From Data to Diagnosis: How Medical AI is Transforming Modern Healthcare

What Makes Artificial Intelligence Essential for Modern Businesses? Artificial Intelligence is no longer futuristic—it’s the driving force behind AI-powered business solutions today. At Deep Data Insight (DDI), AI isn’t just about algorithms; it’s about building AI-powered ecosystems that simplify complexity, empower professionals, and accelerate growth. Every solution ensures that data turns into data-driven decision-making with real-world outcomes. How Does Deep Data Insight Follow a Human-First Innovation Model? Unlike many technology providers, DDI leads with human-first innovation. Their mission is simple yet powerful: building solutions that solve real challenges. Whether it’s artificial intelligence in healthcare to decode medical records, AI recruitment solutions to help recruiters evaluate candidates, or predictive insights for finance leaders—DDI focuses on impact, not just capability. Each product is designed with meaning, ensuring businesses can rely on AI to solve actual problems while driving measurable results. How Does DDI Turn Concepts into Real AI Solutions? Deep Data Insight follows a structured project lifecycle that balances creativity with precision: This approach ensures every project moves seamlessly from strategy to execution, producing sustainable outcomes. What Innovative Products Define Deep Data Insight? DDI brings innovation to life through cutting-edge platforms and business intelligence tools: Why Do Businesses Choose Deep Data Insight? Organizations partner with DDI for clear, measurable advantages: These strengths make DDI a trusted partner for enterprises seeking advanced AI-powered business solutions. What Do Clients Say About Deep Data Insight? Long-term partners frequently describe DDI as an extension of their own team. From automating complex workflows to overcoming data bottlenecks, clients highlight the company’s ability to deliver high-impact solutions that align with strategic business goals. What Is the Future of AI and Data-Driven Decision Making? As data volumes surge, the real challenge isn’t collection—it’s transformation into intelligence. DDI is committed to shaping that future. By combining business intelligence platforms, advanced analytics, and scalable AI, Deep Data Insight empowers organizations to thrive in a data-driven decision making environment. FAQ’s

Large Language Models – An overview Large Language Models are offering numerous advantages for organisations who want to use AI to bring efficiencies to their workflows. But this has not always been the case. For the last few years, language-based Artificial Intelligence models have come to the forefront of Natural Language Processing (NLP). In simple terms, a language model can be derived as a probability distribution over a sequence of words. It assigns a probability to a piece of unseen text, based on some training data. The capabilities of a language model vary from simple analytical tasks such as sentiment analysis, spell checking, translations between languages etc. to more advanced features; question and answering, speech recognition, text summarization, semantic search and many more. Voice assistants like Siri, Alexa, Google Translator, and search engines like Google Search, and Bing are the biggest and most familiar examples that showcase the power of language models. As a result of continuous research, academic institutions, and big tech companies such as OpenAI, Microsoft and Nvidia have come up with more improved versions of these simple language models. They are building more intelligent systems with a richer understanding of language extending the capabilities of existing models. So, these latest high performing language models are now called large language models (LLM). As the name reflects these models are larger in sheer size. Firstly, the enormous amounts of data (covering different styles, such as user-generated content, news data and literature which amounts to billions) on which they have trained. Also, the wide range of tasks which can be applied to create smarter platforms with a greater processing speed. The most fascinating thing about the LLM is that with large models there is no need to start from scratch, train and finetune with costly clusters of servers. Large models are capable of recognizing things that haven’t explicitly been seen during training (zero-shot scenarios) or use with fine-tuning, based on a specific domain with a minimal amount of data (few-shot scenarios). Recently, companies like Nvidia, Microsoft, and Open AI have taken steps to release API access to these LLMs making them accessible and affordable for everyone. Not only that, the usages of LLM have grown dramatically and the models are able to question and answer with tabular data, content generation, image generation, code completion and more. Among LLMs, BERT and GPT can be pointed out as the most capable and easily accessible model families in the market. Large Language Models: BERT Bidirectional Encoder Representations from Transformers (BERT) is a transformer language model developed by Google. BERT was one of the first solutions to dominate the market with transformer architecture in 2018. Amongst many applications, BERT is able to: Interestingly, unlike many other LLMs, BERT is open source, allowing developers to run models quickly without spending fortunes on development. The variants of BERT (SpanBERT, DistilBERT, TinyBERT, ALBERT, RoBERTa and ELECTRA) are special versions which are intelligently optimized to outperform the drawbacks of BERT. Large Language Models: GPT The Generative Pre-Trained Transformer (GPT) model was first introduced in 2018 by OpenAI. The powerful performance of the model with few or zero unlabeled data based on Autoregressive Transformer language intrigued the NLP community at that time. After the initial release, further iterations appeared. GPT-3 appeared in 2020 and was the highest performance LLM at the time, being 1,000 times the size of GPT-1. GPT3 can perform: This is a version which can be used even without fine-tuning the model. GPT-Neo, GPT-J, and GPT-NeoX are the other variants of this family that were trained and released by EleutherAI. Open-source versions of GPT-3 which are released by Open-AI now available for affordable prices. Google researchers in February 2022 published a model far smaller than GPT-3, called Fine-tuned Language Net (FLAN) which beats GPT3 on a number of challenging benchmarks. It outperformed GPT-3 on 19 out of the 25 tasks as well as performance on 10 tasks. Large Language Models: In Conclusion The community of Large Language Models is unstoppable. Their capability is proven and has proven benefits. Use of LLMs is widespread and mainstream. They are extremely accessible since they exist increasingly as open-source solutions. LLMs continue to evolve with new research and technology. Large Language Models and Deep Data Insight Deep Data Insight have been at the forefront of using LLMs to transform their customers’ experiences. Key benefits are to the efficiencies of workflows, where high-cost manual time is replaced with an LLM model. DDI’s emerging ‘Document AI’ platform will ensure that all data extracting functions will be available through a single platform. With Document AI, LLMs are used to extract data from a given document using the LLMs Question and Answering feature. This means that the whole document does not have to be reviewed – instead just ask a question to find and extract the relevant documented information. This can even be used to find and extract data from tables in CSV or Excel files. The platform will also allow a user to search across a variety of documents to find the one that is most relevant for any given keyword. DDI are active in a number of sectors, including healthcare, real estate and insurance. These are all sectors that Platform AI will benefit with its pioneering application of LLMs. For information about how we are successfully helping our clients achieve amazing ROI in their workflows, take a look at our case studies here: https://www.deepdatainsight.com/case-studies/

Introduction AI is capable of table detection in documents. As the world becomes increasingly digitized, we are all feeling the benefit of having our documentation available online. Whether we are individual consumers, or big businesses, these advantages are palpable. Benefits include: As we have seen in previous posts, Deep Data Insight have worked on numerous projects where the introduction of Artificial Intelligence into workflows has brought enormous efficiencies. However, whilst this process is relatively straightforward for text, it is much harder when it comes to tabulated information. Recently though DDI have pioneered the art of table detection in documents. Table detection in documents: The Challenge For centuries, humans have used tables as a way of comparing data and analysing for trends. This evolved in the 20th century when programmers introduced the world’s first spreadsheets. Having data represented in table format means that it can be understood relatively intuitively, and can be the basis for in-depth interrogation with, for example, graphical and figurative interpretations. The 21st Century has seen enormous advances in the use of Artificial Intelligence and machine learning to understand and predict text. The two main technologies are ICR (Intelligent Character Recognition) and OCR (Optical Character Recognition). ICR and OCR enable a computer to digitise information accurately and quickly. PDFs are the pre-eminent solution to presenting documents such as invoices and receipts. Electronic Source PDFs will be digitised from the outset; these are also known as ‘Native’ PDFs. ‘Scanned’ PDFs will have started life as a physical document, captured by a mobile device. The issue arises however when tabulated information is included in documents such as PDFs. This is because the information is no longer being represented in a way that AI can be programmed to understand, and table structure will vary tremendously from one table to another. This is becoming more of a pronounced challenge as we become increasingly reliant on our own mobile devices. We use their cameras to capture information, and increasingly to convert these images into usable data. However, because tables by their nature are problematic to AI, table detection in documents has long been a real challenge, and many non-tech industries will still rely on manual processes for extracting and recreating tables in their documents. This is labor intensive and therefore costly and prone to error. This is largely a sector-agnostic challenge. It follows though that where an increased number of tables are included into the mix of information to be used, then the issue will be more prevalent. Such sectors include engineering, science and academia, FMCG and others. Table detection in documents: The Solution Deep Data Insight have created a set of smart solutions using Artificial Intelligence for table detection and data extraction from any type of document. This means that the process of digitising and therefore streamlining workflows need not be held up if the data involved includes tabulated information. Key to these solutions are two open source technologies to which DDI connects through APIs: Tensorflow and Keras Tensorflow is an open-source software library that has been created as a learning and development resource for programmers, specifically those involved in machine learning and artificial intelligence. Keras is another open-source resource which provides a python interface for artificially created neural networks. By using Tensorflow and Keras, Deep Data Insight can accelerate model building and the creation of scalable machine learning solutions. As well as these two back-end technologies, DDI employ their significant expertise with a deep learning model known as CNN – Convolutional Neural Network – to analyse the visual imagery. A novel CNN model developed with pre-trained VGG-19 features Optical Character Recognition (OCR) is used in order to extract table data accurately. DDI have years of experience with OCR, as this technology underpins its successful EDDIE product. Table detection in documents: The Results The first thing to understand is that the huge generic advantages of digitising data are already being gained by Deep Data Insight customers. They are experiencing enormous cost savings across their multiple workflows. However, since DDI now also has a set of solutions for table detection in documents, and their extraction from varying and multiple documents, these savings be further increased and provided by a single supplier – DDI. These benefits are sector agnostic, and can be as follows: DDI is now supporting its clients across many sectors that are heavily reliant on tabulated information. Insurance, where individual documents needs processing; Construction, where agreement documents often contain tables and in healthcare, where tables are often found in medical prescriptions. Next Steps As with any type of technology, table detection in documents is already evolving quickly, and Deep Data Insight are at the forefront of this evolution. In the not-too distant future, we will see this technology being applied to any object detection problem such as video surveillance and anomaly detection in healthcare. Notes Document AI is a Deep Data Insight product developed over years by our data scientists and using the latest deep learning and OCR technologies. For more information about our client successes, read our case studies here https://www.deepdatainsight.com/case-studies/