Pub Date : 2024-08-01DOI: 10.1016/S2589-7500(24)00145-6
{"title":"Remote illness detection faces a trust barrier","authors":"","doi":"10.1016/S2589-7500(24)00145-6","DOIUrl":"10.1016/S2589-7500(24)00145-6","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001456/pdfft?md5=9f1b7ea2d874e81511d0a2671601c363&pid=1-s2.0-S2589750024001456-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141767683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/S2589-7500(24)00115-8
{"title":"The next generation of evidence synthesis for diagnostic accuracy studies in artificial intelligence","authors":"","doi":"10.1016/S2589-7500(24)00115-8","DOIUrl":"10.1016/S2589-7500(24)00115-8","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001158/pdfft?md5=63d2ae9f56a06a00c10c7d09f23c13cb&pid=1-s2.0-S2589750024001158-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141460002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/S2589-7500(24)00113-4
Background
Chest x-ray is a basic, cost-effective, and widely available imaging method that is used for static assessments of organic diseases and anatomical abnormalities, but its ability to estimate dynamic measurements such as pulmonary function is unknown. We aimed to estimate two major pulmonary functions from chest x-rays.
Methods
In this retrospective model development and validation study, we trained, validated, and externally tested a deep learning-based artificial intelligence (AI) model to estimate forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1) from chest x-rays. We included consecutively collected results of spirometry and any associated chest x-rays that had been obtained between July 1, 2003, and Dec 31, 2021, from five institutions in Japan (labelled institutions A–E). Eligible x-rays had been acquired within 14 days of spirometry and were labelled with the FVC and FEV1. X-rays from three institutions (A–C) were used for training, validation, and internal testing, with the testing dataset being independent of the training and validation datasets, and then x-rays from the two other institutions (D and E) were used for independent external testing. Performance for estimating FVC and FEV1 was evaluated by calculating the Pearson's correlation coefficient (r), intraclass correlation coefficient (ICC), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) compared with the results of spirometry.
Findings
We included 141 734 x-ray and spirometry pairs from 81 902 patients from the five institutions. The training, validation, and internal test datasets included 134 307 x-rays from 75 768 patients (37 718 [50%] female, 38 050 [50%] male; mean age 56 years [SD 18]), and the external test datasets included 2137 x-rays from 1861 patients (742 [40%] female, 1119 [60%] male; mean age 65 years [SD 17]) from institution D and 5290 x-rays from 4273 patients (1972 [46%] female, 2301 [54%] male; mean age 63 years [SD 17]) from institution E. External testing for FVC yielded r values of 0·91 (99% CI 0·90–0·92) for institution D and 0·90 (0·89–0·91) for institution E, ICC of 0·91 (99% CI 0·90–0·92) and 0·89 (0·88–0·90), MSE of 0·17 L2 (99% CI 0·15–0·19) and 0·17 L2 (0·16–0·19), RMSE of 0·41 L (99% CI 0·39–0·43) and 0·41 L (0·39–0·43), and MAE of 0·31 L (99% CI 0·29–0·32) and 0·31 L (0·30–0·32). External testing for FEV1 yielded r values of 0·91 (99% CI 0·90–0·92) for institution D and 0·91 (0·90–0·91) for institution E, ICC of 0·90 (99% CI 0·89–0·91) and 0·90 (0·90–0·91), MSE of 0·13 L2 (99% CI 0·12–0·15) and 0·11 L2 (0·10–0·12), RMSE of 0·37 L (99% CI 0·35–0·38) and 0·33 L (0·32–0·35), and MAE of 0·28 L (99% CI 0·27–0·29) and 0·25 L (0·25–0·26).
Interpretation
This deep learning model allowed
背景：胸部 X 光片是一种基本、经济、广泛使用的成像方法，可用于器质性疾病和解剖异常的静态评估，但其估算肺功能等动态测量值的能力尚不清楚。我们的目的是通过胸部 X 光片估测两种主要的肺功能：在这项回顾性模型开发和验证研究中，我们对基于深度学习的人工智能（AI）模型进行了训练、验证和外部测试，以便从胸部 X 光片中估算出用力肺活量（FVC）和 1 秒用力呼气容积（FEV1）。我们纳入了从 2003 年 7 月 1 日到 2021 年 12 月 31 日期间从日本五家机构（标注为机构 A-E）连续收集的肺活量测定结果和任何相关的胸部 X 光片。符合条件的 X 光片是在肺活量测定后 14 天内获得的，并标有 FVC 和 FEV1。来自三个机构（A-C）的 X 光片被用于训练、验证和内部测试，测试数据集独立于训练和验证数据集，然后来自其他两个机构（D 和 E）的 X 光片被用于独立的外部测试。通过计算与肺活量测定结果相比的皮尔逊相关系数（r）、类内相关系数（ICC）、均方误差（MSE）、均方根误差（RMSE）和平均绝对误差（MAE）来评估 FVC 和 FEV1 的估算结果：我们纳入了五家机构 81 902 名患者的 141 734 对 X 光片和肺活量测定结果。训练、验证和内部测试数据集包括 75 768 名患者的 134 307 张 X 光片（女性 37 718 [50%]，男性 38 050 [50%]；平均年龄 56 岁 [SD 18]），外部测试数据集包括 1861 名患者的 2137 张 X 光片（女性 742 [40%]，男性 1119 [60%]；平均年龄 65 岁 [SD 17]）；外部检测数据集包括 D 机构 1861 名患者（女性 742 人 [40%]，男性 1119 人 [60%]；平均年龄 65 岁 [SD 17]）的 2137 张 X 光片和 E 机构 4273 名患者（女性 1972 人 [46%]，男性 2301 人 [54%]；平均年龄 63 岁 [SD 17]）的 5290 张 X 光片。对 FVC 的外部测试结果显示，D 机构的 r 值为 0-91（99% CI 0-90-0-92），E 机构为 0-90（0-89-0-91），ICC 为 0-91（99% CI 0-90-0-92）和 0-89（0-88-0-90）、MSE为 0-17 L2 (99% CI 0-15-0-19) 和 0-17 L2 (0-16-0-19)，RMSE为 0-41 L (99% CI 0-39-0-43) 和 0-41 L (0-39-0-43)，MAE为 0-31 L (99% CI 0-29-0-32) 和 0-31 L (0-30-0-32)。对 FEV1 的外部测试结果显示，D 机构的 r 值为 0-91（99% CI 0-90-0-92），E 机构为 0-91（0-90-0-91），ICC 为 0-90（99% CI 0-89-0-91）和 0-90（0-90-0-91）、MSE 为 0-13 L2 (99% CI 0-12-0-15) 和 0-11 L2 (0-10-0-12)，RMSE 为 0-37 L (99% CI 0-35-0-38) 和 0-33 L (0-32-0-35)，MAE 为 0-28 L (99% CI 0-27-0-29) 和 0-25 L (0-25-0-26)。解释：该深度学习模型可通过胸部 X 光片估算出 FVC 和 FEV1，与肺活量测量法显示出很高的一致性。该模型为肺活量测定提供了一种评估肺功能的替代方法，尤其适用于无法进行肺活量测定的患者，并可根据从胸部X光片中获得的信息加强CT成像方案的定制，从而改善肺部疾病的诊断和管理。未来的研究应调查该人工智能模型与临床信息相结合的性能，以便更恰当、更有针对性地使用：无。
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Pub Date : 2024-08-01DOI: 10.1016/S2589-7500(24)00095-5
The development and commercialisation of medical decision systems based on artificial intelligence (AI) far outpaces our understanding of their value for clinicians. Although applicable across many forms of medicine, we focus on characterising the diagnostic decisions of radiologists through the concept of ecologically bounded reasoning, review the differences between clinician decision making and medical AI model decision making, and reveal how these differences pose fundamental challenges for integrating AI into radiology. We argue that clinicians are contextually motivated, mentally resourceful decision makers, whereas AI models are contextually stripped, correlational decision makers, and discuss misconceptions about clinician–AI interaction stemming from this misalignment of capabilities. We outline how future research on clinician–AI interaction could better address the cognitive considerations of decision making and be used to enhance the safety and usability of AI models in high-risk medical decision-making contexts.
{"title":"Medical artificial intelligence for clinicians: the lost cognitive perspective","authors":"","doi":"10.1016/S2589-7500(24)00095-5","DOIUrl":"10.1016/S2589-7500(24)00095-5","url":null,"abstract":"<div><p>The development and commercialisation of medical decision systems based on artificial intelligence (AI) far outpaces our understanding of their value for clinicians. Although applicable across many forms of medicine, we focus on characterising the diagnostic decisions of radiologists through the concept of ecologically bounded reasoning, review the differences between clinician decision making and medical AI model decision making, and reveal how these differences pose fundamental challenges for integrating AI into radiology. We argue that clinicians are contextually motivated, mentally resourceful decision makers, whereas AI models are contextually stripped, correlational decision makers, and discuss misconceptions about clinician–AI interaction stemming from this misalignment of capabilities. We outline how future research on clinician–AI interaction could better address the cognitive considerations of decision making and be used to enhance the safety and usability of AI models in high-risk medical decision-making contexts.</p></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000955/pdfft?md5=c4262279ee0696247e86b8dc47f4a153&pid=1-s2.0-S2589750024000955-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141767680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/S2589-7500(24)00116-X
Background
The density of tumour-infiltrating lymphocytes (TILs) could be prognostic in ductal carcinoma in situ (DCIS). However, manual TIL quantification is time-consuming and suffers from interobserver and intraobserver variability. In this study, we developed a TIL-based computational pathology biomarker and evaluated its association with the risk of recurrence and benefit of adjuvant treatment in a clinical trial cohort.
Methods
In this retrospective cohort study, a computational pathology pipeline was developed to generate a TIL-based biomarker (CPath TIL categories). Subsequently, the signature underwent a masked independent validation on H&E-stained whole-section images of 755 patients with DCIS from the UK/ANZ DCIS randomised controlled trial. Specifically, continuous biomarker CPath TIL score was calculated as the average TIL density in the DCIS microenvironment and dichotomised into binary biomarker CPath TIL categories (CPath TIL-high vs CPath TIL-low) using the median value as a cutoff. The primary outcome was ipsilateral breast event (IBE; either recurrence of DCIS [DCIS-IBE] or invasive progression [I-IBE]). The Cox proportional hazards model was used to estimate the hazard ratio (HR).
Findings
CPath TIL-score was evaluable in 718 (95%) of 755 patients (151 IBEs). Patients with CPath TIL-high DCIS had a greater risk of IBE than those with CPath TIL-low DCIS (HR 2·10 [95% CI 1·39–3·18]; p=0·0004). The risk of I-IBE was greater in patients with CPath TIL-high DCIS than those with CPath TIL-low DCIS (3·09 [1·56–6·14]; p=0·0013), and the risk of DCIS-IBE was non-significantly higher in those with CPath TIL-high DCIS (1·61 [0·95–2·72]; p=0·077). A significant interaction (pinteraction=0·025) between CPath TIL categories and radiotherapy was observed with a greater magnitude of radiotherapy benefit in preventing IBE in CPath TIL-high DCIS (0·32 [0·19–0·54]) than CPath TIL-low DCIS (0·40 [0·20–0·81]).
Interpretation
High TIL density is associated with higher recurrence risk—particularly of invasive recurrence—and greater radiotherapy benefit in patients with DCIS. Our TIL-based computational pathology signature has a prognostic and predictive role in DCIS.
Funding
National Cancer Institute under award number U01CA269181, Cancer Research UK (C569/A12061; C569/A16891), and the Breast Cancer Research Foundation, New York (NY, USA).
背景：肿瘤浸润淋巴细胞（TIL）的密度可作为导管原位癌（DCIS）的预后指标。然而，人工定量 TIL 不仅耗时，而且存在观察者之间和观察者内部的差异。在本研究中，我们开发了一种基于TIL的计算病理学生物标志物，并在临床试验队列中评估了其与复发风险和辅助治疗获益的相关性：在这项回顾性队列研究中，开发了一个计算病理学管道，以生成基于TIL的生物标志物（CPath TIL类别）。随后，对英国/新西兰 DCIS 随机对照试验中 755 名 DCIS 患者的 H&E 染色全切片图像进行了独立的掩蔽验证。具体来说，连续生物标志物 CPath TIL 评分计算为 DCIS 微环境中的平均 TIL 密度，并以中值作为分界点，将其分为二元生物标志物 CPath TIL 类别（CPath TIL 高 vs CPath TIL 低）。主要结果是同侧乳腺事件（IBE；DCIS复发[DCIS-IBE]或浸润性进展[I-IBE]）。采用 Cox 比例危险模型估算危险比 (HR)：在 755 例患者（151 例 IBE）中，有 718 例（95%）的 CPath TIL 评分可进行评估。CPath TIL 高的 DCIS 患者比 CPath TIL 低的 DCIS 患者发生 IBE 的风险更高（HR 2-10 [95% CI 1-39-3-18]; p=0-0004）。CPath TIL高的DCIS患者发生I-BE的风险高于CPath TIL低的DCIS患者（3-09 [1-56-6-14]; p=0-0013），CPath TIL高的DCIS患者发生DCIS-IBE的风险无显著性差异（1-61 [0-95-2-72]; p=0-077）。CPath TIL类别与放疗之间存在明显的交互作用（pinteraction=0-025），CPath TIL高的DCIS（0-32 [0-19-0-54]）比CPath TIL低的DCIS（0-40 [0-20-0-81]）在预防IBE方面的放疗获益更大：高TIL密度与DCIS患者较高的复发风险（尤其是侵袭性复发）和更大的放疗获益相关。我们基于TIL的计算病理学特征对DCIS具有预后和预测作用：美国国立癌症研究所（获奖号：U01CA269181）、英国癌症研究中心（C569/A12061; C569/A16891）和美国纽约乳腺癌研究基金会。
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Pub Date : 2024-08-01DOI: 10.1016/S2589-7500(24)00156-0
{"title":"Correction to Lancet Digit Health 2024; 6: e562–69","authors":"","doi":"10.1016/S2589-7500(24)00156-0","DOIUrl":"10.1016/S2589-7500(24)00156-0","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001560/pdfft?md5=499968d7a55949c6abf6cf414a476422&pid=1-s2.0-S2589750024001560-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141767676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/S2589-7500(24)00144-4
{"title":"Evaluation and communication of pandemic scenarios","authors":"","doi":"10.1016/S2589-7500(24)00144-4","DOIUrl":"10.1016/S2589-7500(24)00144-4","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001444/pdfft?md5=208ba0fe86d5c6cdff7a0fec55078935&pid=1-s2.0-S2589750024001444-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141767677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/S2589-7500(24)00096-7
Background
Early identification of an acute respiratory infection is important for reducing transmission and enabling earlier therapeutic intervention. We aimed to prospectively evaluate the feasibility of home-based diagnostic self-testing of viral pathogens in individuals prompted to do so on the basis of self-reported symptoms or individual changes in physiological parameters detected via a wearable sensor.
Methods
DETECT-AHEAD was a prospective, decentralised, randomised controlled trial carried out in a subpopulation of an existing cohort (DETECT) of individuals enrolled in a digital-only observational study in the USA. Participants aged 18 years or older were randomly assigned (1:1:1) with a block randomisation scheme stratified by under-represented in biomedical research status. All participants were offered a wearable sensor (Fitbit Sense smartwatch). Participants in groups 1 and 2 received an at-home self-test kit (Alveo be.well) for two acute respiratory viral pathogens: SARS-CoV-2 and respiratory syncytial virus. Participants in group 1 could be alerted through the DETECT study app to take the at-home test on the basis of changes in their physiological data (as detected by our algorithm) or due to self-reported symptoms; those in group 2 were prompted via the app to self-test only due to symptoms. Group 3 served as the control group, without alerts or home testing capability. The primary endpoints, assessed on an intention-to-treat basis, were the number of acute respiratory infections presented (self-reported) and diagnosed (electronic health record), and the number of participants using at-home testing in groups 1 and 2. This trial is registered with ClinicalTrials.gov, NCT04336020.
Findings
Between Sept 28 and Dec 30, 2021, 450 participants were recruited and randomly assigned to group 1 (n=149), group 2 (n=151), or group 3 (n=150). 179 (40%) participants were male, 264 (59%) were female, and seven (2%) identified as other. 232 (52%) were from populations historically under-represented in biomedical research. 118 (39%) of the 300 participants in groups 1 and 2 were prompted to self-test, with 61 (52%) successfully completing self-testing. Participants were prompted to home-test more frequently due to symptoms (41 [28%] in group 1 and 51 [34%] in group 2) than due to detected physiological changes (26 [17%] in group 1). Significantly more participants in group 1 received alerts to test than did those in group 2 (67 [45%] vs 51 [34%]; p=0·047). Of the 61 individuals who were prompted to test and successfully did so, 19 (31%) tested positive for a viral pathogen—all for SARS-CoV-2. The individuals diagnosed as positive for SARS-CoV-2 in the electronic health record were eight (5%) in group 1, four (3%) in group 2, and two (1%) in group 3, but it was difficult to c
{"title":"Feasibility of wearable sensor signals and self-reported symptoms to prompt at-home testing for acute respiratory viruses in the USA (DETECT-AHEAD): a decentralised, randomised controlled trial","authors":"","doi":"10.1016/S2589-7500(24)00096-7","DOIUrl":"10.1016/S2589-7500(24)00096-7","url":null,"abstract":"<div><h3>Background</h3><p>Early identification of an acute respiratory infection is important for reducing transmission and enabling earlier therapeutic intervention. We aimed to prospectively evaluate the feasibility of home-based diagnostic self-testing of viral pathogens in individuals prompted to do so on the basis of self-reported symptoms or individual changes in physiological parameters detected via a wearable sensor.</p></div><div><h3>Methods</h3><p>DETECT-AHEAD was a prospective, decentralised, randomised controlled trial carried out in a subpopulation of an existing cohort (DETECT) of individuals enrolled in a digital-only observational study in the USA. Participants aged 18 years or older were randomly assigned (1:1:1) with a block randomisation scheme stratified by under-represented in biomedical research status. All participants were offered a wearable sensor (Fitbit Sense smartwatch). Participants in groups 1 and 2 received an at-home self-test kit (Alveo be.well) for two acute respiratory viral pathogens: SARS-CoV-2 and respiratory syncytial virus. Participants in group 1 could be alerted through the DETECT study app to take the at-home test on the basis of changes in their physiological data (as detected by our algorithm) or due to self-reported symptoms; those in group 2 were prompted via the app to self-test only due to symptoms. Group 3 served as the control group, without alerts or home testing capability. The primary endpoints, assessed on an intention-to-treat basis, were the number of acute respiratory infections presented (self-reported) and diagnosed (electronic health record), and the number of participants using at-home testing in groups 1 and 2. This trial is registered with <span><span>ClinicalTrials.gov</span><svg><path></path></svg></span>, <span><span>NCT04336020</span><svg><path></path></svg></span>.</p></div><div><h3>Findings</h3><p>Between Sept 28 and Dec 30, 2021, 450 participants were recruited and randomly assigned to group 1 (n=149), group 2 (n=151), or group 3 (n=150). 179 (40%) participants were male, 264 (59%) were female, and seven (2%) identified as other. 232 (52%) were from populations historically under-represented in biomedical research. 118 (39%) of the 300 participants in groups 1 and 2 were prompted to self-test, with 61 (52%) successfully completing self-testing. Participants were prompted to home-test more frequently due to symptoms (41 [28%] in group 1 and 51 [34%] in group 2) than due to detected physiological changes (26 [17%] in group 1). Significantly more participants in group 1 received alerts to test than did those in group 2 (67 [45%] <em>vs</em> 51 [34%]; p=0·047). Of the 61 individuals who were prompted to test and successfully did so, 19 (31%) tested positive for a viral pathogen—all for SARS-CoV-2. The individuals diagnosed as positive for SARS-CoV-2 in the electronic health record were eight (5%) in group 1, four (3%) in group 2, and two (1%) in group 3, but it was difficult to c","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11296689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141767678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/S2589-7500(24)00157-2
{"title":"Pathology in the era of generative AI","authors":"","doi":"10.1016/S2589-7500(24)00157-2","DOIUrl":"10.1016/S2589-7500(24)00157-2","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001572/pdfft?md5=b34ff67d1c10eff92c7810c03c24a9b8&pid=1-s2.0-S2589750024001572-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141767682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/S2589-7500(24)00114-6
The rapid evolution of generative artificial intelligence (AI) models including OpenAI's ChatGPT signals a promising era for medical research. In this Viewpoint, we explore the integration and challenges of large language models (LLMs) in digital pathology, a rapidly evolving domain demanding intricate contextual understanding. The restricted domain-specific efficiency of LLMs necessitates the advent of tailored AI tools, as illustrated by advancements seen in the last few years including FrugalGPT and BioBERT. Our initiative in digital pathology emphasises the potential of domain-specific AI tools, where a curated literature database coupled with a user-interactive web application facilitates precise, referenced information retrieval. Motivated by the success of this initiative, we discuss how domain-specific approaches substantially minimise the risk of inaccurate responses, enhancing the reliability and accuracy of information extraction. We also highlight the broader implications of such tools, particularly in streamlining access to scientific research and democratising access to computational pathology techniques for scientists with little coding experience. This Viewpoint calls for an enhanced integration of domain-specific text-generation AI tools in academic settings to facilitate continuous learning and adaptation to the dynamically evolving landscape of medical research.
{"title":"ChatGPT for digital pathology research","authors":"","doi":"10.1016/S2589-7500(24)00114-6","DOIUrl":"10.1016/S2589-7500(24)00114-6","url":null,"abstract":"<div><p>The rapid evolution of generative artificial intelligence (AI) models including OpenAI's ChatGPT signals a promising era for medical research. In this Viewpoint, we explore the integration and challenges of large language models (LLMs) in digital pathology, a rapidly evolving domain demanding intricate contextual understanding. The restricted domain-specific efficiency of LLMs necessitates the advent of tailored AI tools, as illustrated by advancements seen in the last few years including FrugalGPT and BioBERT. Our initiative in digital pathology emphasises the potential of domain-specific AI tools, where a curated literature database coupled with a user-interactive web application facilitates precise, referenced information retrieval. Motivated by the success of this initiative, we discuss how domain-specific approaches substantially minimise the risk of inaccurate responses, enhancing the reliability and accuracy of information extraction. We also highlight the broader implications of such tools, particularly in streamlining access to scientific research and democratising access to computational pathology techniques for scientists with little coding experience. This Viewpoint calls for an enhanced integration of domain-specific text-generation AI tools in academic settings to facilitate continuous learning and adaptation to the dynamically evolving landscape of medical research.</p></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11299190/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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