Lancet Digital Health最新文献

{"title":"The new digital era in decompensated cirrhosis","authors":"Kohilan Gananandan BMBCh ,&nbsp;Konstantin Kazankov PhD ,&nbsp;Elliot B Tapper MD ,&nbsp;Prof Rajeshwar P Mookerjee FRCP","doi":"10.1016/S2589-7500(24)00174-2","DOIUrl":"10.1016/S2589-7500(24)00174-2","url":null,"abstract":"<div><div>There is a growing global burden of liver disease with the current management for complications of liver cirrhosis being reactive as opposed to proactive, affecting outcomes. Management can often be suboptimal in overburdened health-care systems with considerable socioeconomic and geographical disparity existing, which was exacerbated by the COVID-19 pandemic, highlighting the need for sustainable care pathways to be delivered remotely. To this end, digital health care could be the key and, in this Review, we highlight the principal studies that have explored the use of digital technology in the management of cirrhosis complications. While digital health care is a somewhat new field, considerable advances have been made in various domains, particularly in the development of remote monitoring and risk modelling. We aim to provide a balanced perspective of the opportunities for and barriers to the integration of digital technology into established liver-care pathways. Lastly, we reflect on the current acceptability of digital health care and the required future directions to ensure the digital transformation of hepatology is a success.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"Pages e54-e63"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689308","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}

{"title":"Generative Pre-trained Transformer 4 (GPT-4) in clinical settings","authors":"Valentina Bellini ,&nbsp;Elena Giovanna Bignami","doi":"10.1016/j.landig.2024.12.002","DOIUrl":"10.1016/j.landig.2024.12.002","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"Pages e6-e7"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899278","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}

{"title":"The potential of Generative Pre-trained Transformer 4 (GPT-4) to analyse medical notes in three different languages: a retrospective model-evaluation study","authors":"Maria Clara Saad Menezes MD ,&nbsp;Alexander F Hoffmann MBA ,&nbsp;Amelia L M Tan PhD ,&nbsp;Mariné Nalbandyan MD ,&nbsp;Prof Gilbert S Omenn MD ,&nbsp;Diego R Mazzotti PhD ,&nbsp;Alejandro Hernández-Arango MD ,&nbsp;Prof Shyam Visweswaran MD ,&nbsp;Shruthi Venkatesh BSc ,&nbsp;Prof Kenneth D Mandl MD ,&nbsp;Florence T Bourgeois MD ,&nbsp;James W K Lee MD ,&nbsp;Andrew Makmur MBBS ,&nbsp;David A Hanauer MD ,&nbsp;Michael G Semanik MD ,&nbsp;Lauren T Kerivan MD ,&nbsp;Terra Hill MD ,&nbsp;Julian Forero MD ,&nbsp;Carlos Restrepo MD ,&nbsp;Matteo Vigna MD ,&nbsp;Prof Isaac S Kohane MD","doi":"10.1016/S2589-7500(24)00246-2","DOIUrl":"10.1016/S2589-7500(24)00246-2","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Patient notes contain substantial information but are difficult for computers to analyse due to their unstructured format. Large-language models (LLMs), such as Generative Pre-trained Transformer 4 (GPT-4), have changed our ability to process text, but we do not know how effectively they handle medical notes. We aimed to assess the ability of GPT-4 to answer predefined questions after reading medical notes in three different languages.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;For this retrospective model-evaluation study, we included eight university hospitals from four countries (ie, the USA, Colombia, Singapore, and Italy). Each site submitted seven de-identified medical notes related to seven separate patients to the coordinating centre between June 1, 2023, and Feb 28, 2024. Medical notes were written between Feb 1, 2020, and June 1, 2023. One site provided medical notes in Spanish, one site provided notes in Italian, and the remaining six sites provided notes in English. We included admission notes, progress notes, and consultation notes. No discharge summaries were included in this study. We advised participating sites to choose medical notes that, at time of hospital admission, were for patients who were male or female, aged 18–65 years, had a diagnosis of obesity, had a diagnosis of COVID-19, and had submitted an admission note. Adherence to these criteria was optional and participating sites randomly chose which medical notes to submit. When entering information into GPT-4, we prepended each medical note with an instruction prompt and a list of 14 questions that had been chosen a priori. Each medical note was individually given to GPT-4 in its original language and in separate sessions; the questions were always given in English. At each site, two physicians independently validated responses by GPT-4 and responded to all 14 questions. Each pair of physicians evaluated responses from GPT-4 to the seven medical notes from their own site only. Physicians were not masked to responses from GPT-4 before providing their own answers, but were masked to responses from the other physician.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;We collected 56 medical notes, of which 42 (75%) were in English, seven (13%) were in Italian, and seven (13%) were in Spanish. For each medical note, GPT-4 responded to 14 questions, resulting in 784 responses. In 622 (79%, 95% CI 76–82) of 784 responses, both physicians agreed with GPT-4. In 82 (11%, 8–13) responses, only one physician agreed with GPT-4. In the remaining 80 (10%, 8–13) responses, neither physician agreed with GPT-4. Both physicians agreed with GPT-4 more often for medical notes written in Spanish (86 [88%, 95% CI 79–93] of 98 responses) and Italian (82 [84%, 75–90] of 98 responses) than in English (454 [77%, 74–80] of 588 responses).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Interpretation&lt;/h3&gt;&lt;div&gt;The results of our model-evaluation study suggest that GPT-4 is accurate when analysing medical notes in t","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"Pages e35-e43"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899352","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}

{"title":"Implementation of an automated deep learning-based quality assurance tool for vertebral body identification in radiotherapy planning","authors":"Nico Sollmann","doi":"10.1016/S2589-7500(24)00266-8","DOIUrl":"10.1016/S2589-7500(24)00266-8","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"Pages e2-e3"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899343","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}

{"title":"Leveraging artificial intelligence for predicting spontaneous closure of perimembranous ventricular septal defect in children: a multicentre, retrospective study in China","authors":"Jing Sun MD ,&nbsp;Tienan Feng MD ,&nbsp;Bo Wang BS ,&nbsp;Prof Fen Li MD ,&nbsp;Prof Bo Han MD ,&nbsp;Prof Maoping Chu MD ,&nbsp;Prof Fangqi Gong MD ,&nbsp;Prof Qijian Yi MD ,&nbsp;Xin Zhou MD ,&nbsp;Prof Sun Chen MD ,&nbsp;Prof Xin Sun MD ,&nbsp;Prof Kun Sun MD","doi":"10.1016/S2589-7500(24)00245-0","DOIUrl":"10.1016/S2589-7500(24)00245-0","url":null,"abstract":"<div><h3>Background</h3><div>Perimembranous ventricular septal defect (PMVSD) is a prevalent congenital heart disease, presenting challenges in predicting spontaneous closure, which is crucial for therapeutic decisions. Existing models mainly rely on structured echocardiographic parameters or restricted data. This study introduces an artificial intelligence (AI)-based model, which uses natural language processing (NLP) and machine learning with the aim of improving spontaneous closure predictability in PMVSD.</div></div><div><h3>Methods</h3><div>We did a multicentre, retrospective analysis using data from 29 142 PMVSD patients across six tertiary centres in China from May, 2004, to September, 2022, for training (70%) and validation (30%; dataset 1, 27 269 patients), and from September, 2001, to December, 2009 for testing (dataset 2, 1873 patients). NLP extracted structured data from echocardiography reports and medical records, which were used to develop machine learning models. Models were evaluated for spontaneous closure occurrence and timing by use of area under the receiver operating characteristic curve (AUC), decision curve analysis, and calibration index.</div></div><div><h3>Findings</h3><div>Spontaneous closure occurred in 3520 patients (12·1%) at a median of 31 months (IQR 16−56). Eleven NLP-derived predictors, identified via least absolute shrinkage and selection operator, highlighted the importance of defect morphology and patient age. The random survival forest algorithm, selected for its superior concordance indexes, showed excellent predictive performance with validation set AUCs (95% CI) of 0·95 (0·94−0·96) for 1-year and 3-year predictions, and 0·95 (0·95−0·96) for 5-year predictions; testing set AUCs were 0·95 (0·94−0·97) for 1-year predictions, 0·97 (0·96−0·98) for 3-year predictions, and 0·98 (0·97−0·99) for 5-year predictions. The model showed high clinical utility through decision curve analysis, calibration, and risk stratification, maintaining consistent accuracy across centres and subgroups.</div></div><div><h3>Interpretation</h3><div>This AI-based model for predicting spontaneous closure in PMVSD patients represents a substantial advancement, potentially improving patient management, reducing risks of delayed or inappropriate treatment, and enhancing clinical outcomes.</div></div><div><h3>Funding</h3><div>National Natural Science Foundation of China, Shanghai Municipal Hospital Clinical Technology Project, Shanghai Municipal Health Commission, and Clinical Research Unit of XinHua Hospital.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"Pages e44-e53"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899348","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}

{"title":"Nobel Prizes honour AI pioneers and pioneering AI","authors":"Talha Burki","doi":"10.1016/j.landig.2024.12.001","DOIUrl":"10.1016/j.landig.2024.12.001","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"Pages e11-e12"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171059","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}

{"title":"A prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy","authors":"Christopher E Kehayias PhD ,&nbsp;Dennis Bontempi MEng ,&nbsp;Sarah Quirk PhD ,&nbsp;Scott Friesen MSc ,&nbsp;Jeremy Bredfeldt PhD ,&nbsp;Tara Kosak MD PhD ,&nbsp;Meghan Kearney MS ,&nbsp;Roy Tishler MD PhD ,&nbsp;Itai Pashtan MD ,&nbsp;Mai Anh Huynh MD PhD ,&nbsp;Hugo J W L Aerts PhD ,&nbsp;Raymond H Mak MD ,&nbsp;Christian V Guthier PhD","doi":"10.1016/S2589-7500(24)00243-7","DOIUrl":"10.1016/S2589-7500(24)00243-7","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We developed a fully automated deep learning-based spine-targeting quality assurance system (DL-SpiQA) for detecting treatment at the wrong anatomic level. DL-SpiQA was evaluated based on retrospective testing of spine radiation therapy treatments and prospective clinical deployment.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;The DL-SpiQA workflow involves auto-segmentation and labelling of all vertebral volumes on CT imaging using TotalSegmentator, an open-source deep learning algorithm based on nnU-Net, calculation of the radiation dose to each vertebra, and flagging and categorisation of potential treatments at the wrong anatomic level with automated email reports sent to involved radiation therapy personnel. We developed the DL-SpiQA tool based on retrospective clinical data from patients treated with palliative spine radiation therapy from sites included in the multicentre hospital network between Feb 12, 2014, and Nov 15, 2022. We used historic cases of patients who had a near-miss (ie, wrong-anatomic-level errors caught before the patient was treated) or had received wrong-anatomic-level treatment to test whether the tool could identify known errors successfully. We then used the tool prospectively over 15 months (April 24, 2023, to July 22, 2024) to evaluate any new spine radiation therapy treatment plan created for a patient, looking for any targeting errors, and dose and volume discrepancies. An email report was circulated with all the radiation therapy personnel; if any errors were found, these were highlighted and each error was defined. The tool was internally validated. All cases flagged by DL-SpiQA for both the retrospective and prospective studies were manually reviewed for dosimetric targeting, variant spine anatomy or spinal anomalies, and artificial intelligence (AI) segmentation errors. DL-SpiQA was further validated based on false positive and negative rates estimated from the retrospective results.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;DL-SpiQA was first tested retrospectively on 513 patients with segmentation of 10 106 vertebrae. The system raised flags for ten dose discrepancies, 49 normal anatomic variants, 49 cases with implants or other anomalies, and 20 segmentation errors (4% false positive rate). DL-SpiQA caught one historic treatment at the wrong anatomic level and three near-misses. DL-SpiQA was then prospectively deployed, reviewing 520 cases and identifying six documentation errors, which triggered detailed review by clinicians, and 43 additional cases, which confirmed clinical knowledge of variant anatomy. In all detected cases (ie, 49 of 520 cases in total), the appropriate personnel were alerted. A false negative rate of 0·03% is estimated based on the 4% AI segmentation error rate and the frequency of reported spine radiation therapy errors.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Interpretation&lt;/h3&gt;&lt;div&gt;The low false pos","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"Pages e13-e22"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899313","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}

{"title":"Improving digital study designs: better metrics, systematic reporting, and an engineering mindset","authors":"Viktor von Wyl","doi":"10.1016/S2589-7500(24)00244-9","DOIUrl":"10.1016/S2589-7500(24)00244-9","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"Pages e4-e5"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899346","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}

{"title":"Attitudes and perceptions of medical researchers towards the use of artificial intelligence chatbots in the scientific process: an international cross-sectional survey","authors":"Jeremy Y Ng PhD ,&nbsp;Sharleen G Maduranayagam BSc ,&nbsp;Nirekah Suthakar BSc ,&nbsp;Amy Li BSc ,&nbsp;Cynthia Lokker PhD ,&nbsp;Prof Alfonso Iorio MD PhD ,&nbsp;Prof R Brian Haynes MD PhD ,&nbsp;Prof David Moher PhD","doi":"10.1016/S2589-7500(24)00202-4","DOIUrl":"10.1016/S2589-7500(24)00202-4","url":null,"abstract":"<div><div>Chatbots are artificial intelligence (AI) programs designed to simulate conversations with humans that present opportunities and challenges in scientific research. Despite growing clarity from publishing organisations on the use of AI chatbots, researchers' perceptions remain less understood. In this international cross-sectional survey, we aimed to assess researchers' attitudes, familiarity, perceived benefits, and limitations related to AI chatbots. Our online survey was open from July 9 to Aug 11, 2023, with 61 560 corresponding authors identified from 122 323 articles indexed in PubMed. 2452 (4·0%) provided responses and 2165 (94·5%) of 2292 who met eligibility criteria completed the survey. 1161 (54·0%) of 2149 respondents were male and 959 (44·6%) were female. 1294 (60·5%) of 2138 respondents were familiar with AI chatbots, and 945 (44·5%) of 2125 had previously used AI chatbots in research. Only 244 (11·4%) of 2137 reported institutional training on AI tools, and 211 (9·9%) of 2131 noted institutional policies on AI chatbot use. Despite mixed opinions on the benefits, 1428 (69·7%) of 2048 expressed interest in further training. Although many valued AI chatbots for reducing administrative workload (1299 [66·9%] of 1941), there was insufficient understanding of the decision making process (1484 [77·2%] of 1923). Overall, this study highlights substantial interest in AI chatbots among researchers, but also points to the need for more formal training and clarity on their use.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"Pages e94-e102"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645003","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}

{"title":"A long STANDING commitment to improving health care","authors":"The Lancet Digital Health","doi":"10.1016/j.landig.2024.12.005","DOIUrl":"10.1016/j.landig.2024.12.005","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"Page e1"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865673","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}