/16/2024] [Revised: 04/10/2024] [Accepted: 05/05/2024] [Indexed: 05/25/2024] Open
Abstract
OBJECTIVES Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility. MATERIALS AND METHODS We engaged a convenience sample of informatics leaders from CTSA hubs (n = 21) for semi-structured interviews and completed a directed content analysis of interview transcripts. RESULTS EDW4R have created institutional capacity for single- and multi-center studies, democratized access to EHR data for investigators from multiple disciplines, and enabled the learning health system. Bibliometrics have been challenging due to investigator non-compliance, but one hub's requirement to link all study protocols with funding records enabled quantifying an EDW4R's multi-million dollar impact. Sustainability of EDW4R has relied on multiple funding sources with a general shift away from the CTSA grant toward institutional and industry support. To address EDW4R demand, institutions have expanded staff, used different governance approaches, and provided investigator self-service tools. EDW4R accessibility can benefit from improved tools incorporating user-centered design, increased data literacy among scientists, expansion of informaticians in the workforce, and growth of team science. DISCUSSION As investigator demand for EDW4R has increased, approaches to tracking impact, ensuring sustainability, and improving accessibility of EDW4R resources have varied. CONCLUSION This study adds to understanding of how informatics leaders seek to support investigators using EDW4R across the CTSA consortium and potentially elsewhere.
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Affiliation(s)
- Thomas R Campion
- Clinical & Translational Science Center, Weill Cornell Medicine, New York, NY 10022, United States
| | - Catherine K Craven
- Division of Clinical Research Informatics, Department of Population Health Sciences, The University of Texas Health San Antonio, San Antonio, TX 78229, United States
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, United States
- Department of Medicine, Oregon Health & Science University, Portland, OR 97239, United States
| | - Elmer V Bernstam
- D. Bradley McWilliams School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX 77030, United States
- Division of General Internal Medicine, McGovern Medical School and Center for Clinical and Translational Sciences, The University of Texas Health Science Center, Houston, TX 77030, United States
| | - Boyd M Knosp
- Roy J. and Lucille A. Carver College of Medicine and the Institute for Clinical & Translational Science, University of Iowa, Iowa City, IA 52242, United States
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4
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Cozzi A, Pinker K, Hidber A, Zhang T, Bonomo L, Lo Gullo R, Christianson B, Curti M, Rizzo S, Del Grande F, Mann RM, Schiaffino S, Panzer A. BI-RADS Category Assignments by GPT-3.5, GPT-4, and Google Bard: A Multilanguage Study. Radiology 2024; 311:e232133. [PMID: 38687216 PMCID: PMC11070611 DOI: 10.1148/radiol.232133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 05/02/2024]
Abstract
Background The performance of publicly available large language models (LLMs) remains unclear for complex clinical tasks. Purpose To evaluate the agreement between human readers and LLMs for Breast Imaging Reporting and Data System (BI-RADS) categories assigned based on breast imaging reports written in three languages and to assess the impact of discordant category assignments on clinical management. Materials and Methods This retrospective study included reports for women who underwent MRI, mammography, and/or US for breast cancer screening or diagnostic purposes at three referral centers. Reports with findings categorized as BI-RADS 1-5 and written in Italian, English, or Dutch were collected between January 2000 and October 2023. Board-certified breast radiologists and the LLMs GPT-3.5 and GPT-4 (OpenAI) and Bard, now called Gemini (Google), assigned BI-RADS categories using only the findings described by the original radiologists. Agreement between human readers and LLMs for BI-RADS categories was assessed using the Gwet agreement coefficient (AC1 value). Frequencies were calculated for changes in BI-RADS category assignments that would affect clinical management (ie, BI-RADS 0 vs BI-RADS 1 or 2 vs BI-RADS 3 vs BI-RADS 4 or 5) and compared using the McNemar test. Results Across 2400 reports, agreement between the original and reviewing radiologists was almost perfect (AC1 = 0.91), while agreement between the original radiologists and GPT-4, GPT-3.5, and Bard was moderate (AC1 = 0.52, 0.48, and 0.42, respectively). Across human readers and LLMs, differences were observed in the frequency of BI-RADS category upgrades or downgrades that would result in changed clinical management (118 of 2400 [4.9%] for human readers, 611 of 2400 [25.5%] for Bard, 573 of 2400 [23.9%] for GPT-3.5, and 435 of 2400 [18.1%] for GPT-4; P < .001) and that would negatively impact clinical management (37 of 2400 [1.5%] for human readers, 435 of 2400 [18.1%] for Bard, 344 of 2400 [14.3%] for GPT-3.5, and 255 of 2400 [10.6%] for GPT-4; P < .001). Conclusion LLMs achieved moderate agreement with human reader-assigned BI-RADS categories across reports written in three languages but also yielded a high percentage of discordant BI-RADS categories that would negatively impact clinical management. © RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
| | | | - Andri Hidber
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Tianyu Zhang
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Luca Bonomo
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Roberto Lo Gullo
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Blake Christianson
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Marco Curti
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Stefania Rizzo
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Filippo Del Grande
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | | | | | - Ariane Panzer
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
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5
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Guevara M, Chen S, Thomas S, Chaunzwa TL, Franco I, Kann BH, Moningi S, Qian JM, Goldstein M, Harper S, Aerts HJWL, Catalano PJ, Savova GK, Mak RH, Bitterman DS. Large language models to identify social determinants of health in electronic health records. NPJ Digit Med 2024; 7:6. [PMID: 38200151 PMCID: PMC10781957 DOI: 10.1038/s41746-023-00970-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 11/15/2023] [Indexed: 01/12/2024] Open
Abstract
Social determinants of health (SDoH) play a critical role in patient outcomes, yet their documentation is often missing or incomplete in the structured data of electronic health records (EHRs). Large language models (LLMs) could enable high-throughput extraction of SDoH from the EHR to support research and clinical care. However, class imbalance and data limitations present challenges for this sparsely documented yet critical information. Here, we investigated the optimal methods for using LLMs to extract six SDoH categories from narrative text in the EHR: employment, housing, transportation, parental status, relationship, and social support. The best-performing models were fine-tuned Flan-T5 XL for any SDoH mentions (macro-F1 0.71), and Flan-T5 XXL for adverse SDoH mentions (macro-F1 0.70). Adding LLM-generated synthetic data to training varied across models and architecture, but improved the performance of smaller Flan-T5 models (delta F1 + 0.12 to +0.23). Our best-fine-tuned models outperformed zero- and few-shot performance of ChatGPT-family models in the zero- and few-shot setting, except GPT4 with 10-shot prompting for adverse SDoH. Fine-tuned models were less likely than ChatGPT to change their prediction when race/ethnicity and gender descriptors were added to the text, suggesting less algorithmic bias (p < 0.05). Our models identified 93.8% of patients with adverse SDoH, while ICD-10 codes captured 2.0%. These results demonstrate the potential of LLMs in improving real-world evidence on SDoH and assisting in identifying patients who could benefit from resource support.
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Affiliation(s)
- Marco Guevara
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA, USA
| | - Shan Chen
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA, USA
| | - Spencer Thomas
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA, USA
- Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tafadzwa L Chaunzwa
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA, USA
| | - Idalid Franco
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA, USA
| | - Benjamin H Kann
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA, USA
| | - Shalini Moningi
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jack M Qian
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Susan Harper
- Adult Resource Office, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA, USA
- Radiology and Nuclear Medicine, GROW & CARIM, Maastricht University, Maastricht, The Netherlands
| | - Paul J Catalano
- Department of Data Science, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Guergana K Savova
- Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Raymond H Mak
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA, USA
| | - Danielle S Bitterman
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA, USA.
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6
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Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol 2023; 16:114. [PMID: 38012673 PMCID: PMC10680201 DOI: 10.1186/s13045-023-01514-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.
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Affiliation(s)
- Chaoyi Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jianhui Yang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
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