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Learning from Human Feedback: A Guide to Reinforcement Learning

An Intro to RLHF What makes an ordinary text a good one? Well, that is not an easy thing to define because texts are subjective and context-dependent. In recent years, language models have demonstrated impressive capabilities in generating diverse and compelling texts from human prompts. Imagine if we could leverage human feedback on generated text as a metric for evaluating the model’s performance, or better yet, use that feedback as a form of loss to optimize the model itself. This forms the basic idea for Reinforcement Learning from Human Feedback or RLHF. The method is what the title says: it makes use of reinforcement learning to directly improve a language model with human feedback. With RLHF, language models can align models trained on a large collection of text data with complex human values. The best example to understand RLHF’s success is ChatGPT, where this technology is one of the most pivotal reasons that make this chatbot so amazing. How does RLHF apply to large language models or LLMs? A Closer Look into Reinforcement Learning from Human Feedback Reinforcement learning is a machine learning field where agents learn decision-making by interacting with the environment. Agents take actions, including choosing not to act at all, impacting the environment, and triggering state transitions and rewards. Rewards are vital for refining the agent’s decision-making strategy. Throughout the training, the agent changes its policy to maximize cumulative rewards. This approach enables continuous learning and improvement over time. RLHF enhances the RL’s training by making the process human-centred. As such, this new technique has been pivotal in some of the latest chatbots that are creating headlines, such as: OpenAI’s ChatGPT InstructGPT DeepMind’s Sparrow Anthropic’s Claude With RLHF, LLMs are not merely trained to predict the next word. Instead, it is trained to understand instructions and give appropriate responses. Why Language is a Problem in Reinforcement Learning LLMs have proven to be good at handling multiple tasks at a time, such as: Code generation Text generation Question answering Protein folding Text summary On a large scale, they can do zero, and few-shot learning, thereby doing tasks they haven’t learned yet. The transformer model, which is the architecture utilized in large language models (LLMs), has achieved a significant milestone by demonstrating its capacity for training without supervision. LLMs, while impressive in their achievements, share basic features with other ML models. They are huge prediction machines that can guess the next prompt (token in a sequence). But, the biggest challenge here is that there are more than one correct answers for one prompt. All these answers may not be desirable in specific LLM contexts, applications, and users. Also, learning without supervision on extensive text corpora, although beneficial, may not fully correspond with the diverse range of uses it will encounter. In such cases, RL can guide LLMs appropriately. To understand it better, let’s approach language as a Reinforcement Learning problem: The agent is where the Language model itself functions as an RL agent, aiming to generate optimal text output. Action space- A list of language results generated by the LLM. The state space- The environmental state comprises of prompts from the user and the LLM results. Reward measures how the LLM responds to the application context and user intent. Other than the reward system, all other elements are relatively straightforward. Defining clear and effective guidelines for rewarding the language model’s performance is not a simple task. Luckily, it is possible to design an effective reward system for the language model by using RLHF. How Does RLHF Work: 3 Steps of RLHF For Language Models There are several challenges to RLHF. It has to be trained with multiple models and go through several deployment stages. As such, Reinforcement Learning from Human Feedback is executed through three basic steps: Step 1: The Pre-trained Language Model Initially, the RLHF uses a pre-trained LM trained with classical pretraining objectives. This step is crucial because LLMs need vast training data. Such an LLM trained in unsupervised learning will have a good language model capable of generating coherent outputs. However, some output may not always be relevant to the user’s needs and goals. Further, training the model having labelled data can generate more correct and appropriate results for specific tasks or domains. Step 2: Training the reward model Reward models are trained to recognize ideal results produced by the generative model. Then it rates them on relevancy and accuracy. The main LLM receives a prompt for each training example and generates several outputs. A dataset of LLM-generated text with quality labels is generated during the training process. Next, human evaluators review and categorize the generated texts from the best ones to the worst. The reward model is then trained to make predictions about the score from the LLM text. As a result, the generative model learns more and generates better and more relevant results. Step 3: Fine tuning with RL During the last phase, the reinforcement learning loop is established, which involves fine-tuning certain or all parameters of a replicated version of the initial language model using a policy-gradient RL algorithm. In reinforcement learning, the policy takes actions from a given state to maximize rewards, enabling real-time learning and adaptation. The model interacts with the environment, receiving feedback as rewards or penalties to know the actions that yield positive outcomes. Rigorous testing done with the help of a curated group ensures its competence in actual situations and accurate predictions. 3 Ways ChatGPT Utilizes RLHF Here’s how ChatGPT utilizes the RLHF framework in every phase: Phase 1 A pre-trained GPT-3.5 model underwent supervised fine-tuning by a team of engineers. A team of writers wrote answers to several prompts using the dataset of prompt-answer pairs to refine the large language model. Phase 2 A standard reward model was created. It generated several answers to the prompts, and human annotators ranked the responses. Phase 3 The Proximal Policy Optimization or (PPO) Reinforcement Learning algorithm was used to train the main LLM. However, there is no further information

May 25, 2023 No Comments

Content Annotation

The Role of Data Annotation in Training ChatGPT

When OpenAI introduced ChatGPT in 2022, it created a near-historical milestone in conversational AI. ChatGPT is one of the most advanced AI chatbots powered by a highly sophisticated language model. What makes ChatGPT a cut above the rest? Experts point out that the chatbot is powered by an extensive data annotation process that goes into its model training. ChatGPT could accurately interpret human language thanks to vast amounts of human-labeled text data. Annotations are crucial to a chatbot’s (like ChatGPT) ability to converse intricately and provide insightful responses. The technologies driving the ability to process, understand, and speak any human language are Natural Language Processing (NLP) and Machine Learning (ML). What is the reason for this sudden explosion in high-end technologies? Let’s explore. What is Driving the AI Revolution? Natural Language Processing is a pulsing buzzword in the tech world. The global NLP market is projected to reach an expected value of USD 91 billion by the year 2030. The market is already growing at a steady CAGR of 27% and is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029, with a CAGR of 38.8%. The existing Large Language Models or LLMs are all powered by NLP and ML that are, in turn, trained with very high-quality training data. This is what determines the success of these AI applications. What’s training data? Training data are sets (input and output pairs) of examples on which Machine Learning models are trained to make accurate predictions. The ML models use the input-output pairs to learn how to map inputs to the corresponding outputs. This mapping is the project’s foundation and is the learning basis for all ML models. This concept is better explained with an example. Take the sentiment analysis task, for instance. The training data for this task comprises a set of reviews and corresponding sentiment labels like: Fabulous > positive Unacceptable > negative Functional >neutral The model is trained on this kind of data to learn how to predict the sentiment of new reviews. The concept is simple: The higher the sample quality, the more accurate the output. ChatGPT 3 is an ideal example of this concept. The chatbot was trained on 176 billion parameters, 570 GB of books, articles, websites, and other textual data taken from the Internet. Where is the ChatGPT’s Data Sourced From? Basically, ChatGPT is fed WebText datasets comprising nearly 8 million web pages taken from the internet, along with additional datasets to enhance its performance. The WebText datasets which refers to data taken from the Internet, provide easy access to information. The diverse collection covers various sources like online forums, websites, and new articles. The additional datasets comprising text sources like written works, articles, and books make the training data diverse for developing LLMs like ChatGPT. So, how was ChatGPT trained? Let’s unravel that puzzle. How ChatGPT Was Trained: A Step-by-Step Guide Data annotation is the key element used to construct LLM as advanced as ChatGPT. The main process here was adding meaningful tags to text data to enable the AI model to understand the context and meanings behind phrases and words. Using data annotation as the core element, ChatGPT was developed using these steps: Step 1: Data collection To build such an advanced Chatbot, OpenAI used a massive corpus of text data from numerous online sources. All irrelevant and duplicate information was then removed from this enormous data collection to clean it up. Step 2: Data labeling All the collected data were annotated by a skilled team of annotators who were trained to apply the labels with complete precision. The labels included: Pat-of-speech tagging Text classification Sentiment labels Named entity recognition Step 3: Training the model By using transformer architecture, the language model was trained using the annotated data. The model was trained to predict the most suitable labels for words or phrases based on the context and annotated data. Step 4: Evaluation & Fine-tuning A separate dataset evaluated ChatGPT’s ability to accurately predict labels in new, unseen texts. The evaluation results were then used to fine-tune the AI model until it achieved the desired performance level. Step 5: Deployment The trained and fine-tuned ChatGPT was deployed and made available for real-time usage allowing users to generate natural language responses to their inputs. How Data Annotation Fuelled ChatGPT’s Conversational Capabilities As a starting point, ChatGPT was trained using transformer-based language modeling. Basically, ChatGPT’s architecture follows the concept of transformer architecture. It comprises a multi-layer encoder-decoder system and self-attention capabilities. The self-attention capabilities allow it to focus on various input aspects as it generates output. During the training phase, ChatGPT’s parameters were modified by exposing them to vast volumes of text data. The aim was to minimize the disparity between the model-generated text and the target text. Identifying patterns in the text data was necessary to create contextually appropriate and semantically sound text. The fully trained model was then deployed for several Natural Language Processing tasks like: Finding answers to questions Language Translation Text creation ChatGPT is powered by the GPT-3 model, which was trained using annotated data, which provided it with a wealth of information, including named entities, coreference chains, and syntax trees. This data annotation enabled ChatGPT’s model to completely understand text generation and comprehension in multiple genres and styles. ML and AI applications depend heavily on data labeling to ensure the accuracy and quality of the data used to train effective ML models. Furthermore, the text data was basically annotated manually by a team of annotators trained to label accurately and consistently. To ensure data accuracy and quality, labelers annotated the data using automated methods in some cases. How ChatGPT Eases The Work For Data Annotators ChatGPT is a boon for data annotators. This amazing AI tool helps annotators with the following tasks: Classifying sentences into various categories like intent, sentiments, and other topics. Identifying named entities in texts, such as locations, dates, organizations, and people. Extract structured information from unstructured data like product prices and names.

May 18, 2023 No Comments

Two mobile phones displayed one with ChatGPT with some examples and another mobile with Google Search page, illustrating new AI updates from Google.

News

Is Google Challenging ChatGPT? Alphabet Unveils New AI Updates @ Google I/O 2023.

With ChatGPT creating headlines worldwide, Alphabet Inc. plans to cause a sensation by announcing the latest updates at its Google I/O Conference on May 10th, 2023. While the central theme of this annual developer’s conference is AI, it will also highlight the following: Google’s heavy investments in generative AI Announcement of PaLM 2, the new large language model Updates to Bard and Search with “generative experiences.” PaLM 2: A Big Step Forward in Multilingual & Medical Language Models Alphabet Inc. is all set to launch the latest LLM, PaLM2, which is already in operation with the codename “ Unified Language Model.” The Large Language Model (LLM) has more than 100 languages and has also performed many tests in coding, Math, creative writing, and analysis. Last month, Google announced that Med-PaLM 2, its medical LLM answered medical exam questions at an “expert doctor level” with 85% accuracy. Google to Announce Updates to Search & Bard Since AI will be the central theme of Google’s I/O 2023 conference, the company plans to explain AI’s role in facilitating people to optimize their potential. It will also announce the latest updates to Searcn and Bard. Google CEO Sundar Pichai will showcase the company’s advancements in AI during the conference. In March, Google officially introduced the Bard model. The company had been working on Multi-Bard, a multi-modal adaptation which used larger data sets and solved problems in coding and Mathematics. Other versions, named Big Bard and Giant Bard, have also been tested by Google. Why Google’s Updates Are Important Google’s announcements come at a time when the digital world is witnessing intense AI competition. Google and Microsoft Corp are top contenders racing to capitalize on the AI chatbot technology. Microsoft Corp has already invested in OpenAI to bolster Bing’s search engine. Other Updates on Hardware Releases Alphabet also plans to use the conference to unveil other hardware releases, such as additional updates for Google Lens, its image recognition software. Google will introduce improvements to the “multi-search” feature for voice and camera features. Besides these announcements, Google will showcase The Pixel Fold, its new foldable phone. Google claims the phone will have an extremely durable hinge available for a foldable phone and also comes with an exchange option. The phone is also designed to be pocket-sized and durable. This AI update was brought to you by Opporture – a well-known AI company in North America offering a wide range of AI-driven content services.

May 11, 2023 No Comments

Content Moderation

The Best Guide to Understanding AI-powered Content Moderation

Did you know that around 2.5 quintillion bytes of data are uploaded to the Internet every single day? That is A LOT of data comprising good and bad content. This makes content moderation a growing necessity to make this huge volume of content appropriate for all. As a leading AI company in North America, Opporture has been at the forefront of cutting-edge content moderation services to ensure that the digital world is a much safe and secure place. Now, let’s dive deeper into the world of content moderation and why it is a necessity in the current digital scenario. Content Moderation: What is it & Why is it So Important? As we know, digital media content can be broadly classified into informative, useful, user-friendly, age-appropriate content and harmful, misleading, and negatively-impactful content. Essentially, content moderation is regulating digital content and keeping it well within the standards and guidelines issued by online platforms. The process involves: Reviewing and monitoring of user-generated content Removing inappropriate and offensive content Enforcing community guidelines and terms of service Today, all online platforms that thrive on user-generated content consider content moderation a mandatory lifeline. They include: Social media platforms Dating sites Communities Online forums Sharing economy E-commerce marketplaces Generally, content moderation is done using a team of human moderators, automated tools, or AI-powered technologies. Irrespective of the method, all user-generated content will require effective moderation to remove illegal, harmful, or copyrighted content. It goes without saying that AI-driven moderation is the most potent option to speed up the review process and scale the overall operation. What is AI-powered content moderation? As we have seen already, with the growing amount of content generated every day, human moderators can just simply not handle the volume. On the other hand, AI-assisted content moderation can help optimize the moderation procedures and can bring scalability and efficiency to the overall process. Content moderation driven by Artificial Intelligence uses Machine Learning models trained on online-platform specific data to identify and remove unwanted content with accuracy. The AI moderation decisions are highly precise enough to automatically refuse, approve, or prioritize unwanted content. Generally, moderation can happen at 2 stages: Pre-moderation – before content is published Post-moderation – after the content has been published. AI models can impact the moderation process in the following ways: AI can help improve the accuracy of moderation at the pre-moderation stage by flagging inappropriate content for review by human moderators. The user-generated content and other content being moderated can be fed back as training data for improving the contextual understanding and accuracy of the model. Once done, AI can assist the human moderation teams in processing content at scale and flagging off or rejecting inappropriate content. Why is there so much buzz around content moderation? Well, if content is king, content moderators are kingmakers. The content on your online platform can elevate your business by several levels or completely obliterate it. What better reasons do businesses need to create high-quality content appreciated by all types of users? However, content moderation serves more purposes than you know. 4 Reasons Why Content Moderation is Important 1. It ensures your content remains pure and unadulterated. Ironically, freedom of thought and expression makes it impossible to dodge user-generated content that does not violate your platform’s guidelines. However, consistent moderation with AI-powered technology and human moderators effectively curbs the presence of unsavory and inappropriate content created by online miscreants. 2. It helps you understand pattern recognition and user behavior. Tagging content with key properties gives you better insights into user behavior and pattern recognition, especially for high-volume campaigns. The more intent you show in moderating your content, the better you can protect your brand and users. These insights also help you make better product and marketing decisions. 3. It takes your campaigns to the next level. If you are looking to drive marketing and sales campaigns, all you need is an effective content moderation strategy to dispel any negativity about your brand and make the campaign work in your favor. Using content moderation, you can scale the campaign by hosting contests, crowdsourcing ideas, garnering reviews, etc. 4. It attracts organic traffic & boosts conversions. Moderation creates a safe and reliable online environment, encouraging people to engage and spend more time on your platform. This also increases the chances of them clicking on ads and making purchases. Content moderation also curtails spam, improving user experience on your company’s website or social media page. The higher the quality of content you own, the better your chances of attracting organic traffic with greater engagement rates. Now that you know what you gain with content moderation, you may want to use it to its full potential. However, you need to have a basic understanding of how it works. Content moderation works in different ways for different data types. Moreover, there are various techniques too. AI-driven content moderation significantly reduces the risk of damaging content being posted by mistake. However, such errors are highly possible when you rely on human moderators. Here are some ways AI can help you optimize your content moderation. AI-driven Content Moderation Services: What It Moderates & How it Works Text moderation with AI Natural Language Processing is one of the key methods for text moderation because the volume of texts is usually much higher than that of images or videos. In text moderation, keyword filtering and topic analysis helps identify relevant content and remove it if it is inappropriate. For example: Linking keywords to sentiments and categorizing them under positive, neutral, and hostile. Using keywords to alert negativity such as crises, age-inappropriate content, or brutality. Entity recognition Entity recognition uses AI to extract names, locations, and companies to indicate the number of times your brand was mentioned on a particular platform. It also tells you how users from a specific location post reviews of your company or brand. In entity recognition, AI tools trained to detect emotions can predict the message tone and classify it. Image & video moderation Text moderation and

May 8, 2023 No Comments

News

AI-fabricated Content Farms On The Rise – Warns Misinformation Tracker

Warning From Newsguard Makers of the NewsGuard, a content rating tool, warned on May 1st about a new generation of content farms found in 49 news sites that publish content. These content farms appear to be AI-fabricated, NewsGuard said. According to NewsGuard, the tool identified 49 websites covering seven languages: Thai, Tagalog, English, French, Portuguese, Czech, and Chinese. These websites are partially or entirely driven by Artificial Intelligence models designed to mimic human communication in the form of news websites. However, none of these websites acknowledged using AI to generate stories. Newsguard’s review of the content revealed that most are relatively low-stakes, created to generate easy clicks and revenue, but some sites spread potentially dangerous misinformation. Obvious Telltale Signs Chatbot-generated news has some very obvious red flags. For example, CelebritiesDeaths.com published news claiming President Joe Biden had “passed away peacefully in his sleep.” The news also claimed that Vice President Kamala Harris succeeded him. As in this case, ChatGPT posted an error message after the first few lines of the fake story. The AI chatbot claimed it “cannot complete the prompt as it goes against OpenAI’s use case policy on generating misleading content.” Red Flags Indicating AI-generated News With Aggregated Content Journalists and analysts at NewsGuard worked on spotting prominent red flags indicating AI-generated content. Some sentences, like “I am not capable of producing 1500 words,” are AI-generated. NewsGuard’s report also revealed that all 49 sites had at least one article with obvious AI errors, and most were summaries of articles from other prominent news organizations. The report also reveals ample evidence of the growing interest among digital publishers in investing in AI chatbots. Tech’s news site CNET came under fire for using AI to generate low-quality news articles. CNET Money’s AI-generated content was found to have multiple factual errors. Misinformation Risks From AI-Generated Content Farms Based on NewsGuard analysis, content farms are prolifically misusing AI and rarely checking the resulting content for factual errors. With services offering widely available coherent, error-free texts, AI-generate content farms are steadily on the rise. To make matters worse, reputed news websites already use AI despite the risk of spreading misinformation by mistakenly letting AI-induced facts bypass editing and proofreading. Human-in-the-Loop: The Ideal Approach to Thwart AI-generated False News The likelihood of factual errors and editorial oversights calls for the compelling need for ethical guidelines while incorporating AI into the news industry. The Human-in-the-Loop approach is the ideal solution to overcome AI-generated content farms from spreading rumors and misleading information. To ensure integrity and factual accuracy, it is imperative to deploy human oversight even for AI-generated content. Hence, developing a human-AI partnership is crucial to curbing factual errors with AI-generated content. Such an approach will make news more transparent, reliable, and genuine. Opporture, is a leading AI company with a reputation for high-quality, AI-powered content related services. Reports claim that they ensure that all AI-generated content is error-free and clean. This can be a great help to thwart the AI-generated false news and ensure integrity.

May 3, 2023 No Comments

Content Moderation

Content Moderation For Social Media: Why You Need it & How It Works

Artificial Intelligence is hogging the limelight. This groundbreaking technology has immense potential waiting to be unleashed across diverse industries and verticals. AI’s capacity for pattern recognition and consequence prediction can be leveraged for content moderation, especially on social media. Why Do We Need Content Moderation for Social Media? More than half the world’s population is on social media. Online platforms like Twitter, Facebook, Instagram, TikTok, YouTube, and LinkedIn welcome people irrespective of race, color, creed, or religion. Age, of course, is a criterion, but that hasn’t stopped parents from creating special accounts for their children. Even pets have millions of followers on Instagram and Facebook. Social media platforms allow users to share videos, posts, and audios that cover almost every topic under the sun. They are free to express opinions and feelings and even share an hourly account of what they are doing and where they are. Businesses use these platforms to establish an online presence, garner followers and entice them into becoming customers. These pulsing centers of digital activity also have a dark side. Statistics show that nearly 38% or 4 out of every 10 people face offensive online behavior from people who hide behind nonsensical usernames and fake ids. Cyberbullies and digital miscreants are misusing social media platforms. This unsavory scenario underlines the pressing need for powerful content moderation strategies. At this point, only AI and ML (machine learning) can tackle this issue head-on. How Does AI-powered Content Moderation Work for Social Media? Content moderation is the process of monitoring and managing content pouring in from various social media platforms. The main aim of using content moderation for social media is to identify and remove inappropriate content and make the platform safe for all ages. Manual moderation is almost unthinkable with the amount of content uploaded every second of every minute. AI-driven content moderation, on the other hand, automated the entire process. It meticulously reviews content, identifies objectionable material, and forwards the revised content for approval. This systematic process makes the content visible to all users, removes it from the user’s account, or even blocks unruly users from using their accounts. Sometimes, users who post unwanted content are let off with a mild warning. As far as social media is concerned, content moderation happens in two ways: Reviewing and approval of content after it is uploaded by the users. Moderation of content before it is streamed live. The types of content moderation generally used to filter spam and make platforms clean and usable for everyone are: Post-moderation Pre-moderation Reactive moderation User-only moderation Automated or Algorithmic moderation Of these, automated moderation is the most advanced because it is AI-driven. In automated moderation, ML algorithms detect inappropriate content from millions of posts uploaded every single day. These ML algorithms are trained to detect unsavory images, videos, audio, and texts. What it cannot accurately interpret, however, is subtle or nuanced messages of hate, obscenity, bias, or misinformation. Most often, social media platforms use content moderation tools trained on social media postings, web pages, and Natural Language Processing from various communities. This annotated data empowers these tools to detect abusive content in the communication taking place within various communities. Types of Data Moderated on Social Media Platforms Social media content moderation covers various types of data, such as: Text moderation The volume of text content generated on social medial platforms exceeds the number of images and videos shared by users. Since the text covers a multitude of languages from all over the world, content moderation requires Natural Language Processing techniques to moderate textual content. Image moderation AI image recognition requires more sophistication for automated image detection and identification. In images, ML algorithms use Computer Vision to identify objects or characteristics like nudity, weapons, and logos. Video moderation Generative Adversarial Networks, or GANs, are used to identify images or videos manipulated with malicious intent. These ML models can also detect videos with fictional characters, actions, and deep fakes. More than a decade has passed since social media came into existence, but the need for content moderation is prolific now more than ever. If it is not implemented with a robust hand, it may be too late to prevent the repercussions when things go out of hand. 5 Reasons Why Content Moderation is a Must For Social Media Platforms 1. To maintain a safe online environment for users. Every social media platform is responsible for protecting its users from any content that instigates hate, crimes, untoward behavior, cyberbullying, and misinformation. Content moderation significantly reduces such risks by identifying and eliminating damaging content off the platform. 2. To maintain a harmonious user relationship. Content moderation bridges the gap between moderators and users. Users can share company, brand, or product-related feedback directly with the moderators. These insights help businesses improve their services and maintain a harmonious relationship with their customers. 3. To ensure safe and user-friendly communities. Social media platforms are also virtual communities, and like any other community, they require decorum to keep it safe and welcoming for everyone. Moderation helps keep an eye on non-compliant users and ensures positivity and inclusivity. 4. To prevent the spread of false information. Anything can become “trending” or “viral” on social media platforms, and misleading information is at the top of the list. Such information in videos, texts, or images can spread like wildfire as users share it on their profiles. Content moderation curbs the spread of false information inside the community. 5. To regulate the live streaming of videos. Many people do not stop to think twice about misusing live-streaming technology to gain personal attention or put others into embarrassing situations. Many have even tried to stream dangerous or sensitive videos on their social media handles. Only AI-powered content moderation can help curb such nonsense and ensure that live streaming is used for the right reasons. Social media content moderation is the best and most pragmatic solution to regulate content on these digital platforms. If not, the unfiltered content can severely damage the person, business, and

May 1, 2023 No Comments

Collection of X-ray images of human body parts representing role of AI image annotation in medical imaging.

Data Annotation

AI-Powered Data Annotation & Its Groundbreaking Role in Medical Imaging

AI in Healthcare: Facilitating Greater Possibilities in Medical Sciences Technology and medicine are starkly different fields, but they are intricately intertwined, with the latter being more dependent on the former. Even the latest tech “bigwigs” like Artificial Intelligence and Machine Learning are becoming more prevalent in healthcare. AI/ML models trained on customized data make it easier to predict diagnostic results with greater accuracy in various healthcare scenarios, thanks to their algorithms. Today, AI in healthcare is a prolific market valued at around one billion US dollars as of 2016. Estimates predict this value will exceed $28 billion by 2025. Globally, the market for AI in medical imaging alone takes a hefty chunk of this value. The current figure stands at around $980 million and is projected to rise at a CAGR of 26.77%, which is a whopping $3215 million by 2027. In this scenario, researchers are exploring opportunities to implement AI into medical imaging. This technology can open the doors to precision diagnosis for cardiac, thoracic, and neurological issues. It can also improve medical screenings to simplify the assessment of patient risk factors and reduce doctors’ workload. Medical Image Annotation: A Remarkable Technology Medical image annotation involves labeling imaging data like MRI, CT scans, ultrasounds, text-based medical records, etc. The annotated data is then used to train ML models with deep learning algorithms for more accurate medical diagnosis. Also, a valuable ML model can only be developed with accurately annotated text, notes, and metadata. The most commonly annotated medical documents include: Medical images and X-rays CT scans and ultrasounds MRI and mammograms Medical videos and photos Physician dictation audio DICOM EEG NlfTI EHR dataset Artificial Intelligence holds great potential for the medical field. It unleashes many prospects for the healthcare industry in North America and across the world to provide quicker, more accurate, and more reliable diagnoses. Medical Image Annotation: How it Helps Improve Healthcare 1. Brain injury diagnosis When trained with precise annotated images, ML models can detect brain tumors, blood clots, and other neuro-related conditions. AI facilitates neuro-imaging by properly annotating and feeding data about brain injuries into CT scans and MRIs. Once the model is fully trained, it will, one day, replace our radiologists and make medical imaging easy. 2. Cancer detection Deep learning models can be pre-trained with cancer image data that has been labeled to accurately predict cancer cells. Once trained, the model can recognize abnormal regions in new image data and can effectively aid in early cancer detection, eliminating the need for human judgment and possible errors. It can also predict if a person is healthy or suffering from undetected cancer. 3. Liver ailment diagnosis Usually, doctors assess and characterize liver images on ultrasounds or CT scans to diagnose the occurrence of liver diseases. In such cases, there are possibilities for inaccurate diagnosis due to an unintended bias that could stem from their experience. Medical image annotation eliminates such inaccuracies by training AI models to perform quantitative assessment rather than qualitative reasoning. This enables the model to produce a more accurate and unbiased imaging diagnosis. 4. Kidney stone detection The use of AI for kidney-related ailments is yet to attain significance, although the technology is currently used for: Diagnostic guidance Prognoses evaluation Guiding in treatments Alerting mechanisms However, the prospects of the AI model diagnosing kidney failure in the near future are very much possible. It will likely happen when the algorithms have access to the appropriate annotated data sets. 5. Fracture detection X-ray images be used to train models to visualize the bone structure. They can then be annotated to identify fractured areas. This annotated data is fed to the model, which is trained to accurately detect and predict bone fractures. 6. Eye cell analysis Eye scans are a great tool for doctors to detect a wide range of eye and retinal complications. Using the right AI techniques, it is possible to annotate the visible eye-related symptoms to enable accurate diagnosis of ocular diseases, cataracts, and other ailments. 7. Dentistry AI-enabled models will become very handy for dentists to diagnose structural abnormalities of the teeth, deep-rooted cavities, gum-related issues, and other dental diseases. 8. Pathology Medical imaging annotation improves pathologists’ ability to diagnose tumors and other abnormalities by leveraging deep learning algorithms trained on massive datasets of medical records. This timely diagnosis will considerably reduce the time for patients to receive medical care for the condition. How to Ensure HIPAA Compliance With AI-based Models In the USA, anything related to medicine should be HIPAA-compliant. The same goes for medical image annotation because AI models are trained and tested on massive volumes of annotated medical images. What is HIPAA? Known as the Health Insurance Portability and Accountability Act of 1996, HIPAA is a federal law governing the safety of electronically transmitted health information. According to HIPAA, healthcare providers must protect patient information from being disclosed without consent. Hence, it is imperative to choose an AI model training platform that ticks the following criteria: Has a system for healthcare information and storage management. Stores, maintains, and updates backups for all systems. Prevents unauthorized access to sensitive medical data. Ensures data encryption during rest and transfer. Prevents users from exporting and storing medical images on personal devices. Let’s Wrap Up Training AI models on accurately annotated datasets will enable groundbreaking progress in the field of medicine. In turn, it will simplify diagnosis and disease identification to promote early intervention. As a reputed AI model training company in North America, Opporture offers high-quality data annotation services to support many US-based clients in the healthcare industry with medical image annotation. Want to know how we leverage our advanced AI technology for medical image annotation? Give us a call, and let’s discuss. Related terms Annotation Machine learning Model

April 27, 2023 No Comments