Lancet Digital Health最新文献

{"title":"Clinical trials for implantable neural prostheses: understanding the ethical and technical requirements.","authors":"Marcello Ienca, Giacomo Valle, Stanisa Raspopovic","doi":"10.1016/S2589-7500(24)00222-X","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00222-X","url":null,"abstract":"<p><p>Neuroprosthetics research has entered a stage in which animal models and proof-of-concept studies are translated into clinical applications, often combining implants with artificial intelligence techniques. This new phase raises the question of how clinical trials should be designed to scientifically and ethically address the unique features of neural prostheses. Neural prostheses are complex cyberbiological devices able to acquire and process data; hence, their assessment is not reducible to only third-party safety and efficacy evaluations as in pharmacological research. In addition, assessment of neural prostheses requires a causal understanding of their mechanisms, and scrutiny of their information security and legal liability standards. Some neural prostheses affect not only human behaviour, but also psychological faculties such as consciousness, cognition, and affective states. In this Viewpoint, we argue that the technological novelty of neural prostheses could generate challenges for technology assessment, clinical validation, and research ethics oversight. To this end, we identify a set of methodological and research ethics challenges specific to this medical technology innovation. We provide insights into relevant ethical guidelines and assess whether oversight mechanisms are well equipped to ensure adequate clinical and ethical use. Finally, we outline patient-centred research ethics requirements for clinical trials involving implantable neural prostheses.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large language models for the mental health community: framework for translating code to care.","authors":"Matteo Malgaroli, Katharina Schultebraucks, Keris Jan Myrick, Alexandre Andrade Loch, Laura Ospina-Pinillos, Tanzeem Choudhury, Roman Kotov, Munmun De Choudhury, John Torous","doi":"10.1016/S2589-7500(24)00255-3","DOIUrl":"10.1016/S2589-7500(24)00255-3","url":null,"abstract":"<p><p>Large language models (LLMs) offer promising applications in mental health care to address gaps in treatment and research. By leveraging clinical notes and transcripts as data, LLMs could improve diagnostics, monitoring, prevention, and treatment of mental health conditions. However, several challenges persist, including technical costs, literacy gaps, risk of biases, and inequalities in data representation. In this Viewpoint, we propose a sociocultural-technical approach to address these challenges. We highlight five key areas for development: (1) building a global clinical repository to support LLMs training and testing, (2) designing ethical usage settings, (3) refining diagnostic categories, (4) integrating cultural considerations during development and deployment, and (5) promoting digital inclusivity to ensure equitable access. We emphasise the need for developing representative datasets, interpretable clinical decision support systems, and new roles such as digital navigators. Only through collaborative efforts across all stakeholders, unified by a sociocultural-technical framework, can we clinically deploy LLMs while ensuring equitable access and mitigating risks.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142956984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The new digital era in decompensated cirrhosis.","authors":"Kohilan Gananandan, Konstantin Kazankov, Elliot B Tapper, Rajeshwar P Mookerjee","doi":"10.1016/S2589-7500(24)00174-2","DOIUrl":"10.1016/S2589-7500(24)00174-2","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","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":"","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, Alexander F Hoffmann, Amelia L M Tan, Mariné Nalbandyan, Gilbert S Omenn, Diego R Mazzotti, Alejandro Hernández-Arango, Shyam Visweswaran, Shruthi Venkatesh, Kenneth D Mandl, Florence T Bourgeois, James W K Lee, Andrew Makmur, David A Hanauer, Michael G Semanik, Lauren T Kerivan, Terra Hill, Julian Forero, Carlos Restrepo, Matteo Vigna, Piero Ceriana, Noor Abu-El-Rub, Paul Avillach, Riccardo Bellazzi, Thomas Callaci, Alba Gutiérrez-Sacristán, Alberto Malovini, Jomol P Mathew, Michele Morris, Venkatesh L Murthy, Tommaso M Buonocore, Enea Parimbelli, Lav P Patel, Carlos Sáez, Malarkodi Jebathilagam Samayamuthu, Jeffrey A Thompson, Valentina Tibollo, Zongqi Xia, Isaac S Kohane","doi":"10.1016/S2589-7500(24)00246-2","DOIUrl":"10.1016/S2589-7500(24)00246-2","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&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;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&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;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&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;/p&gt;&lt;p&gt;&lt;strong&gt;Interpretation: &lt;/strong&gt;The results of our model-evaluation study suggest that GPT-4 is accurate when analysing medical notes in three differe","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","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":"","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, Sharleen G Maduranayagam, Nirekah Suthakar, Amy Li, Cynthia Lokker, Alfonso Iorio, R Brian Haynes, David Moher","doi":"10.1016/S2589-7500(24)00202-4","DOIUrl":"10.1016/S2589-7500(24)00202-4","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","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":"","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":" ","pages":"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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of key factors related to digital health observational study adherence and retention by data-driven approaches: an exploratory secondary analysis of two prospective longitudinal studies.","authors":"Peter J Cho, Iredia M Olaye, Md Mobashir Hasan Shandhi, Eric J Daza, Luca Foschini, Jessilyn P Dunn","doi":"10.1016/S2589-7500(24)00219-X","DOIUrl":"10.1016/S2589-7500(24)00219-X","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Longitudinal digital health studies combine passively collected information from digital devices, such as commercial wearable devices, and actively contributed data, such as surveys, from participants. Although the use of smartphones and access to the internet supports the development of these studies, challenges exist in collecting representative data due to low adherence and retention. We aimed to identify key factors related to adherence and retention in digital health studies and develop a methodology to identify factors that are associated with and might affect study participant engagement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In this exploratory secondary analysis, we used data from two separate prospective longitudinal digital health studies, conducted among adult participants (age ≥18 years) during the COVID-19 pandemic by the BIG IDEAs Laboratory (BIL) at Duke University (Durham, NC, USA; April 2, 2020 to May 25, 2021) and Evidation Health (San Mateo, CA, USA; April 4 to Aug 31, 2020). Prospective daily or weekly surveys were administered for up to 15 months in the BIL study and daily surveys were administered for 5 months in the Evidation Health study. We defined metrics related to adherence to assess how participants engage with longitudinal digital health studies and developed models to infer how demographic factors and the day of survey delivery might be associated with these metrics. We defined retention as the time until a participant drops out of the study. For the purpose of clustering analysis, we defined three metrics of survey adherence: (1) total number of surveys completed, (2) participation regularity (ie, frequency of filling out surveys consecutively), and (3) time of activity (ie, engagement pattern relative to enrolment time). We assessed these metrics and explored differences by age, sex, race, and day of survey delivery. We analysed the data by unsupervised clustering, survival analysis, and recurrent event analysis with multistate modelling, with analyses restricted to individuals who provided data on age, sex, and race.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;In the BIL study, 5784 unique participants with the required demographic data completed 388 600 unique daily surveys (mean 67 [SD 90] surveys per participant). In the Evidation Health study, 89 479 unique participants with the required demographic data completed 2 080 992 unique daily surveys (23 [32] surveys per participant). Participants were grouped into adherence clusters based on the three metrics of adherence, and we identified statistically discernible differences in age, race, and sex between clusters. Most of the individuals aged 18-29 years were observed in the clusters with low or medium adherence, whereas the oldest age group (≥60 years) was generally more represented in clusters with high adherence than younger age groups. For retention, survival analysis indicated that 18-29 years was the age group with the highest risk of exit","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"e23-e34"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11725373/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899342","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":"Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations.","authors":"Joseph E Alderman, Joanne Palmer, Elinor Laws, Melissa D McCradden, Johan Ordish, Marzyeh Ghassemi, Stephen R Pfohl, Negar Rostamzadeh, Heather Cole-Lewis, Ben Glocker, Melanie Calvert, Tom J Pollard, Jaspret Gill, Jacqui Gath, Adewale Adebajo, Jude Beng, Cassandra H Leung, Stephanie Kuku, Lesley-Anne Farmer, Rubeta N Matin, Bilal A Mateen, Francis McKay, Katherine Heller, Alan Karthikesalingam, Darren Treanor, Maxine Mackintosh, Lauren Oakden-Rayner, Russell Pearson, Arjun K Manrai, Puja Myles, Judit Kumuthini, Zoher Kapacee, Neil J Sebire, Lama H Nazer, Jarrel Seah, Ashley Akbari, Lew Berman, Judy W Gichoya, Lorenzo Righetto, Diana Samuel, William Wasswa, Maria Charalambides, Anmol Arora, Sameer Pujari, Charlotte Summers, Elizabeth Sapey, Sharon Wilkinson, Vishal Thakker, Alastair Denniston, Xiaoxuan Liu","doi":"10.1016/S2589-7500(24)00224-3","DOIUrl":"10.1016/S2589-7500(24)00224-3","url":null,"abstract":"<p><p>Without careful dissection of the ways in which biases can be encoded into artificial intelligence (AI) health technologies, there is a risk of perpetuating existing health inequalities at scale. One major source of bias is the data that underpins such technologies. The STANDING Together recommendations aim to encourage transparency regarding limitations of health datasets and proactive evaluation of their effect across population groups. Draft recommendation items were informed by a systematic review and stakeholder survey. The recommendations were developed using a Delphi approach, supplemented by a public consultation and international interview study. Overall, more than 350 representatives from 58 countries provided input into this initiative. 194 Delphi participants from 25 countries voted and provided comments on 32 candidate items across three electronic survey rounds and one in-person consensus meeting. The 29 STANDING Together consensus recommendations are presented here in two parts. Recommendations for Documentation of Health Datasets provide guidance for dataset curators to enable transparency around data composition and limitations. Recommendations for Use of Health Datasets aim to enable identification and mitigation of algorithmic biases that might exacerbate health inequalities. These recommendations are intended to prompt proactive inquiry rather than acting as a checklist. We hope to raise awareness that no dataset is free of limitations, so transparent communication of data limitations should be perceived as valuable, and absence of this information as a limitation. We hope that adoption of the STANDING Together recommendations by stakeholders across the AI health technology lifecycle will enable everyone in society to benefit from technologies which are safe and effective.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"e64-e88"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11668905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865660","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":"Building robust, proportionate, and timely approaches to regulation and evaluation of digital mental health technologies.","authors":"Gareth Hopkin, Richard Branson, Paul Campbell, Holly Coole, Sophie Cooper, Francesca Edelmann, Grace Gatera, Jamie Morgan, Mark Salmon","doi":"10.1016/S2589-7500(24)00215-2","DOIUrl":"10.1016/S2589-7500(24)00215-2","url":null,"abstract":"<p><p>Demand for mental health services exceeds available resources globally, and access to diagnosis and evidence-based treatment is affected by long delays. Digital mental health technologies present an opportunity to reimagine the delivery of mental health support by providing innovative, effective, and tailored approaches that meet people's individual preferences and goals. These technologies also present new challenges, however, and efforts must be made to ensure they are safe and effective. The UK Medicines and Healthcare products Regulatory Agency and the National Institute for Health and Care Excellence have launched a partnership, funded by Wellcome, that explores regulation and evaluation of digital mental health technologies. This Viewpoint describes a series of key challenges across the regulatory and health technology assessment pathways and aims to facilitate discussions to ensure that approaches to regulation and evaluation are informed by patients, the public, and professionals working within mental health. We invite partners from across the mental health community to engage with, collaborate with, and provide scrutiny of this project to ensure it delivers the best possible outcomes.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"e89-e93"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using artificial intelligence technologies to improve skin cancer detection in primary care.","authors":"Owain T Jones, Rubeta N Matin, Fiona M Walter","doi":"10.1016/S2589-7500(24)00216-4","DOIUrl":"10.1016/S2589-7500(24)00216-4","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"e8-e10"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}