Pub Date : 2025-01-01DOI: 10.1016/j.landig.2024.12.001
Talha Burki
{"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}
Pub Date : 2025-01-01DOI: 10.1016/S2589-7500(24)00243-7
Christopher E Kehayias PhD , Dennis Bontempi MEng , Sarah Quirk PhD , Scott Friesen MSc , Jeremy Bredfeldt PhD , Tara Kosak MD PhD , Meghan Kearney MS , Roy Tishler MD PhD , Itai Pashtan MD , Mai Anh Huynh MD PhD , Hugo J W L Aerts PhD , Raymond H Mak MD , Christian V Guthier PhD
<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Findings</h3><div>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.</div></div><div><h3>Interpretation</h3><div>The low false pos
{"title":"A prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy","authors":"Christopher E Kehayias PhD , Dennis Bontempi MEng , Sarah Quirk PhD , Scott Friesen MSc , Jeremy Bredfeldt PhD , Tara Kosak MD PhD , Meghan Kearney MS , Roy Tishler MD PhD , Itai Pashtan MD , Mai Anh Huynh MD PhD , Hugo J W L Aerts PhD , Raymond H Mak MD , Christian V Guthier PhD","doi":"10.1016/S2589-7500(24)00243-7","DOIUrl":"10.1016/S2589-7500(24)00243-7","url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Findings</h3><div>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.</div></div><div><h3>Interpretation</h3><div>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}
Pub Date : 2025-01-01DOI: 10.1016/S2589-7500(24)00244-9
Viktor von Wyl
{"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}
Pub Date : 2025-01-01DOI: 10.1016/S2589-7500(24)00202-4
Jeremy Y Ng PhD , Sharleen G Maduranayagam BSc , Nirekah Suthakar BSc , Amy Li BSc , Cynthia Lokker PhD , Prof Alfonso Iorio MD PhD , Prof R Brian Haynes MD PhD , Prof David Moher PhD
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.
{"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 , Sharleen G Maduranayagam BSc , Nirekah Suthakar BSc , Amy Li BSc , Cynthia Lokker PhD , Prof Alfonso Iorio MD PhD , Prof R Brian Haynes MD PhD , 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}
Pub Date : 2025-01-01DOI: 10.1016/j.landig.2024.12.005
The Lancet Digital Health
{"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}
Pub Date : 2025-01-01DOI: 10.1016/S2589-7500(24)00219-X
Peter J Cho BA , Iredia M Olaye PhD , Md Mobashir Hasan Shandhi PhD , Eric J Daza DrPH , Luca Foschini PhD , Prof Jessilyn P Dunn PhD
<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Findings</h3><div>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
背景：纵向数字健康研究结合了从数字设备（如商业可穿戴设备）被动收集的信息和参与者主动提供的数据（如调查）。尽管智能手机的使用和互联网的接入支持了这些研究的发展，但由于依从性和保留率低，在收集代表性数据方面存在挑战。我们的目标是确定数字健康研究中与依从性和保留相关的关键因素，并开发一种方法来确定与研究参与者参与相关并可能影响研究参与者参与的因素。方法：在这项探索性的二次分析中，我们使用了来自两项独立的前瞻性纵向数字健康研究的数据，这些研究是由杜克大学（Durham， NC， USA）的BIG IDEAs实验室（BIL）在COVID-19大流行期间对成年参与者（年龄≥18岁）进行的；2020年4月2日至2021年5月25日)和Evidation Health (San Mateo， CA， USA；2020年4月4日至8月31日)。在BIL研究中进行了长达15个月的前瞻性每日或每周调查，在Evidation Health研究中进行了5个月的每日调查。我们定义了与依从性相关的指标，以评估参与者如何参与纵向数字健康研究，并开发了模型，以推断人口因素和调查交付日期如何与这些指标相关联。我们将保留时间定义为参与者退出研究的时间。为了进行聚类分析，我们定义了调查依从性的三个指标：(1)完成调查的总数，(2)参与频率（即连续填写调查的频率）和(3)活动时间（即相对于注册时间的参与模式）。我们评估了这些指标，并探讨了年龄、性别、种族和调查交付日期的差异。我们通过无监督聚类、生存分析和多状态模型的复发事件分析来分析数据，分析仅限于提供年龄、性别和种族数据的个体。研究结果：在BIL研究中，5784名具有所需人口统计数据的独特参与者完成了388 600次独特的每日调查（平均每位参与者67次[SD 90]调查）。在Evidation Health研究中，89 479名具有所需人口统计数据的独特参与者完成了2 080 992项独特的每日调查（每位参与者23 bb10项调查）。根据依从性的三个指标将参与者分为依从性组，我们确定了组间年龄、种族和性别的统计学差异。大多数年龄在18-29岁的个体被观察到在低或中等依从性的集群中，而年龄最大的年龄组（≥60岁）通常在高依从性的集群中比年轻年龄组更多。对于保留，生存分析表明18-29岁是在任何给定时间点退出研究的风险最高的年龄组(BIL研究，18-29岁vs≥60岁的风险比[HR]， 1.69 [95% CI 1.53 -1·86；解释：我们的分析显示，年龄与依从性和保留率始终相关，年轻参与者的依从性较低，退出率高于年长参与者。无监督聚类和生存分析是该领域的既定方法，而据我们所知，使用复发事件分析是将该方法应用于远程数字健康数据的第一个实例。这些方法有助于了解参与者对数字健康研究的参与情况，支持有针对性的措施，以提高依从性和保留性。资助：美国国家科学基金会、美国国立卫生研究院、微软健康人工智能、杜克临床和转化科学研究所、北卡罗来纳生物技术中心、杜克医学中心、杜克巴斯连接、杜克马戈利斯卫生政策中心和杜克信息技术办公室。
{"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 BA , Iredia M Olaye PhD , Md Mobashir Hasan Shandhi PhD , Eric J Daza DrPH , Luca Foschini PhD , Prof Jessilyn P Dunn PhD","doi":"10.1016/S2589-7500(24)00219-X","DOIUrl":"10.1016/S2589-7500(24)00219-X","url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Findings</h3><div>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 ","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"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}
Pub Date : 2025-01-01DOI: 10.1016/S2589-7500(24)00224-3
Joseph E Alderman MBChB , Joanne Palmer PhD , Elinor Laws MBBCh , Melissa D McCradden PhD , Johan Ordish MA , Marzyeh Ghassemi PhD , Stephen R Pfohl PhD , Negar Rostamzadeh PhD , Heather Cole-Lewis PhD , Prof Ben Glocker PhD , Prof Melanie Calvert PhD , Tom J Pollard PhD , Jaspret Gill MSc , Jacqui Gath MBCS , Adewale Adebajo MBE , Jude Beng BSc , Cassandra H Leung , Stephanie Kuku MD , Lesley-Anne Farmer BSc , Rubeta N Matin PhD , Xiaoxuan Liu PhD
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.
{"title":"Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations","authors":"Joseph E Alderman MBChB , Joanne Palmer PhD , Elinor Laws MBBCh , Melissa D McCradden PhD , Johan Ordish MA , Marzyeh Ghassemi PhD , Stephen R Pfohl PhD , Negar Rostamzadeh PhD , Heather Cole-Lewis PhD , Prof Ben Glocker PhD , Prof Melanie Calvert PhD , Tom J Pollard PhD , Jaspret Gill MSc , Jacqui Gath MBCS , Adewale Adebajo MBE , Jude Beng BSc , Cassandra H Leung , Stephanie Kuku MD , Lesley-Anne Farmer BSc , Rubeta N Matin PhD , Xiaoxuan Liu PhD","doi":"10.1016/S2589-7500(24)00224-3","DOIUrl":"10.1016/S2589-7500(24)00224-3","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"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}
Pub Date : 2025-01-01DOI: 10.1016/S2589-7500(24)00216-4
Owain T Jones , Rubeta N Matin , Fiona M Walter
{"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":"7 1","pages":"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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/S2589-7500(24)00215-2
Gareth Hopkin PhD , Richard Branson MA , Paul Campbell FRCA , Holly Coole PgDip , Sophie Cooper BSc , Francesca Edelmann BSc , Grace Gatera BA , Jamie Morgan MA , Mark Salmon MBA
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.
在全球范围内，对心理健康服务的需求超过了可用资源，而获得诊断和循证治疗则受到长期拖延的影响。数字心理健康技术通过提供创新、有效和量身定制的方法，满足人们的个人偏好和目标，为重新想象心理健康支持的提供方式提供了机会。然而，这些技术也带来了新的挑战，我们必须努力确保它们的安全性和有效性。英国药品和保健品监管局与英国国家健康与护理卓越研究所（National Institute for Health and Care Excellence）在惠康公司的资助下建立了合作关系，共同探讨数字心理健康技术的监管和评估问题。本观点阐述了监管和健康技术评估途径中的一系列关键挑战，旨在促进讨论，以确保监管和评估方法由患者、公众和心理健康领域的专业人士提供信息。我们邀请整个心理健康界的合作伙伴参与、合作并对该项目进行监督，以确保其取得最佳成果。
{"title":"Building robust, proportionate, and timely approaches to regulation and evaluation of digital mental health technologies","authors":"Gareth Hopkin PhD , Richard Branson MA , Paul Campbell FRCA , Holly Coole PgDip , Sophie Cooper BSc , Francesca Edelmann BSc , Grace Gatera BA , Jamie Morgan MA , Mark Salmon MBA","doi":"10.1016/S2589-7500(24)00215-2","DOIUrl":"10.1016/S2589-7500(24)00215-2","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-20DOI: 10.1016/S2589-7500(24)00241-3
David Shaw, Raquel Abad Torreblanca, Zahin Amin-Chowdhury, Adriana Bautista, Desiree Bennett, Karen Broughton, Carlo Casanova, Eun Hwa Choi, Heike Claus, Mary Corcoran, Simon Cottrell, Robert Cunney, Lize Cuypers, Tine Dalby, Heather Davies, Linda de Gouveia, Ala-Eddine Deghmane, Stefanie Desmet, Mirian Domenech, Richard Drew, Mignon du Plessis, Carolina Duarte, Kurt Fuursted, Alyssa Golden, Samanta Cristine Grassi Almeida, Desiree Henares, Birgitta Henriques-Normark, Markus Hilty, Steen Hoffmann, Hilary Humphreys, Susanne Jacobsson, Christopher Johnson, Keith A Jolley, Aníbal Kawabata, Jana Kozakova, Karl G Kristinsson, Pavla Krizova, Alicja Kuch, Shamez Ladhani, Thiên-Trí Lâm, María Eugenia León Ayala, Laura Lindholm, David Litt, Martin C J Maiden, Irene Martin, Delphine Martiny, Wesley Mattheus, Noel D McCarthy, Mary Meehan, Susan Meiring, Paula Mölling, Eva Morfeldt, Julie Morgan, Robert Mulhall, Carmen Muñoz-Almagro, David Murdoch, Martin Musilek, Ludmila Novakova, Shahin Oftadeh, Amaresh Perez-Arguello, Maria Dolores Pérez-Vázquez, Monique Perrin, Benoit Prevost, Maria Roberts, Assaf Rokney, Merav Ron, Olga Marina Sanabria, Kevin J Scott, Julio Sempere, Lotta Siira, Ana Paula Silva de Lemos, Vitali Sintchenko, Anna Skoczyńska, Hans-Christian Slotved, Andrew J Smith, Muhamed-Kheir Taha, Maija Toropainen, Georgina Tzanakaki, Anni Vainio, Mark P G van der Linden, Nina M van Sorge, Emmanuelle Varon, Julio Vazquez Moreno, Sandra Vohrnova, Anne von Gottberg, Jose Yuste, Angela B Brueggemann
Microbiology reference laboratories perform a crucial role within public health systems. This role was especially evident during the COVID-19 pandemic. In this Viewpoint, we emphasise the importance of microbiology reference laboratories and highlight the types of digital data and expertise they provide, which benefit national and international public health. We also highlight the value of surveillance initiatives among collaborative international partners, who work together to share, analyse, and interpret data, and then disseminate their findings in a timely manner. Microbiology reference laboratories have substantial impact at regional, national, and international levels, and sustained support for these laboratories is essential for public health in both pandemic and non-pandemic times.
{"title":"The importance of microbiology reference laboratories and adequate funding for infectious disease surveillance.","authors":"David Shaw, Raquel Abad Torreblanca, Zahin Amin-Chowdhury, Adriana Bautista, Desiree Bennett, Karen Broughton, Carlo Casanova, Eun Hwa Choi, Heike Claus, Mary Corcoran, Simon Cottrell, Robert Cunney, Lize Cuypers, Tine Dalby, Heather Davies, Linda de Gouveia, Ala-Eddine Deghmane, Stefanie Desmet, Mirian Domenech, Richard Drew, Mignon du Plessis, Carolina Duarte, Kurt Fuursted, Alyssa Golden, Samanta Cristine Grassi Almeida, Desiree Henares, Birgitta Henriques-Normark, Markus Hilty, Steen Hoffmann, Hilary Humphreys, Susanne Jacobsson, Christopher Johnson, Keith A Jolley, Aníbal Kawabata, Jana Kozakova, Karl G Kristinsson, Pavla Krizova, Alicja Kuch, Shamez Ladhani, Thiên-Trí Lâm, María Eugenia León Ayala, Laura Lindholm, David Litt, Martin C J Maiden, Irene Martin, Delphine Martiny, Wesley Mattheus, Noel D McCarthy, Mary Meehan, Susan Meiring, Paula Mölling, Eva Morfeldt, Julie Morgan, Robert Mulhall, Carmen Muñoz-Almagro, David Murdoch, Martin Musilek, Ludmila Novakova, Shahin Oftadeh, Amaresh Perez-Arguello, Maria Dolores Pérez-Vázquez, Monique Perrin, Benoit Prevost, Maria Roberts, Assaf Rokney, Merav Ron, Olga Marina Sanabria, Kevin J Scott, Julio Sempere, Lotta Siira, Ana Paula Silva de Lemos, Vitali Sintchenko, Anna Skoczyńska, Hans-Christian Slotved, Andrew J Smith, Muhamed-Kheir Taha, Maija Toropainen, Georgina Tzanakaki, Anni Vainio, Mark P G van der Linden, Nina M van Sorge, Emmanuelle Varon, Julio Vazquez Moreno, Sandra Vohrnova, Anne von Gottberg, Jose Yuste, Angela B Brueggemann","doi":"10.1016/S2589-7500(24)00241-3","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00241-3","url":null,"abstract":"<p><p>Microbiology reference laboratories perform a crucial role within public health systems. This role was especially evident during the COVID-19 pandemic. In this Viewpoint, we emphasise the importance of microbiology reference laboratories and highlight the types of digital data and expertise they provide, which benefit national and international public health. We also highlight the value of surveillance initiatives among collaborative international partners, who work together to share, analyse, and interpret data, and then disseminate their findings in a timely manner. Microbiology reference laboratories have substantial impact at regional, national, and international levels, and sustained support for these laboratories is essential for public health in both pandemic and non-pandemic times.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":""},"PeriodicalIF":23.8,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873161","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}
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