MedGraphRAG: A new graph-based tool for medical AI

More access to AI learning and processing tools that recognize complex relationships and patterns between data— and can even process data in visualized imagery — will transform patient care. AI usage in this in the healthcare industry is in an “elementary phase” due to moral and ethical considerations of data, even though it could be helpful to skilled physicians. Why? According to this article, “improved diagnostic accuracy, a reduction in physician burnout, and an enhanced treatment modality.” Further to this, being well prepared for the future of modern medicine depends on physicians embracing AI into medical education. Read more in the article, "Artificial Intelligence in Healthcare: Perception and Reality." Akinrinmade et al., 2023. Artificial Intelligence in Healthcare: Perception and Reality. NIH National Library of Medicine. https://lnkd.in/gdHn8gDj https://lnkd.in/gY78xTRT

Probabilistic medical predictions of large language models - npj Digital Medicine Large Language Models (LLMs) show promise in healthcare, yet struggle with reliable probabilistic predictions essential for clinical decision-making. A recent study reveals that explicit probabilities generated by LLMs often underperform compared to implicit probabilities derived from token likelihoods. This discrepancy is particularly notable in smaller models and imbalanced datasets, emphasizing the need for improved probability estimation methods. As LLMs are increasingly integrated into clinical settings, understanding their probabilistic outputs is crucial for ensuring patient safety and effective treatment. This research paves the way for refining LLM applications in healthcare. #HealthcareIT #AIinHealthcare #MachineLearning #DigitalHealth #ClinicalAI #HealthTech #DataScience ai.mediformatica.com #llms #medical #this #prediction #clinical #digital #datasets #largelanguagemodels #medicine #predictions #about #digitalmedicine #digitalhealth #healthit #healthtech #healthcaretechnology @MediFormatica (https://buff.ly/3BOrdl4)

Day 11: AI in Healthcare "Transforming Patient Care and Diagnostics" Welcome to Day 11 of the 30 Days of AI Mastery challenge! Today, we’re looking at how AI is transforming healthcare by enabling more accurate diagnostics, personalized medicine, and improved patient care. From radiology to predictive analytics, AI is revolutionizing the healthcare industry. Key Applications of AI in Healthcare: 1.Medical Imaging Analysis: AI models are trained to analyze medical images like X-rays, MRIs, and CT scans, helping doctors detect diseases such as cancer, pneumonia, and brain disorders. By identifying patterns in large datasets, AI can often detect abnormalities faster and with a high degree of accuracy. 2.Predictive Analytics: Hospitals use AI to analyze patient records, lab results, and historical data to predict disease risk. This is especially useful for chronic conditions like diabetes and heart disease, allowing early intervention and personalized treatment plans. 3.Drug Discovery: AI accelerates the drug discovery process by analyzing large chemical databases and predicting potential drug candidates. AI-driven drug development helps researchers understand molecular interactions and explore new treatments more efficiently, saving both time and cost. 4.Virtual Health Assistants: AI-driven chatbots and virtual assistants support patients by answering common health queries, scheduling appointments, and even reminding them to take medications. These assistants provide patients with 24/7 access to information and can help reduce the workload on healthcare professionals. 5.NLP in Healthcare: Natural Language Processing (NLP) is helping doctors extract critical insights from unstructured data, such as doctors’ notes, patient records, and medical literature. NLP-powered systems can automatically generate summaries and support clinical decision-making. Ethical and Privacy Considerations: With patient data at the core of AI in healthcare, it’s essential to address privacy concerns and ensure compliance with data protection regulations. Ethical issues also arise around AI-assisted decision-making and ensuring algorithms remain free from biases. Learning Resources: - Video Course: https://lnkd.in/gma_FHU5 - Article: “The Role of AI in Healthcare” on Forbes - Research Paper: “Artificial Intelligence in Health Care” by National Academy of Medicine Tomorrow, we’ll explore AI in Finance, covering areas like fraud detection, risk management, and algorithmic trading. Stay tuned! 🚀 #ArtificialIntelligence #HealthcareAI #MachineLearning #AIInMedicine #AIChallenge #30DaysOfAI #LearningAI

#Artificialintelligence (#AI) algorithms hold the potential to improve not only image analysis but also other #medical fields. However, even in medical AI‘s supreme discipline “a significant portion of the published literature lacks transparency and #reproducibility, which hampers sustained progress toward #clinical translation[2].” This year, "End-to-end reproducible AI pipelines in radiology using the cloud" joined a series of promising publications that give hope that medical AI will solve its #replicabilitycrisis. ✅#My2_cents Some say, we are at “a critical moment in machine learning in medicine[3]” - a field where   1️⃣“failure of... models may have severe implications for patients’ #health, 2️⃣the high requirements for accuracy, robustness, and interpretability confront ML researchers with a unique set of challenges[3-5].” Not only managers like me but also scientists are enthusiastically imagining ways in which AI tools might improve business as well as research. ➡️“Artificial intelligence and illusions of understanding in scientific research” came with a taxonomy of scientists’ visions. The paper observed that “their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions...[6-8].” ➡️ Platforms could become a crucial building block. For example, autonomous, cloud-based laboratories (CBLs) could not only democratize access to state-of-the-art scientific instruments, but also address the replication crisis[9]. In January, “Robustness and reproducibility for AI learning in biomedical sciences…” introduced a focus on the learning aspect, presenting “RENOIR (REpeated random sampliNg fOr machIne leaRning), a modular open-source platform for robust and reproducible machine learning (ML) analysis. RENOIR adopts standardised pipelines for model training and testing, introducing elements of novelty, such as the dependence of the performance of the algorithm on the sample size. Additionally, RENOIR offers automated generation of transparent and usable reports…[10]”. ➡️“It's not all doom and gloom for AI and reproducibility, and the technology itself could hold the solution for some of its own challenges[11].” Several other publications also propagated platforms to establish 🔢Hosting services to share code, data, and ML model parameter settings 🔢Virtualization to reproduce the environment and setup of any ML experiment 🔢Managing sources of randomness e.g., via random number seeds, deterministic algorithms, or other methods…[12] 🔢Richness of data with sufficient diversity in the training dataset[13]   Last but not least I want to recommend a humble reading: "Avoiding common machine learning pitfalls[14]." ➡️In this sense, 2024 could herald the end of medical AI's replicability crisis.   🆔References check my 1st comment   20241229—10945

AI in Clinical Research: Transforming Healthcare with Cutting-Edge Technology Artificial Intelligence (AI) is revolutionizing clinical research, making healthcare smarter, faster, and more efficient. By harnessing the power of AI, we are unveiling new insights, speeding up drug discovery, improving patient care, and transforming medical practices. Here’s how AI is reshaping the future of healthcare: Data-Driven Insights: Unveiling Patterns and Predictions: AI is analyzing massive amounts of healthcare data to uncover patterns and make accurate predictions, helping doctors make informed decisions faster. Accelerating Drug Discovery and Development: AI is playing a critical role in identifying new drug compounds, optimizing drug design, and predicting drug interactions, reducing the time it takes to bring new medicines to market. Streamlining Clinical Trials and Enhancing Efficiency: AI is helping design smarter clinical trials, improving patient recruitment, monitoring trials in real-time, and ensuring better overall trial outcomes. Revolutionizing Medical Imaging with AI: AI is enabling healthcare providers to analyze medical images more accurately and faster, assisting in early diagnosis and improving the quality of care. Personalized Medicine: Tailoring Treatment to the Individual: By analyzing genetic and medical data, AI is helping healthcare professionals personalize treatment plans, ensuring better outcomes for patients. Clinical Decision Support: Empowering Healthcare Professionals: AI-driven clinical decision support systems are giving healthcare professionals real-time recommendations based on the latest research, guidelines, and patient data. Unleashing the Power of Natural Language Processing (NLP): AI is using NLP to interpret and process clinical notes, medical records, and other text data, making it easier for healthcare providers to access key patient information. Automating Administrative Tasks for Enhanced Efficiency: AI is automating time-consuming administrative tasks, freeing up healthcare professionals to focus on patient care and improving overall workflow. AI in clinical research is paving the way for faster treatments, more personalized care, and a smarter healthcare system. It’s an exciting time to be part of this transformative journey! #AI #ClinicalResearch #HealthcareInnovation #DataScience #DrugDiscovery #ClinicalTrials #MedicalImaging #PersonalizedMedicine #ClinicalDecisionSupport #NaturalLanguageProcessing #AIinHealthcare #SAS #CDM #Pharma #FDA #RegulatoryAuthorities #HealthTech #ClinicalSAS #DigitalHealth #Pharmaceuticals #NLP #Automation #ArtificialIntelligence

We are happy to announce a new feature offered by the Medical Chatbot: Literature Review, specifically tailored to streamline the complex process of medical literature research and review. This tool enables researchers, healthcare professionals or clinicians to efficiently sift through vast amounts of published research to support in-depth meta-analyses, clinical decision-making, or evidence-based practices. The literature review is a critical and multifaceted process that involves searching, reading, analyzing, and synthesizing scholarly materials on a given topic to identify existing knowledge in a particular research area. This process helps map out what is already known and allows for the critical evaluation of prior studies to uncover gaps, inconsistencies, or emerging trends. User Benefits In the medical domain, this tool has been designed to make the following impacts: 1. Time and Effort Savings: The Literature Review tool dramatically speeds up the review process, cutting down the time and effort traditionally required for comprehensive literature analysis. 2. Specificity for Meta-Analysis: It enhances the precision and robustness of meta-analyses by allowing users to specifically filter studies to match detailed research criteria. 3. Automated and Intelligent Data Point Extraction: This feature automates the extraction of relevant data from literature, reducing manual effort and minimizing human error. 4. Improved Clinical Decision-Making: Provides timely and precise access to research, supporting better-informed clinical decisions grounded in quality, peer-reviewed studies. More information: https://lnkd.in/g5_djfv8 MEdical Chatbot: https://lnkd.in/dT9fTJCv #MedicalChatbot #HealthcareAI #LiteratureReview #AIInHealthcare #MedicalResearch #NLP #GenerativeAI #JohnSnowLabs #DigitalHealth #ClinicalDecisionMaking #EvidenceBasedMedicine #MedicalInnovation #HealthTech

𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐌𝐞𝐝𝐢𝐜𝐢𝐧𝐞: 𝐂𝐚𝐧 𝐀𝐈 𝐃𝐨𝐜𝐭𝐨𝐫𝐬 𝐁𝐞𝐜𝐨𝐦𝐞 𝐚 𝐑𝐞𝐚𝐥𝐢𝐭𝐲? Exciting news in the field of medicine and AI! A recent study explores the capabilities of OpenAI's o1 model in the medical field, and the results are promising. The study found that o1's enhanced reasoning ability and multilinguality make it a strong contender for medical applications. In fact, it outperforms the previous GPT-4 model by an average of 6.2% and 6.6% across various medical datasets. But what does this mean for the future of medicine? Could AI doctors become a reality? While we're not quite there yet, this study suggests that we're taking significant steps towards making AI-assisted medicine a possibility. However, the study also highlights the need for further research and development to address limitations such as hallucination, inconsistent multilingual ability, and discrepant metrics for evaluation. 🔗 https://lnkd.in/ev6TQdew What are your thoughts on the future of AI in medicine? Share your insights and let's continue the conversation! #AIinMedicine #MedicalInnovation #FutureOfMedicine #ArtificialIntelligence #HealthcareTechnology

The Role of Artificial Intelligence in Improving Disease Diagnosis: A Revolution in Healthcare Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, particularly computer systems. In the healthcare sector, AI encompasses a multitude of technologies designed to improve patient outcomes, optimize clinical workflows, and enhance the overall efficiency of healthcare delivery. The relevance of AI in healthcare has become increasingly pronounced as medical professionals and technology developers collaborate to apply these advanced tools in disease diagnosis... Find out more on: https://lnkd.in/d9rpFykp #StudyTime #SmartLearning #QuckLearn #DailyLearning #LearnEveryday #LearningMadeEasy #StudyShorts #EduShorts #LearnWithMe #LearningIsFun #BrainTeasers #OnlineClasses #OnlineLearning #Tech #Technology #TechReview #Gadgets #TechNews #TechTips #TechTalk #TechTrends #TechVideos #Innovation #TechCommunity #TechLife #FutureTech #TechReviews #GadgetReview #ai #deeplearning

Mendel.ai, a leader in clinical AI, unveils groundbreaking advancements in its Neuro-Symbolic AI system, revolutionizing patient cohort identification. By combining large language models with a hypergraph reasoning engine, Mendel's AI surpasses GPT-4 in key benchmarks, achieving remarkable results in Automatic Cohort Retrieval (ACR). Traditional methods are outpaced by this innovative approach, offering faster, more accurate cohort identification crucial for clinical research and patient care. The introduction of structured and temporal reasoning types sets a new standard, with the Hypercube system consistently outperforming LLM-based models, showcasing a significant F1 score increase to 62.9. This milestone promises accelerated and precise patient cohort identification, improved treatment personalization, and enhanced efficiency in retrospective studies. Mendel's commitment to advancing AI in healthcare shines through, marking a transformative leap towards more robust and scalable clinical reasoning for better patient outcomes and streamlined research processes. Learn more about this cutting-edge development: https://lnkd.in/dGNH7NSH Karim Galil, M.D. #AI #Healthcare #ClinicalResearch

Despite AI advancements, human oversight remains essential: State-of-the-art artificial intelligence systems known as large language models (LLMs) are poor medical coders, according to researchers. Their study emphasizes the necessity for refinement and validation of these technologies before considering clinical implementation. The study extracted a list of more than 27,000 unique diagnosis and procedure codes from 12 months of routine care in the Mount Sinai Health System, while excluding identifiable patient data. Using the description for each code, the researchers prompted models from OpenAI, Google, and Meta to output the most accurate medical codes. The generated codes were compared with the original codes and errors were analyzed for any patterns. #ScienceDaily #Technology