Davide Ferrari, PhD(c)的动态

Considering data is the lifeblood of AI, here's an interesting look at establishing a complete and accurate data set to train a large language model (LLM) for AI-driven healthcare delivery applications. This paper also conveys the critical message of using AI/ML to support and not replace clinicians during patient care encounters. #AI #ML #LLM #artificialintelligence #generativeai #machinelearning #largelanguagemodel #healthcare #telehealth #healthcaredata #IEEE #responsibleai #telemedicine #datascience

Are you a clinician or researcher in the field of #healthcare? Do you want to learn more about how machine and federated learning are improving your industry? Sign up to this CERN webinar in collaboration with EATRIS: From Data to Diagnosis: How Federated Learning Protects Patient Privacy While Shaping the Future of Healthcare! ?? https://shorturl.at/nsdAg ??Preliminary agenda: ?? Multidisciplinary collaboration as enablers of digitalisation and AI – Sara Zullino, scientific & SME outreach manager at EATRIS ??The challenge in #stroke treatment optimisation – Carlos Molina, director of the Stroke Unit and Brain Hemodynamics at Vall d’Hebron Research Hospital and TRUSTroke coordinator ??CAFEIN: CERN’s federated learning solution for the privacy-preserving training of AI algorithms – Luigi Serio, principal scientist at CERN ??Industry perspective: current efforts and bridging the gap: how artificial intelligence can be used for better stroke patient outcomes – Hanno Herrmann, Siemens Healthineers ??A roundtable discussion hosted by Anton Ussi, operations and finance director at EATRIS #machinelearning #data #innovation #medtech

Attending the https://lnkd.in/g8GaejnX conference these last 2 days. As an AI curious, but not yet AI savvy individual working in medical research and education, here are a few small takeaways. #StanfordAIHealth Thanks to Stanford and the amazing speakers 1) We are further than I thought we were (in some areas) - generative AI exploding, and MANY very smart people working in this AI Health space 2) We are not as far along as I thought we were (in other areas) - policy, regulation, consistency, validation, etc. 3) Terms in AI can be amusing like hallucination and flops 4) Many references to AI passing USMLE exams, which is now an antiquated example of AI success compared to other capabilities 5) AI does a better job at being trusted and empathetic when answering patient questions on Reddit than clinicians, but do you really want to get your medical advice on Reddit? 6) If you had a dollar for each use of the words "model" "GPT" or "multimodal" during an AI conference, you'd retire tomorrow. 7) AI as a clinician co-pilot can elevate all providers and accelerate better, safer, evidence-based, and consistent healthcare and ... 8) The future is bright, as long as privacy, safety, and equity are all ensured and prioritized

?? Sharing our latest preprint: "Development & Evaluation of a Digital Scribe: Conversation Summarization Pipeline for Emergency Department Counseling Sessions to Reduce Documentation Burden." at Intelligent Futures Research Lab @ Nationwide Children's Hospital. ?? This study is a step forward in exploring the alignment of health services, healthcare providers and AI, while addressing documentation burden. We evaluated pre-trained language models' performance to automate clinical conversation documentation using medical records and clinic conversations dataset. This is a crucial aspect of healthcare that often demands a significant amount of time and resources from medical professionals. ?? The BART-Large-CNN model showed better performance in understanding and summarizing clinical conversations, effectively recognizing both the content and context. However, we observed variability in model accuracy, which underscores the need for further refinement. Stay tuned for more as we advance our work, aiming to further examine AI-assisted conversation documentation in clinical practice. ?? Link: https://lnkd.in/gQHGidiJ #DigitalScribe #TextSummarization #DocumentationBurden #AIForGood #HealthcareFuture

Google has launched the Articulate Medical Intelligence Explorer (AMIE), a chatbot designed to engage in diagnostic conversations with patients, exhibiting reasoning akin to human doctors. Google Research leaders Alan Karthikesalingam and Vivek Natarajan emphasized the significance of AI in improving healthcare accessibility and quality through enhanced physician-patient communication. AMIE underwent training on real-world medical datasets, employing a self-play-based simulated diagnostic dialogue environment for development. In evaluations with simulated patients, AMIE demonstrated diagnostic conversations comparable to board-certified primary care physicians, outperforming in accuracy and various consultation aspects. While promising, the researchers acknowledged limitations and called for further research before real-world implementation.

?? Excited to Share My Latest Project: AI-Driven Medical Diagnosis Systems! ?? I'm thrilled to announce the successful completion of a groundbreaking project where I utilized machine learning to develop predictive models for several critical medical conditions under CodeClause . This initiative focused on: Diabetes Prediction ?? Heart Disease Prediction ?? Parkinson’s Disease Prediction ?? Using advanced machine learning techniques, I created models that analyze medical data to provide accurate predictions. These systems are designed to assist healthcare professionals in early diagnosis and personalized treatment planning, ultimately aiming to enhance patient outcomes. This project was a significant step forward in applying AI to healthcare, combining my passion for technology with the goal of making a tangible impact on patient care. A big thank you to CodeClause for the incredible opportunity and support! I’m eager to connect with others interested in AI in healthcare or potential collaborations. Github link: https://lnkd.in/gtPM5TSd streamlit link: https://lnkd.in/gNy6Azww #MachineLearning #AI #MedicalDiagnosis #HealthcareInnovation #DataScience #CodeClause

Next Speaker of the 3rd Annual ACDM Symposium on Artificial Intelligence (AI) in Clinical Trials is announced as....... The?ACDM: Association for Clinical Data Management?is delighted to announce that?Gail Kniveton of Pop Science will present the session “Evolving Job Roles in Clinical Trials: Navigating the New Frontier of Clinical Data Management....or should we call it Clinical Data Science?” at the AI Symposium. AI and ML technologies in CDM are reshaping job roles within the industry. To thrive in this new era, professionals must acquire skills related to AI and ML model integration, data security, and ethical data handling. Embracing innovative technologies & staying adaptable and continuously updating their skill sets, clinical data managers can harness the full potential of these advancements to drive innovation and improve healthcare outcomes in the clinical trial space. The AI Symposium will take place from 24-25 June 2024 in Basel, Switzerland. Book your place today and be a part of the story in the adoption of AI in CDM: https://lnkd.in/ef-cQ9SN ACDM Board:?Robert King, Sverre Bengtsson, Jo Marshall, Nina Reyes, Eva Alder, Richard Davies, Nicola G?tz, Anita Kratchmarov, Ashley Howard, Amelie Spieser AI Symposium Steering Group: Richard Davies, Ashley Howard, Marcus Schwabedissen, Tim Armitage, Rob Nichols #clinicalresearch?#clinicaltrials?#clinicaldatamanagement?#clinicaloperations?#edc?#ACDM #AI #artificialintelligence

Book Title: Transformative Technologies in Healthcare: A Deep Dive into AI and Machine learning Publisher: Cambridge Scholar Publishing Email: [email protected] 2. Foundations of Healthcare Data: Challenges and Opportunities 4. Machine Learning Algorithms for Disease Diagnosis 5. Predictive Analytics in Personalized Medicine 9. Robotics and Automation in Healthcare: From Surgery to Patient Care 10. Remote Patient Monitoring: Enabling Continuous Healthcare 11. Big Data Analytics for Population Health Management 13. Telemedicine and AI: Revolutionizing Healthcare Delivery 15. The Role of Explainable AI in Healthcare Decision Support 16. Human-AI Collaboration in Healthcare: A Future Perspective 17. Regulatory Challenges and Frameworks for AI in Healthcare 18. AI and Machine Learning in Public Health Surveillance 20. Future Trends and Challenges in AI-driven Healthcare If anyone is interested, Please submit chapter from above mentioned topics to the given mail id.