Open access @NaturePortfolio journal for the #DigitalMedicine community, publishing research, reviews & comments. Tag us & tweet your work with @npjDigitalMed.
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There are over 7000 rare diseases. Due to clinicians’ limited experience with such diseases and the heterogeneity of clinical presentations, 70% of individuals remain undiagnosed.
Can deep learning help close the gap?
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This study develops a generative clinical LLM using 277 billion words of text and up to 20 billion parameters. The model improves biomedical natural language processing, generates synthetic clinical text, and passed Turing test in writing clinical notes.
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This study developed an open-source tool using a LLM to extract important medical information from clinical text, focusing on decompensated #LiverCirrhosis.
The tool identified liver cirrhosis from free text with 100% sensitivity and 96% specificity, and showed strong results for other symptoms inc. ascites & abdominal pain, demonstrating its potential for efficiently analyzing clinical data.
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Foundation models (FMs) such as #ChatGPT have the potential to revolutionize healthcare. But what's hype and what's real?
This review from a team in @StanfordMed includes 84 clinical FMs + proposes an evaluation framework better suited to assess value.
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In this perspective, @Berci & @EricTopol discuss the regulation of GPT-4 & generative #ArtificialIntelligence in medicine... balancing the exciting + transformative potential, but ensuring safety, maintaining ethical standards, & protecting privacy.
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It is important to ensure #digitalhealth does not accelerate #healthdisparities. @SafiyaMD et al. from @nyugrossman present this framework inc. key digital determinants of health, from individual to societal, to understand the impact on health #equity.
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This article introduces ETHOS, an AI tool that uses advanced #MachineLearning to predict future health outcomes based on patient records, without needing labelled data or model tuning. This tool can simulate different treatment options, helping improve patient care & reduce biases in healthcare.
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We are delighted to welcome @girish_nadkarni to the #npjDigitalMed Editorial Team. Girish brings a wealth of #DigitalHealth and #Nephrology expertise to our Editorial Board Member team.
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How are #LLMs in healthcare evaluated by humans?
This article identifies gaps in current methods such as reliability & generalizability. To improve these evaluations, the authors propose the QUEST framework, which focuses on assessing LLMs based on five principles: information quality, reasoning, communication style, safety, & trustworthiness.
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Matching patients to #ClinicalTrials is usually complex and time-consuming, often leaving patients unaware of potential treatment options.
This study presents a custom fine-tuned language model that uses real-world patient data to automate trial matching, showing performance equal to qualified doctors and better than GPT-3.5 in identifying eligible patients.
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Your weekend read editorial: Discussing the importance of shifting the focus towards clinically relevant outcomes, when adopting #ArtificialIntelligence tools, the ecosystem required for AI to succeed in health, & the human aspect of #healthcare.
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To help surgeons learn faster, it's important to give them real-time feedback during surgery, but this is hard to study because there's so much information to analyze. This study by an interdisciplinary team @AjhungMD @AnimaAnandkumar @CedarsSinai @Caltech uses AI to analyze conversations during surgery, identify key teaching topics like how to handle tissues or control bleeding, and show how these topics relate to the surgeon's improvement, ultimately leading to better surgical training. Read more below: nature.com/articles/s41746-0…
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Are large language models (LLMs) safe for use in medicine? In this study led by @OmiyeTofunmi & @RoxanaDaneshjou, the authors found that four different LLMs had outputs that perpetuated false race-based medicine.
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Revealing a vulnerability in large language models (LLMs) used in medicine...
By altering just 1.1% of the model's weights, incorrect medical information can be introduced without affecting performance in other areas.
This finding highlights security concerns, emphasizing the need for strong safeguards and verification processes to ensure #LLMs remain trustworthy and reliable in healthcare settings.
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SurgeryLLM is a large language model developed by @armancohan and team @YaleCompsci @YaleSurgery that can understand and use medical guidelines for surgery. Combining this knowledge with information about a specific patient can help surgeons work more efficiently, make surgery safer, and improve how well patients recover.
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How accurate & ethical are claims of #ArtificialIntelligence outperforming clinicians?
"Constructive ethical guidance that can benefit authors, journal editors, & peer reviewers when reporting and evaluating findings in studies comparing AI to physician performance" is offered in this commentary from @JojannekeDrogt, @JongsmaKr et al. from @UMCUtrecht.
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How to read a paper involving artificial intelligence
@BMJMedicine @Yosra_Mekki
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(1/4) Can AI help surgeons improve their skills?
We are delighted to share a trio of articles published across @NaturePortfolio covering AI-based assessment of surgery skills, authored by @DaniKiyasseh and colleagues.
All articles are fully open access 🔓
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As large language models are introduced into healthcare and clinical decision-making, these systems are at risk of inheriting – and even amplifying – cognitive biases.
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A meta-meta-analysis shows e- & m-Health interventions improve #physicalactivity, sedentary behavior, #diet, bodyweight & #sleep. These scalable interventions offer significant benefits across diverse populations, enhancing public health.
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This study explored how #LLMs handle multiple clinical tasks at once, finding that performance decreases as the number of tasks and notes increases.
High-capacity models showed strong accuracy and efficiency, managing up to 50 tasks with significant cost savings, making them suitable for scaled healthcare applications.
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A comprehensive review of natural language processing over the past 15 years from a team in the UK. NLP budgets from research funding now are 80x that from 2007-10, yet there is still much to do in this field to realise its potential.
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A curated database of @US_FDA-cleared #ArtificialIntelligence products for medical imaging indicates a high prevalence of triage systems, variability in basic output characteristics, and often limited #explainability of AI predictions.
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Of 4 collaboration strategies to deploy #AI for the diagnosis of ARDS from chest XR, the most accurate is to allow AI review the XR first & defer to the physician if uncertain. 79% of cases were decided by AI - significantly reducing physician workload.
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"Data have become the most valuable commodity in health care, but questions remain about whether there will be an #AI 'revolution' or 'evolution' in health".
This perspective article discusses action areas & recommendations to achieve AI's full potential.
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Gestational age estimation has an error of +/- 2 weeks in late #pregnancy. The application of this #machinelearning model using only ultrasound image analysis reduces this error to 3 + 4 days in the 2nd & 3rd trimesters representing a major step forward.
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Generative #ArtificialIntelligence can create synthetic patient data that closely mimics disease biology, treatment response & long-term survival compared to real clinical trial cohorts. Synthetic patient data may augment control groups in trial designs.
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What can we learn from how you type on your phone?
This study shows that it’s possible to track and predict fluctuations in people’s mental health status based on how they type on their phone with nearly 95% accuracy.
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With advancements in data science & #AI, the concept of #DigitalTwin for Health holds immense potential.
This scoping review explores its promising applications, paving the way for a collaborative global effort in healthcare innovation.
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New article! Read Making machine learning matter to clinicians: model actionability in medical decision-making dlvr.it/ShLsh1 in npj Digital Med
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This perspective report, @MassGenBrigham @harvardmed by the Mass General Brigham AI Governance Committee, explores how to responsibly use artificial intelligence (AI) in healthcare, ensuring it’s fair, safe, and beneficial. It shares practical guidelines and a real-life example of using AI for medical documentation, showing how to handle challenges and improve patient care. Read here: nature.com/articles/s41746-0…
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Introducing @drjessilyn, Jessilyn joined us as an Associate Editor in March 2021 and is one of our longest-serving Associate Editors. Jessilyn is highly experienced in assessing manuscripts focused on digital biomarkers and machine learning.
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New article! Read Self-supervised learning for human activity recognition using 700,000 person-days of wearable data dlvr.it/T5PS9S in npj Digital Med
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This perspective from @JohnTorousMD, @arieldora + @ftbourgeois gives a useful outline of the structure + challenges in the SaMD regulatory landscape with @US_FDA incl. examples. Encouraging work is happening, but plenty more to be done... #DigitalHealth
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This Perspective, written by one of our previous Editors-in-Chief, @EricTopol, and @Berci continues to attract readers and was our 🏆 top @altmetric article for July 2023.
A Perspective in @npjDigitalMed argues that regulatory oversight should assure medical professionals and patients can use large language models and AI without causing harm or compromising their data or privacy and offers recommendations to regulators. go.nature.com/3Y0h63t
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🏆Most cited paper of 2022 is this review on #MachineLearning for medical imaging, including its methodological failures and recommendations for the future.
@GaelVaroquaux @DrVeronikaCH
go.nature.com/470KQRL
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Smartphones & #wearables transform health monitoring. However, bias in digital health studies and algorithms arises from underrepresented datasets.
This @DukeU team explore device ownership, reasons for use, & willingness to share data for research.
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New this week in @Nature: A new #AI tool designs #mRNAvaccines that have greater shelf stability and trigger a larger antibody response in mice than conventionally designed vaccines.
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Large language models show promise for identifying social determinants of health in clinic notes, with smaller, fine-tuned models showing more robust performance & less potential bias than ChatGPT-family models.
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Using a phone app to monitor wounds for infection after surgery... In this multi-centre pilot, 200 patients felt the app improved almost every aspect of their wound care after surgery including usefulness, ease of use, reliability of tech, & satisfaction.
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"It’s déjà vu all over again"
This thought provoking commentary considers the question of whether we can 'learn from the failures of the EHR to guide the implementation of #artificalintelligence in medicine, or is history destined to repeat itself?'
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Digital twins are a model or blueprint that simulates a physical-world object or system. They aim to be a virtual replica of the dynamics of the real-world entity
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This new study found that the commercial version of ChatGPT is an unreliable clinical calculator, providing correct answers in only two-thirds of trials across a diverse group of tasks.
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A new paper @AllofUsResearch used data from wearable devices to track physical activity in a large group of people across the United States. The study reinforces that differences in activity levels are linked to factors like gender, age, where people live, and how easy it is to walk in their neighborhoods, and these differences are connected to obesity rates and how walkable people feel their areas are.
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We would like to welcome Prof. @girish_nadkarni from @IcahnMountSinai to our Associate Editor team. Girish is specialized in AI and emerging technologies in medicine.
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Our June Reviewer of the Month is @kdpsinghlab! On peer review, Karandeep comments: "With all the rapid innovations happening right now in the space of AI & large language models, it's more important than ever to separate hype from reality."
go.nature.com/45GBPwF
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A single approach that can recognise walking with a variety of body-worn devices regardless of walking style, sensor location and configuration is presented in this paper to estimate walking time, cadence and step count.
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Large language models can rapidly phenotype patients with no manual annotation. This study reports on the evaluation of a publicly available LLM for zero-shot phenotyping using clinical notes with a proof-of-concept in postpartum hemorrhage.
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Generative AI models showed comparable diagnostic performance to non-expert physicians in a variety of specialties. nature.com/articles/s41746-0…
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Can synthetic data improve AI models?
In this paper, a generative model for synthesizing mixed-type timeseries health records, including physiological signals, lab results, & medication uses, improved the downstream applications of AI in critical care.
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This team from @cerebral built a #MachineLearning system to identify chat messages from patients experiencing #mentalhealth crises.
When deployed in a national telehealth network it reduced response times to patients in crisis from 10 hours to <10 mins.
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Natural language processing (#NLP) can accurately detect common side effects from anticancer drugs in clinical records, reflecting known side effect rates.
Using records from >44k cancer patients, this study used NLP to identify higher risks of side effects like neuropathy, mouth sores, taste issues, and appetite loss in patients on specific anticancer drugs compared to those not on treatment.
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Our latest News & Views piece discusses the recent awarding of the 2024 @NobelPrize for Physics & Chemistry both being in the #ArtificialIntelligence field.
Here @ben_li123 & @Stephen44026116 discuss the implications for science, medicine, society & ethics.
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Introducing @JohnTorousMD, John joined us as an Associate Editor in November 2021. John is highly experienced in assessing manuscripts focused on digital psychiatry and mental health.
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This study developed and evaluated an autonomous clinical artificial intelligence agent leveraging GPT-4 and multimodal precision oncology tools to support personalized cancer decision-making
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We have officially opened our Editorial Fellowship applications for 2025-2026! The deadline is June 30, so apply now!
More information here: nature.com/npjdigitalmed/edi…
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Using #DeepLearning to help novice nurses with no prior #Ultrasound experience screen for abdominal aortic aneurysms (AAA).
The results from this study show that nurses guided by this model achieve scan quality nearly as good as physicians, with high accuracy in detecting AAA, suggesting the potential for broader, more accessible screening.
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This study found that AI-generated clinical reviews often have fewer references, less comprehensive insights, and lower logical consistency than human reviews.
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Can a novel #DeepLearning algorithm to predict #sepsis in the emergency department improve care?
Researchers from @UCSDHealth show proper implementation of the model can save lives of sepsis patients and improve quality metrics.
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This study investigated the capacity of five publicly available large language models (ChatGPT 4o mini, Claude 3.5 Sonnet, Copilot, Llama 3, and Gemini 1.5 Flash) to respond to medical ethics scenarios
ai.nejm.org/doi/full/10.1056…
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Mitigating the 'black box' of AI: LLMs can imitate diagnostic reasoning strategies when solving clinical cases & provide an interpretable means to assess if the generated answer is true/false based on the diagnostic reasoning's factual & logical accuracy.
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This perspective proposes a framework to improve computational phenotyping by enhancing accessibility & usefulness. The authors present an example focussing on easy adoption, cross-task reusability, and clinical phenotyping algorithm development support
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The value of medical AI lies in human interaction...
However, formal conceptual frameworks for assessing the quality of these human-AI interactions from a user perspective are lacking.
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📢 Open for submissions, our new collection, Clinical Applications of AI in Mental Health Care. This collection is in collaboration with @npjMentalHealth & is Guest Edited by Eric Kuhn, @MauriceMulvenna, Raymond Bond, & @JohnTorousMD.
go.nature.com/433f43k
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We are thrilled to welcome Professor @adamdunn from @Sydney_Uni to our Associate Editor team! Adam brings a wealth of public health & clinical informatics expertise, with a particular interest in online misinformation & health behaviors.
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Scaling AI for surgical guidance to operating rooms globally remains a major challenge, especially resource-limited settings. These authors describe a novel equipment-agnostic #MachineLearning pipeline to enable real-time deployment to any edge device.
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Incorporating #AI education into medical training is essential for equipping future healthcare professionals.
This study developed an expert-approved AI curriculum for Canadian medical students, emphasizing ethics, law, application, and collaboration, with 82 essential competencies identified to enhance AI literacy and integration in healthcare.
#MedicalCollege
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Germany's DiGA program was the first to implement #DigitalTherapeutics on a large scale. Three years later, increased usage and accumulated experience have prompted regulatory changes that expand the program's scope.
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This #InternationalWomensDay, we celebrate the brilliant women driving innovation in digital medicine! At every stage, their contributions shape digital health and the journal at @npjdigitalmed @Nature. 🚀💡 #WomenInSTEM #IWD
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A novel wearable-sensor dataset of healthcare workers from @sujnagaraj et al. reveals high levels of inter- & intra-individual heterogeneity in stress - an important step towards characterizing #stress in individuals.
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This umbrella review summarizes 48 #systematicreviews on effectiveness of app-based health interventions within patient populations published from 2013-23.
Find out how skewed the evidence is towards some indications & where quality remains suboptimal:
nature.com/articles/s41746-0…
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New article! Read A medical multimodal large language model for future pandemics dlvr.it/Szf1V9 in npj Digital Med
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Large language models in oncology: a review
LLMs can serve as powerful tools to augment clinical expertise and patient-centred care
bmjoncology.bmj.com/content/…
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Clinical evidence synthesis largely relies on systematic reviews of clinical studies from medical literature. This study proposes a generative AI pipeline to streamline study search, study screening, and data extraction tasks.
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Claims about how #AI will increase efficiency are prominent but flawed, because they are based on studies of accuracy.
Accuracy ≠ efficiency...
We must remain conscious that an accurate model does not guarantee efficiency gains & workload reduction.
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Electronic Health Record (EHR) data in pediatrics often lack detail, making data analysis challenging.
This article introduces a transfer learning approach to enhance pediatric EHR analysis by creating specialized code embeddings that combine data from both pediatric and general patient records. This method effectively improves patient profiling and aids research, particularly for conditions like pulmonary hypertension.
nature.com/articles/s41746-0…
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A #deeplearning model, the Stanford Estimator of Electrocardiogram Risk, predicts long-term risk of #cardiovascular mortality and disease from a single ECG with performance better than current standard risk screening tools.
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Using datasets of EHR + #echofirst, @David_Ouyang et al. found confounders mediate AI prediction of demographics. Unaccounted confounders provide a substantial amount of the information in “predicting race” and performance drops when balancing covariates.
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Holiday read recommendation🎄
AI in healthcare needs tons of patient data to work, and there's debate about whether patients should automatically be included (opt-out) or have to actively consent (opt-in). Researchers @harvardmed believe patient choice, control over their data, and privacy should be at the heart of how this data is collected.
Read here: nature.com/articles/s41746-0…
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Are you a student or trainee interested in science communication? Apply for our communications fellowship! You will assist with journal outreach activities including developing an exciting new podcast on digital health.
Applications due July 4th.
nature.com/npjdigitalmed/com…
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Breaking news 💥 Our Guest Editor Raymond Bond announces the launch of our new collection call for papers, Clinical applications of AI in mental health care. This is a collaboration with new launch @Nature_NPJ journal @npjMentalHealth.
nature.com/collections/ifgbd…
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Advancing EHR-based deep phenotyping... An algorithm for mapping clinical Observational Medical Outcomes Partnership (OMOP) vocabularies to @OBOFoundry ontologies to help systematically identify undiagnosed patients who might benefit from genetic testing.
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The U.S. is the first country in the world to propose legislation that would allow AI to autonomously prescribe medications.
@TinglongDai
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New editorial from our EiC @jkvedar and colleagues at @harvardmed. This piece focuses on promoting #RacialEquity in #DigitalHealth and suggests applying a framework to 3 areas within digital medicine
1⃣ #AI applications
2⃣ #Wearables
3⃣ #Telehealth
go.nature.com/3ILnEgA
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Our most recent News & Views article examines the ethical tension between "selective deployment," where AI is used only for subgroups it performs well in, and "equitable deployment," which aims for fair performance across all groups.
This article emphasizes the need for better evaluation metrics to ensure AI models truly benefit all patients without reinforcing biases.
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New article! Read Navigating the European Union Artificial Intelligence Act for Healthcare dlvr.it/TBpgMn in npj Digital Med
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Artificial intelligence for diagnosing psychological & neuro disorders is frequently studied in labs but rarely implemented clinically. This review outlines the biological, technical & institutional barriers to making diagnostic AI a reality in hospitals.
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This study tested an AI model to help clinicians recognize critical #Anatomy near pituitary tumors during surgery, comparing accuracy with & without AI assistance.
Results showed AI improved anatomy identification accuracy, especially for less experienced participants, suggesting potential for AI to support surgical training and in-surgery guidance.
nature.com/articles/s41746-0…
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Deep learning of ECGs can help identify patients with hidden #atrialfibrillation better than predictions based on clinical risk factors & echo measurements.
This may offer additional opportunities to guide patient screening & support early intervention.
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Using AI to analyze coronary artery calcium scans improves the prediction of various cardiovascular events, not just coronary heart disease.
By assessing plaque characteristics & cardiac chamber size, the AI-based approach in this study significantly outperformed the traditional Agatston Score in predicting heart disease, heart failure, atrial fibrillation, and stroke over a 15-year period.
#cardiovasculardisease #CardioTwitter
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Health data justice: "the study and use of health-related data in ways that aim to redress the exclusions of structurally marginalized communities from systems of health care and public health…" An interesting piece from @jayshaw29 & @sharifasekalala.
nature.com/articles/s41746-0…
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Surgeons with different experience levels use different surgical gestures in robotic-assisted surgery. These gestures were predictive of 1-year postop function, suggesting gestures could objectively measure surgical performance + outcomes.
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Wearable devices show promise for improving remote monitoring of heart failure patients by tracking important physiological measurements. However, their use in real-world healthcare faces challenges because they haven't been thoroughly tested in clinical settings. This review @ErasmusMC looked at existing research and assessed how ready these wearables are for medical use, using a tool called the Medical Device Readiness Level (MDRL).
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Studies of #AI in medicine may overestimate accuracy to new situations by 20%.
Models learn hidden biases in X-rays, ECGs, clinical notes etc. instead of real disease markers. This study proposes a solution for inc. reliability in reporting of results.
nature.com/articles/s41746-0…
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New article! Read Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications in npj Digital Med dlvr.it/SpkH79
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Exploring evaluation metrics for health #Chatbots. What is the current norm? What are their weaknesses?
This perspective also introduces a set of user-centered evaluation metrics, grouped into accuracy, trustworthiness, empathy, & computing performance
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An #RCT led by @JRGolbus & @bnallamo from @umichCVC with 220 participants showed that contextually-tailored text messages from wearable devices may improve physical activity for some cardiac rehab participants but displayed no sig. difference at 6 months.
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Using computer vision to transform smartphone videos into highly accurate tools for tremor analysis to rival gold-standard equipment...
These digital biomarkers provide added value for assessing DBS outcomes in patients with essential tremor.
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A digital twin in surgery is a dynamic virtual replica of an individual’s physical and physiological state, integrating both bodily systems and healthcare interactions.
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