Ambit Inc.’s Post

Excited to share my completion of the Healthcare Artificial Intelligence course! 💼 Mastered the art of crafting formidable AI models for real-world healthcare applications, armed with Data Science, Machine Learning, and Deep Learning. From tackling DNA classification to diagnosing coronary artery disease and predicting diabetes, I'm eager to contribute to healthcare innovation. Plus, I've also aced the AI - Artificial Intelligence Intro in Healthcare, Plain & Simple course, solidifying my expertise in the field. Let's make a tangible impact together! Dive into my journey on GitHub: https://bit.ly/3V2tjFc #HealthTech #AIInHealthcare #DataScience #deeplearning #machinelearningalgorithms

"Did you know that machine learning algorithms can now predict heart disease with astonishing accuracy? The incredible advancements in AI and data science continue to revolutionize healthcare. Let's continue to strive for groundbreaking innovations in this field! #DataScience #MachineLearning #ArtificialIntelligence #Innovation #HealthTech #FutureTech"

❤️ Heart Disease Prediction Using Machine Learning ❤️ As an Artificial Intelligence and Machine Learning expert, I recently focused on predicting heart disease using advanced machine learning techniques. By leveraging my expertise in classification, regression, and clustering models, I aimed to improve prediction accuracy and provide valuable insights for healthcare applications. Here’s a summary of my approach: Data Preprocessing: Cleaned and prepared the heart disease dataset with Pandas and Numpy to ensure high-quality analysis. Visualization: Utilized Seaborn and Matplotlib to explore key features and visualize data patterns. Model Training: Applied various classification models to predict the likelihood of heart disease. Evaluation: Conducted comprehensive model evaluations using appropriate metrics to validate performance and accuracy. #DataScience #DataAnalytics #ArtificialIntelligence #MachineLearning #HeartDiseasePrediction #HealthcareAI #DataVisualization #ModelTraining #AI

One of the most pressing questions in AI today is the battle between specialized models and LLMs (or other foundation models) for solving extremely sophisticated problems such as disease detection. Are we moving beyond the era of specialized models? Early evidence from some published papers suggests that with better prompting, specialized models are being beaten by LLMs. However, neither approach individually solves complex specialized problems with certainty. So, how can we combine the power of the two approaches to build more sophisticated outcomes? At Softsensor AI, we're working on answering this question in our specialized product models for Oncological outcomes. Our core hypothesis involves the application of multi-modal combination of specialized models with LLMs in a multi-step workflow. This approach will teach us how specialist knowledge in a very confined area can be supplemented by vast general knowledge of the subject. Solving specialist outcomes, outcomes on confined knowledge space, and solving higher degrees of specificity in localized knowledge are just a few of the important problems that will lead to better enterprise and business adoption. Join us on this journey towards better outcomes with specialized models and LLMs. #SoftsensorAI #SpecializedModels #LLMs #MultiModalCombination #BetterOutcomes #OncologicalOutcomes #BusinessAdoption

🚀 Excited to Share My Latest AI Project! 🚀I’m thrilled to announce the completion of my recent project: Heart Disease Prediction Using Machine Learning. In this project, I leveraged medical data to predict heart disease, applying seven different algorithms to ensure the highest accuracy.After rigorous testing and validation, Gradient Boosting emerged as the top performer, delivering the best accuracy in predicting heart disease.🔍 Project Highlights:Objective: Predict heart disease using medical data.Algorithms Applied: 7 different machine learning algorithms.Top Performer: Gradient Boosting.This project not only highlights the potential of AI in the healthcare sector but also emphasizes the importance of data-driven decision-making in improving patient outcomes.You can check out the complete project on my GitHub: [https://lnkd.in/dXaFRZi3] I am incredibly proud of this work and eager to continue exploring the intersection of technology and healthcare.#MachineLearning #AI #Healthcare #DataScience #HeartDisease #GradientBoosting #Innovation

Curious about the outcomes of an AI Residency program? Let's take a look at our current statistics! If you want to learn more or chat about upcoming cohorts, visit apziva.com and schedule a free call with our talent team! 📅💬 See you there! 🌐 #AIResidency #DataScience #MachineLearning #AIResearch #CareerGrowth

It was a privilege to learn from industry and academic experts at the Causal AI Conference organized by causaLens! Here are three of the key takeaways: 1️⃣ Addressing Interference and Complex Experiments: Guido Imbens provided insights on handling interference in complex experiments. He outlined four approaches:   - Clustering Designs (https://lnkd.in/ekAcnHvU)   - Equilibrium Designs (https://lnkd.in/ekHDHRKK)   - Multiple Randomization Designs (https://lnkd.in/eRs_BJdc)   - Bipartite Experiments (https://lnkd.in/e2yMg7SA) 2️⃣ Innovative Applications in Healthcare: Naveed Sharif and Jeff Groesbeck from Kaiser Permanente highlighted the use of A/B testing using a Bayesian framework to evaluate digital products. When A/B tests aren't feasible, quasi-experiments come into play. They showed that SMS refill reminders can impact medication adherence and overutilization. 3️⃣ Assumptions in Causal Inference: Eray Turkel from Google emphasized that "causality is a result of the assumptions that we impose to the data generating process, not the properties of a model" #CausalAI #DataScience #MachineLearning

Challenges of Using generative AI in medicine: 1. Data Quality and Availability: High-quality, large-scale datasets are essential for training generative models, but such data can be scarce, incomplete, or biased in the medical field. 2. Privacy and Security: Handling sensitive patient data requires stringent privacy measures. Ensuring that generative AI models comply with regulations like HIPAA is a significant challenge. 3. Regulatory Compliance: Generative AI applications in medicine must adhere to strict regulatory standards set by authorities such as the FDA, which can slow down development and deployment. 4. Bias and Fairness: Generative models can inadvertently learn and propagate biases in the training data, leading to unfair or inaccurate outcomes. Interpretability and Transparency: Medical professionals must understand how AI models make decisions. Many generative models, like deep learning networks, are often considered "black boxes." 5. Validation and Testing: It is crucial but challenging to ensure that generative AI models are thoroughly validated and tested in diverse, real-world scenarios. Integration with Existing Systems: Integrating generative AI solutions with existing healthcare IT systems (like EHRs) can be complex and costly. 6. Ethical Concerns: Using generative AI in medicine raises various ethical issues, including the potential for misuse, the impact on doctor-patient relationships, and the implications of AI-generated content. 7. User Trust and Acceptance: Building trust among healthcare providers and patients in AI-generated solutions is essential for widespread adoption. 8. Cost: Developing, implementing, and maintaining generative AI solutions can be expensive, particularly for smaller healthcare providers. 9. Scalability: Scaling generative AI solutions across different healthcare settings and geographies can be challenging due to variability in healthcare practices and infrastructure. 10. Responsibility and Accountability: Determining who is responsible and accountable for AI-generated decisions or errors is a complex issue. 11. Training and Expertise: Healthcare professionals need proper training to effectively use and understand generative AI tools. 12. Real-Time Processing: Generative AI models often require substantial computational resources, hindering real-time clinical applications. 13. Generalizability: It is crucial to ensure that generative AI models generalize well to different patient populations and medical conditions. 14. Data Annotation and Labeling: Annotating medical data for training generative models is labor-intensive and requires expert knowledge. 15. Patient Consent: Obtaining informed consent from patients for using their data in training generative AI models can be complicated.

We're pleased to share a new video featuring Christopher Foley PhD, Chief Scientist and Managing Director at bioXcelerate AI, discussing how data is revolutionising drug development. 📹 "Data is the fundamental asset that allows us to derive insights into disease aetiology and risk factors," Chris explains. "It's not merely about acquiring vast volumes of data, but about innovative methodologies in statistics, machine learning, and data engineering that underpin our ability to derive principled insights." At bioXcelerate, we're transforming raw data into reliable assets that power state-of-the-art AI algorithms. Our aim is to identify drugs that target the influential factors that switch off disease risk. Watch the video to learn how we at bioXcelerate are using advanced qualifications in mathematics to drive progress and improve health outcomes. Head over to our website and find out more about the experts behind bioXcelerate AI: https://lnkd.in/eRZDGmSE #DataInnovation #AIinMedicine #DrugDiscovery #BioXcelerate #FutureofHealthcare

Want to learn more about my background, thoughts on the industry, and a fun fact you might not know? If so, check out this Pheno-Type post! With thanks to Simon Eng for a delightful interview.