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Confidently Navigating Software as a Medical Device (SaMD) Product Development

 

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Short course on SaMD (Software as a medical device), IEC 62304 and IEC 82304-1


ENTIRE TRANSCRIPT  

hello software friends I assume you landed on this video because you are interested in software as a medical device also known as Sam in this short course I want to share my thoughts what to get right when working with software only products and at the end of this video I will also share tips about software risk management which is painful if you don't get it right I am Christian Kessner and I started my career as embedded software engineer in the early 90s after a couple of years I moved into the medical advice field where I have developed sterilization equipment transplantation devices wound care therapy and much more several years ago I joined the project teams authoring IEC 62304 and IEC 82304-1 and I also work as a lead auditor this short course applies to you if you are developing software as a medical device for example an app a cloud service or a regular desktop application I will be showing you some key Concepts in the medical device software area to help you understand it and avoiding mistakes to get things right I first want you to know the difference between a software release and a product release then I want you to know about the necessary standards that are needed for smooth approval process but standards are not too exciting so I also want to share a few practical tips on how to conform to the standards and I've saved the best for the last software risk management now let's get to it I often hear people say that working with software as a medical device is different from working with other medical devices in my opinion on product level there are no differences because you must work with planning requirements risk management verification and validation regardless of what type of medical device products you develop but to be fair there are some differences when it comes to the development of software let me share what I believe are the two main differences the first and the most obvious reason is software bugs a software product comes with all bugs enabled but you don't know when that will pop up if you have a car you can take it to a garage and ask them to check your brakes or oil level but for software there is no such option one Infamous example of software bug is a Windows blue screen it is very unexpected when it happens and you lose all your unsaved work when working with medical devices the same blue screen can mean the loss of a patient's life clearly this makes people nervous because there's no way to confidently claim a hundred percent test coverage when working with software this brings me to the second topic which is about how products are created the vast majority of software issues or bugs are traceable back to errors made during the design and development process and this is logical because there is no manufacturing process when working with software products for tangible products designing the manufacturing process is a significant part of your development work in the manufacturing process you can Implement checkpoints like in process controls and apply statistical process controls when working with software products process control is not an option because there is no manufacturing process you simply have to trust that the development was done correctly and all quality controls must be part of your development work and again sorry to say or solve for repeating myself there is no final production test where you can Implement inspections to capture product failures closest thing in software would perhaps be the software release node which is not at all the same thing speaking of software release takes me to software release versus product release in modern software development it is common to find continuous integration and continuous deployment continuous delivery is a software development methodology where the release process is automated every software change is automatically built tested and can be deployed to production at any time this is cool but before you get too excited this is usually a software release and not a product release now you might think when talking about software as a medical device there are no other components than the software system perhaps not components but in an audit situation I would expect to find accompanying documents such as the instruction views and evidence of a successful design validation in your product release it may be simplification but you could say that as software as a medical device product is the combination of software and its relevant documentation and product documentation is usually overlooked in a software release as you can see a complete medical device release flow spans many more activities than the software release you find on the left hand side just to mention a few there are requirements for design validation and accompanying documents such as the instruction views these activities will result in what I call a design release you may be using another term once the design release is done you may need to wait for regulatory approval before product release and placing the product on the market after initial product release you can usually manage non-significant changes without new third-party regulatory approval however please ensure you maintain relevant regulatory documentation for example keep the MDR general safety and performance checklist and Technical documentation up to date when working with Standalone software you can merge soft release and design release into a single activity for example by including technical documentation and instruction views as configuration items in your development work this is a lean approach and makes sense in most cases still I want you to be careful about placing a software on the market without considering all the activities and deliverables needed for a product release now you know the difference between a software release design release and a product release next up is the central standards applicable to software as a medical device standards aren't the most exciting part of software development but the idea with standard is to establish a common way of thinking which can be recognized by various stakeholders such as regulatory authorities so by conforming the standards your head previews and Auditors to understand your development process and the resolving output if you do this right your chances of a smooth approval process increases significantly when working with the software as a medical device there are six essential standards you should be aware of and preferably also understand and Implement at the bottom you will find something often referred to as management standards they usually the core in any quality management system ISO 13485 contains requirements on your quality management system that is how you're supposed to work in general this includes for example requirements on design and development and that you have to establish design and development inputs but ISO 13485 also contains requirements that are not related to designer development for example how to structure your quality management system and how to manage documents in general getting back to the design and development inputs they are inputs to your software requirements and software development process in ic 6234 the design and development inputs as they are calling ISO 1345 are referred to as system requirements when working with Standalone software too many layers of a requirement might just complicate things if so feel free to merge requirements into fuel or even a single document just remember to include the requirements for all applicable standards ISO 14971 contains requirements on your risk management process that is how you should be performing risk management the risk management process provides one of the most important inputs to a software development process and that is hazardous situations a good understanding of hazardous situation is key to performing meaningful software risk management and getting it right and as it so happens you can find causes on both ISO 1345 and ISO 4971 on medical device HQ IEC 6204 is the leading process standard you need to understand for software development luckily for you we're sure to take a closer look at IC 6204 if you have used interface of any kind in your software you will also bump into IEC 62366-1 which is about usability engineering the usability Engineering Process can contribute to both functional requirements and user interface related risks which you need to consider in your development work please note though the definition of use interface in the usability engineering standard is Broad it is not only reducing phase you see in the software so be aware even if your software is intended to run as a technical background service without any traditional use interface the usability standard might still be applicable for example when working with instruction views and installation instructions that are connected to how to use the software product the last process standard I want you to be aware of is IEC 81001-5-1 which is about security it might not be applicable to all some products but if you are working with Standalone software there is a highlight load that security is something you should keep a close eye on the next and the last standard to be covered in this overview is IEC 82 304-1 it is a product standard for Standalone software such as apps PC program and cloud services that are supposed to run on generic Hardware platforms so when working with Standalone software this standard applies to you and your product iec82304 is a product standard for health software the standard defines requirements for establishing high-level product requirements for your software product you also find requirements on design validation and accompanying documents but in all honesty IEC 82304-1 essentially duplicates the requirements you find on design inputs and design validation in ISO 13485 therefore when working with software asymmetical device my recommendation is to adapt your existing quality management procedure to embrace the additions you find in IEC 8234-1 instead of establishing a standalone procedure for ISE 82304-1 but in one aspect IEC 82304-1 is much more specific than what you find in ISO 1345 which is requirements about accompanying documents as a product standard it defines a detailed requirements on what to include in the instruction for use and the technical description of your software for example the following activities are expected to be described in instructions for use installation startup shutdown operating and decommissioning now let's have a closer look at IEC 62304 which is a process standing meaning you will not find details like performance or screen resolution requirement or what to include in instructions for use what you will find is a list of requirements and activities you should carry out throughout your development cycle if you are a software developer this standard will influence your work the most this is because the standard defines requirements you must relate to in your daily work many Sam the developers are working agile nowadays and unfortunately the standard is very sequential which implies a water foliage development method however the standard has no requirement forcing you to apply a specific development method you're free to use whatever method you want to as long as you acknowledge the process approach and don't skip any activities in the standard let's take a look at the standards essential elements development risk management configuration management problem resolution and maintenance each process is divided into activities with a development process having the most activities and now this is not rocket science if you have been working in a mature organization developing software whether that be in defense industry or mobile phone industry you will find that the ways of working are similar but use different names there's one difference though in the medical advice industry we are obsessed with safety because in the medical device industry a software bug is not only blue screen it can literally be a blue screen of death a bug can kill someone back to the standard a nice thing with this standard is that the number of activities you need to do depends on how harmful your product can be this is decided with the help of software safety classification the flow through the standard is the same but the rigor is different depending on your classification which is further explained in the standard graphical overview visualizes the scope of IEC 6234 blue boxes are in scope gray or out of scope arrows between blue and gray is where there is an information exchange and as you learned before IEC 6234 gets its input from IEC 82304-1 and ISO 13485 the first Arrow gives you inputs about what and when when is for example pre-releases for various purposes before final release what can be functional requirements such as information to display on a screen or alarm management it can also be Hardware constraints even for Standalone software for example do you have one or more CPUs available or should Communications happen over USB or ethernet in the standard there is a clause about software requirement content but nothing about how you document your requirements you can capture requirements in any way you want it can be tickets stories Wiki Pages or Advanced requirement management tools it is up to you but please remember a couple of formal requirements you need to consider for example in your system verification you must reference a specific set of requirements which I would call a Baseline this is particularly important when working with agile methods you should be able to identify the complete set of requirements applicable to a specific version even if you work in sprints requirements shall also be reviewed and improved please make sure your setup can support you in this work traceability is also required by the standard ideally your choice of requirement management also supports you with traceability from a requirement to verification in addition to when software releases are expected you also need to plan for Content purpose and how to deliver each software release the expectations are most likely different if you're releasing for a demonstration usability test or aim for a design release he will back to our obsession with safety to develop a safe software we need input about hazardous situations to understand what we must avoid this input often includes risk related requirements for example a clear warning when a diagnosis can't be decided the go to standard for risk management is ISO 14971 your risk management process should provide you with all information necessary to effectively Implement software risk management bulk of your development work will be in the highlighted section where you can see the various development activities that will take place and the order in which they should ideally happen however it is unlikely your development work will happen exactly like this and your development method should allow you to be adaptive as your knowledge evolves over time one way to do this is by working with agile methods Agile development methods Embrace changes and allow a product to evolve over time let's look at an example with the help of scrum you might feel there's a mismatch between the waterfall approach in the standard compared to working with agile methods well there is no need to be worried working with backlog stories and Sprint planning can be compared with when eating an elephant take one bite at a time let's look at an example from the standard and compare the two ways of working you need to verify the quality of your requirements by for example verifying that they do not contradict one another or expressed in terms that avoid ambiguity are traceable to system requirements or other sources if you review a scrum story before it gets implemented you do it by taking one byte at a time the wording might differ but you certainly don't want contradictions and ambiguity in your stories if a simplify things you verify software requirements on a story based level instead of verifying your complete backlog of requirements if we now compare with a waterfall approach you typically aim to have nearly a full set of requirements and then go for a review in this late example you will not verify as frequently as you do in scrum but you will spend more time in every review so you can work with agile principles and meet the standard requirements it is only a question of how you have chosen to meet the requirements in case you're into Agile development there is an excellent technical report from Amy tir45 which is a guidance on the use of agile principle enjoy your reading there's much more to talk about when it comes to Theory and standards but for now I will continue with other topics which I believe your more practical user let's kick off with planning I'm not a planning guy but I've learned to accept the importance of proper planning when working with software as a medical device you will spend most of your time in software development which should be planned following the requirements of IEC 6234 the standard requires you to plan configuration management software risk management and much more you can choose to split these into different plans but instead of making your life hard I suggest you keep it simple and start with a single plan a single source of Truth you should only start splitting the project into different plans if it adds value to your project team for example if you have a separate test team then it could make sense to have a dedicated verification plan the software development plan is a perfect place to explain how the team is structured and how you interact with your quality management system let's take a look at an example of what interacting with quality management system could be many quality management systems are developed with a physical piece of paper mod you could call it a document based mindset software information is typically not organized based on physical papers information is structured based on other aspects such as architectural functional considerations and preferably the information is kept where it supports the developer the most I would say in the source code there is a requirement in a standard about detail design the conservative approach to this requirement is to write a traditional document but since you already Version Control your source code why not let some of your documentation coexist with the code instead of forcing the information into traditional paper document here it's up to you either you keep the information in the source file or you decide to extract the information from source file and create traditional documents your way of working with information and documents can be explained in the software development plan because there's certainly no need to convert all the information to paper document but you need to identify it where it can be found and how you control it in this example I've used oxygen format for the comments in case you really must you can also easily extract the comments to tradition document formats when you later maintain the code the documentation is available where it supports the developer the most and and it's also much easier to maintain if you take this one step further technical documentation come an integral part of your build process by creating a build pipeline for documentation running in parallel with your software build this would ensure that documentation always being in sync with the software let's talk about configuration management I would say that a software product usually has more configuration items to control than all documents together in a complete development project if you look at the definition of a configuration item it says entity that can be uniquely identified at a given reference point in the software domain a given reference point can be down to hours or even minutes and this is very difficult if you compare with documents which typically are released with the help of signatures in a much lower Pace maybe ask yourself what is a configuration item a configuration item can be many things it can be pre-commered libraries build files source code compiler settings basically any piece of information needed to create your deliverable including documentation one example of configuration management is Version Control Systems which can support you with traceability in this example you see two branches for bug fixes by using branches you can quickly review how a specific bug has been resolved in the bigger picture Version Control also supports you in reviewing changes between releases this is super helpful when you're determining how much regression testing needs to be done or what you should put into your release node but it's not only the Practical aspects of gathering data and providing traceability support correctly implemented configuration management can also support process aspects of your development work let's break down the flow in a couple of steps the first step is a decision step for example when you decide to implement a change request you can use rules to Define who can create branches and use this to control when implementation work starts in response to an approved change request the next step is the implementation step if you define a new procedure that you should always work on a branch and never directly in the main this ensures that you always have traceability back to the reason why you are implementing something the third step is about verification this is a great step to implement a peer review and good practice is that you are not budging back your own code to the main the verification step is also a step where you can get a lot of compliance work integrated directly into your workflow and while you're at it also take the opportunity to review relevant documentation if you prepare good work instructions on how to merge back branches to main you can cover most of the content of the courses I've listed here on the screen the documentation part of this would end up as information pieces in your configuration management system and as I mentioned in the planning section it will be documented but not as traditional documents if you haven't done it yet spend some time in looking for good configuration management tool that can satisfy your needs and please make sure you validate the system set up and use it as much as you can I assume you have some basic understanding of risk management if you don't I recommend you watch the videos about risk management at medical device HQ one of the most misunderstood Statesmen in this field is that the probability of occurrence of harm should be set to 100 just because you're working with software I can tell you that statement is wrong the probability of occurrence of harm can be split up into two components P1 and P2 the probability of occurrence of the hassle situation is P1 P2 is the likelihood that the Hazardous situation will lead to harm when people are talking about the probability of occurrence of harm and claim it shall be a hundred percent they refer to P1 but also this assumption can be challenged let's look at an example where a software is used to recommend the proper medication in this example we assume that one percent of the patients are allergic to drug a and we have five different drugs to choose from the software fails by choosing the wrong medication what is the PO for this risk the correct answer is it depends it depends on the type of software failure we are dealing with either the software failure results in randomly selecting one of the five drugs or the failure always result in choosing drug a for the sake of safety we could assume that drug a will always be selected but if we are talking about random errors the like load for the software to only select drug b or any other drug is as high as it is for only selecting drug a this results in a different scenario in the random drug example I still agree that the software failure always will happen but cannot predict the outcome so in this particular case if the Hazardous situation is that drug a is incorrectly chosen the likelihood of the hassle situation occurring is 20 percent it is quite a difference to summarize if you are working a lot with software please do yourself a favor and start using P1 and P2 if you stay with po only you will be in a challenging situation I will of course go into more depth on this topic in the full course and also explain how to work with risk control measures in software to reduce P1 and eventually even P2 thank you for watching this short course if you want to learn more about software as a medical device IEC 6204 and IEC 82304-1 I welcome you to sign up for the full online course introduction to sound IEC 6234 and IEC 82304-1 the food cause is similar to this short course but much more comprehensive with more in-depth information and quizzes at the end of each topic to test your knowledge and understanding at the end of the full course you will also receive a course certificate which many Auditors will be asking for at medical device hq.com we offer online courses public classroom courses as well as in-house training on safety risk management usability engineering design control quality management and clinical investigation for medical devices drop us a line on support at medical device hq.com if you would like to learn more about your options or receive a proposal all right to me on the same email address if you have questions relating to medical device development in general I hope to hear from you [Music] 

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Confidently Navigating Software as a Medical Device (SaMD) Product Development

 June 15, 2023

 Josh Cates, Tim Thirion & Andinet Enquobahrie


There are specific technical and regulatory considerations for Software as a Medical Device (SaMD), many of which are still evolving. It is challenging to stay informed in this fast-changing landscape, especially with new regulations around artificial intelligence and cybersecurity. To help address these challenges, it is advantageous to partner with a company like Kitware that is experienced with navigating both the quality and regulatory aspects of SaMD engineering. There are many ways our team can provide support to medical device companies throughout a typical SaMD product lifecycle.

Understanding the evolving SaMD regulations

SaMD products are stand-alone medical devices that may perform a variety of functions, such as clinical decision support, therapeutic guidance, diagnostics, or patient monitoring. Since this is a relatively new area in the medical device industry, the regulatory landscape is still evolving. As technology advances and integrates the latest innovations (e.g. artificial intelligence), regulatory agencies including the U.S. Food and Drug Administration (FDA) are continuously updating their guidance documents.

Some of the technical and regulatory considerations you should be aware of for SaMD versus traditional hardware-based medical devices include cybersecurity risk mitigation, human factor engineering, unique installation and operational qualification requirements, software-oriented post-market surveillance processes, and evolving guidance from the FDA specifically related to AI software.

How to maximize speed-to-market for SaMD products

Whether you are just starting out or an established medical company pivoting to software, the many considerations for SaMD can be overwhelming. Good regulatory and quality advisors are essential for ensuring compliance with all of the evolving software standards, but it is equally important to find an engineering partner with hands-on experience building compliant SaMD products. An experienced engineering team can work more efficiently with your regulatory and quality teams to optimize speed-to-market and minimize costs. And bringing experienced SaMD partners on board early in your software design process will help avoid costly mistakes and delays.

SaMD engineering experts will positively impact your medical software product development lifecycle

IEC 62304 is the internationally harmonized standard that defines the product lifecycle process for medical device software. This standard is recognized by regulatory agencies around the world including the FDA. IEC 62304 describes product lifecycle activities in the following areas:


Stay on top of all the latest changes with AI, cybersecurity, and SaMD

Finding a company to support both the regulatory and engineering guidelines is difficult. That’s why Kitware is proud to offer our customers comprehensive support for software medical devices. We provide regulatory experience and software engineering support throughout all phases of the SaMD product lifecycle, whether we are supplementing your internal engineering team, consulting on medical device software design and testing, contributing to specialized areas of development (e.g. machine learning and PCCPs), or helping with post-market surveillance activities and CAPA support. Kitware’s software engineers are recognized leaders in medical computing, machine learning, and software engineering, and are comfortable working closely with your team to provide support in these areas. We also bring considerable experience in software cybersecurity, as we work extensively with the U.S. government on classified projects. Kitware’s commitment to open source development can also provide a unique competitive advantage in terms of speed-to-market, lower documentation overhead, and more robust, bug-free code.

Take the next step in your SaMD lifecycle

This discussion of a SaMD lifecycle is a high-level illustration of a typical software process. It is important that every company develop its own compliant quality management system and operating procedures that are appropriate for their organization and device risk categorization. Kitware is happy to help you navigate this process. The FDA publishes extensive guidance on best practices for SaMD development (some related guidance documents are listed below), but you can contact our team to discuss how we can support you through a compliant engineering process for SaMD products.

Want to learn more? Read some of Kitware’s other related blog posts

https://www.kitware.com/confidently-navigating-software-as-a-medical-device-samd-product-development/

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Data Commons (IDC): Intuitive visualizations and cloud resources for medical imaging research

 May 24, 2023

 Brianna Major, Andriy Fedorov, Steve Pieper, Deepa Krishnaswamy, Paul Elliott, Tom Birdsong, Stephen Aylward & Matt McCormick


ITKWidgets run within Jupyter Notebooks to provide 2D and 3D image visualizations, with optional color overlays.  Users can switch between 2D and 3D views, select predefined or create custom color and opacity transfer functions, crop the data, and perform many other visualization manipulations.

We are pleased to announce the integration of the visualization capabilities of ITKWidgets with the massive collection of clinical data in the NCI Imaging Data Commons (IDC). This work was a collaboration between Kitware, the National Cancer Institute, and Brigham and Women’s Hospital.  As demonstrated in the IDC’s Getting Started and deep learning (Segmentation Primer) jupyter notebooks, integrating ITKWidgets’ visualizations with IDC’s data handling and AI workflows creates a streamlined research experience, facilitating the discovery of new insights and improved results. Furthermore, the visualization capabilities of ITKWidgets are not limited to IDC data.  They can be utilized in any jupyter notebook running on Macs, Windows, Linux, Google Colab, or Amazon Sagemaker Studio.  They are available now in a pre-release version of ITKWidgets via the pip command with the –pre option.   Details and highlights are given next.

Background

The IDC is a part of the Cancer Research Data Commons’ (CRDC) initiative to provide a cloud-based centralized repository of “AI-ready” medical imaging data from various sources, including academic research centers, clinical trials, and industry partners. The IDC aims to enable researchers of varying expertise to easily access and explore imaging data, which makes ITKWidgets the perfect complementary tool! 

ITKWidgets is a Python package that provides high quality, interactive visualizations that run directly within Jupyter notebooks.  They allow users of all skill levels to quickly inspect and manipulate data and processing results within their workflows, without having to interrupt their focus or switch to other applications.


Calling itkWidgets.view(image, overlay) in a Jupyter Notebook cell creates an interactive visualization as the cell’s output. 

Features

During our collaboration, we received invaluable feedback, feature requests, and bug reports from students and researchers involved in this project and actively using IDC data with ITKWidgets in their own work.  This hands-on information allowed us to rapidly improve the overall ease of use and capabilities of ITKWidgets during our collaboration.

One key new feature is a more intuitive and powerful system for window width and level manipulation. In the world of medical image visualization, the ability to set the window width and level, which maps recorded to displayed intensities and is commonly referred to as windowing, is particularly useful. This allows radiologists to adjust the appearance of the image they are viewing to highlight structures of interest.  The new ITKWidgets window/level interactivity provides an experience expected by radiologists when using a radiological viewer, but it also extends that capability to support arbitrary colormaps (not just grayscale), complex opacity functions, and 3D as well as 2D visualizations. As shown in the lower left in the video below, itkWidgets displays a histogram of an image’s recorded intensities and a combined color and opacity transfer function is overlaid.  The height of the function and the color under the function at each recorded intensity position defines how that recorded intensity is displayed in 2D and 3D

.

With window/level enabled, left-click-hold and moving the mouse up/down over the visualization controls the window’s level and moving right/left controls window’s width.  The color and opacity transfer function, overlaid on the image’s histogram is automatically updated.

Within ITKWidgets users can toggle its window/level feature on or off with the black and white circle icon underneath the transfer function widget. When this feature has been toggled “on”, users can then left-click and drag the mouse up and down to change the window level or left and right to change the window width. Additionally, users can manually enter specific window/level values in the numeric fields located above the histogram. users can also quickly undo changes using the reset button located to the right of those numeric fields.  See the figure belo

w.

Yellow box highlights the circular icon used to toggle the windowing feature on or off. Green boxes highlight where window/level values can be manually entered.

Other new features include improved image and labelmap blending functions, stable support for Google Colab and Amazon Sagemaker Studio cloud notebooks, and the ability to handle large images thanks to an improved, multi-scale, data streaming protocol between the webpage visualization and the notebook’s python kernel server.

Additional information on these and other features is available in the ITKWidget documentation: https://itkwidgets.readthedocs.io/en/latest/

Summary

By providing interactive widgets for visualizing and exploring 3D images, ITKWidgets makes it easier to understand the complex data that medical professionals rely on. And by supporting window width and level adjustments, ITKWidgets helps users identify important details that might otherwise be missed. When combined with platforms like IDC and the MONAI open-source medical image artificial intelligence library, ITKWidgets can accelerate the pace of important research that can improve patient outcomes and advance the field of medical imaging.

Try it Yourself

The IDC platform for sharing and accessing medical imaging data, combined with the inline, interactive visualizations provided by ITKWidgets is a great example of how open-source initiatives can work together to lower barriers and simplify medical imaging research. To try quickly iterating on 3D analysis of open IDC datasets, start with the example notebook that demos some of the data shown in this post, or try the updated tutorials or the ITKWidgets exampl

es.

Acknowledgments

Research reported in this publication was supported, in part, by the National Institute Of Biomedical Imaging And Bioengineering, the National Institute of Neurological Disorders and Stroke, and the National Institute of Mental Health at the National Institutes of Health, under Award Numbers R01EB028283, R01EB014955, R42NS086295, and 1RF1MH126732. IDC has been funded in whole or in part with Federal funds from the NCI, NIH, under task order no. HHSN26110071 under contract no. HHSN261201500003l. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

https://www.kitware.com/itkwidgets-and-idc/

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A guide to improving your existing business application of artificial intelligence

מדריך לשיפור היישום העסקי הקיים שלך בינה מלאכותית

What is Artificial Intelligence and how does it work? What are the 3 types of AI?

What is Artificial Intelligence and how does it work? What are the 3 types of AI? The 3 types of AI are: General AI: AI that can perform all of the intellectual tasks a human can. Currently, no form of AI can think abstractly or develop creative ideas in the same ways as humans.  Narrow AI: Narrow AI commonly includes visual recognition and natural language processing (NLP) technologies. It is a powerful tool for completing routine jobs based on common knowledge, such as playing music on demand via a voice-enabled device.  Broad AI: Broad AI typically relies on exclusive data sets associated with the business in question. It is generally considered the most useful AI category for a business. Business leaders will integrate a broad AI solution with a specific business process where enterprise-specific knowledge is required.  How can artificial intelligence be used in business? AI is providing new ways for humans to engage with machines, transitioning personnel from pure digital experiences to human-like natural interactions. This is called cognitive engagement.  AI is augmenting and improving how humans absorb and process information, often in real-time. This is called cognitive insights and knowledge management. Beyond process automation, AI is facilitating knowledge-intensive business decisions, mimicking complex human intelligence. This is called cognitive automation.  What are the different artificial intelligence technologies in business? Machine learning, deep learning, robotics, computer vision, cognitive computing, artificial general intelligence, natural language processing, and knowledge reasoning are some of the most common business applications of AI.  What is the difference between artificial intelligence and machine learning and deep learning? Artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions.  Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.  Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.  What are the current and future capabilities of artificial intelligence? Current capabilities of AI include examples such as personal assistants (Siri, Alexa, Google Home), smart cars (Tesla), behavioral adaptation to improve the emotional intelligence of customer support representatives, using machine learning and predictive algorithms to improve the customer’s experience, transactional AI like that of Amazon, personalized content recommendations (Netflix), voice control, and learning thermostats.  Future capabilities of AI might probably include fully autonomous cars, precision farming, future air traffic controllers, future classrooms with ambient informatics, urban systems, smart cities and so on.  To know more about the scope of artificial intelligence in your business, please connect with our expert.

מהי בינה מלאכותית וכיצד היא פועלת? מהם 3 סוגי הבינה המלאכותית?

מהי בינה מלאכותית וכיצד היא פועלת? מהם 3 סוגי הבינה המלאכותית? שלושת סוגי הבינה המלאכותית הם: בינה מלאכותית כללית: בינה מלאכותית שיכולה לבצע את כל המשימות האינטלקטואליות שאדם יכול. נכון לעכשיו, שום צורה של AI לא יכולה לחשוב בצורה מופשטת או לפתח רעיונות יצירתיים באותן דרכים כמו בני אדם. בינה מלאכותית צרה: בינה מלאכותית צרה כוללת בדרך כלל טכנולוגיות זיהוי חזותי ועיבוד שפה טבעית (NLP). זהו כלי רב עוצמה להשלמת עבודות שגרתיות המבוססות על ידע נפוץ, כגון השמעת מוזיקה לפי דרישה באמצעות מכשיר התומך בקול. בינה מלאכותית רחבה: בינה מלאכותית רחבה מסתמכת בדרך כלל על מערכי נתונים בלעדיים הקשורים לעסק המדובר. זה נחשב בדרך כלל לקטגוריית הבינה המלאכותית השימושית ביותר עבור עסק. מנהיגים עסקיים ישלבו פתרון AI רחב עם תהליך עסקי ספציפי שבו נדרש ידע ספציפי לארגון. כיצד ניתן להשתמש בבינה מלאכותית בעסק? AI מספקת דרכים חדשות לבני אדם לעסוק במכונות, ומעבירה את הצוות מחוויות דיגיטליות טהורות לאינטראקציות טבעיות דמויות אדם. זה נקרא מעורבות קוגניטיבית. בינה מלאכותית מגדילה ומשפרת את האופן שבו בני אדם קולטים ומעבדים מידע, לעתים קרובות בזמן אמת. זה נקרא תובנות קוגניטיביות וניהול ידע. מעבר לאוטומציה של תהליכים, AI מאפשר החלטות עסקיות עתירות ידע, תוך חיקוי אינטליגנציה אנושית מורכבת. זה נקרא אוטומציה קוגניטיבית. מהן טכנולוגיות הבינה המלאכותית השונות בעסק? למידת מכונה, למידה עמוקה, רובוטיקה, ראייה ממוחשבת, מחשוב קוגניטיבי, בינה כללית מלאכותית, עיבוד שפה טבעית וחשיבת ידע הם חלק מהיישומים העסקיים הנפוצים ביותר של AI. מה ההבדל בין בינה מלאכותית ולמידת מכונה ולמידה עמוקה? בינה מלאכותית (AI) מיישמת ניתוח מתקדמות וטכניקות מבוססות לוגיקה, כולל למידת מכונה, כדי לפרש אירועים, לתמוך ולהפוך החלטות לאוטומטיות ולנקוט פעולות. למידת מכונה היא יישום של בינה מלאכותית (AI) המספק למערכות את היכולת ללמוד ולהשתפר מניסיון באופן אוטומטי מבלי להיות מתוכנתים במפורש. למידה עמוקה היא תת-קבוצה של למידת מכונה בבינה מלאכותית (AI) שיש לה רשתות המסוגלות ללמוד ללא פיקוח מנתונים שאינם מובנים או ללא תווית. מהן היכולות הנוכחיות והעתידיות של בינה מלאכותית? היכולות הנוכחיות של AI כוללות דוגמאות כמו עוזרים אישיים (Siri, Alexa, Google Home), מכוניות חכמות (Tesla), התאמה התנהגותית לשיפור האינטליגנציה הרגשית של נציגי תמיכת לקוחות, שימוש בלמידת מכונה ואלגוריתמים חזויים כדי לשפר את חווית הלקוח, עסקאות בינה מלאכותית כמו זו של אמזון, המלצות תוכן מותאמות אישית (Netflix), שליטה קולית ותרמוסטטים ללמידה. יכולות עתידיות של AI עשויות לכלול כנראה מכוניות אוטונומיות מלאות, חקלאות מדויקת, בקרי תעבורה אוויריים עתידיים, כיתות עתידיות עם אינפורמטיקה סביבתית, מערכות עירוניות, ערים חכמות וכן הלאה. כדי לדעת יותר על היקף הבינה המלאכותית בעסק שלך, אנא צור קשר עם המומחה שלנו.

Glossary of Terms


Application Programming Interface(API):

An API, or application programming interface, is a set of rules and protocols that allows different software programs to communicate and exchange information with each other. It acts as a kind of intermediary, enabling different programs to interact and work together, even if they are not built using the same programming languages or technologies. API's provide a way for different software programs to talk to each other and share data, helping to create a more interconnected and seamless user experience.

Artificial Intelligence(AI):

the intelligence displayed by machines in performing tasks that typically require human intelligence, such as learning, problem-solving, decision-making, and language understanding. AI is achieved by developing algorithms and systems that can process, analyze, and understand large amounts of data and make decisions based on that data.

Compute Unified Device Architecture(CUDA):

CUDA is a way that computers can work on really hard and big problems by breaking them down into smaller pieces and solving them all at the same time. It helps the computer work faster and better by using special parts inside it called GPUs. It's like when you have lots of friends help you do a puzzle - it goes much faster than if you try to do it all by yourself.

The term "CUDA" is a trademark of NVIDIA Corporation, which developed and popularized the technology.

Data Processing:

The process of preparing raw data for use in a machine learning model, including tasks such as cleaning, transforming, and normalizing the data.

Deep Learning(DL):

A subfield of machine learning that uses deep neural networks with many layers to learn complex patterns from data.

Feature Engineering:

The process of selecting and creating new features from the raw data that can be used to improve the performance of a machine learning model.

Freemium:

You might see the term "Freemium" used often on this site. It simply means that the specific tool that you're looking at has both free and paid options. Typically there is very minimal, but unlimited, usage of the tool at a free tier with more access and features introduced in paid tiers.

Generative Art:

Generative art is a form of art that is created using a computer program or algorithm to generate visual or audio output. It often involves the use of randomness or mathematical rules to create unique, unpredictable, and sometimes chaotic results.

Generative Pre-trained Transformer(GPT):

GPT stands for Generative Pretrained Transformer. It is a type of large language model developed by OpenAI.

GitHub:

GitHub is a platform for hosting and collaborating on software projects


Google Colab:

Google Colab is an online platform that allows users to share and run Python scripts in the cloud

Graphics Processing Unit(GPU):

A GPU, or graphics processing unit, is a special type of computer chip that is designed to handle the complex calculations needed to display images and video on a computer or other device. It's like the brain of your computer's graphics system, and it's really good at doing lots of math really fast. GPUs are used in many different types of devices, including computers, phones, and gaming consoles. They are especially useful for tasks that require a lot of processing power, like playing video games, rendering 3D graphics, or running machine learning algorithms.

Large Language Model(LLM):

A type of machine learning model that is trained on a very large amount of text data and is able to generate natural-sounding text.

Machine Learning(ML):

A method of teaching computers to learn from data, without being explicitly programmed.

Natural Language Processing(NLP):

A subfield of AI that focuses on teaching machines to understand, process, and generate human language

Neural Networks:

A type of machine learning algorithm modeled on the structure and function of the brain.

Neural Radiance Fields(NeRF):

Neural Radiance Fields are a type of deep learning model that can be used for a variety of tasks, including image generation, object detection, and segmentation. NeRFs are inspired by the idea of using a neural network to model the radiance of an image, which is a measure of the amount of light that is emitted or reflected by an object.

OpenAI:

OpenAI is a research institute focused on developing and promoting artificial intelligence technologies that are safe, transparent, and beneficial to society

Overfitting:

A common problem in machine learning, in which the model performs well on the training data but poorly on new, unseen data. It occurs when the model is too complex and has learned too many details from the training data, so it doesn't generalize well.

Prompt:

A prompt is a piece of text that is used to prime a large language model and guide its generation

Python:

Python is a popular, high-level programming language known for its simplicity, readability, and flexibility (many AI tools use it)

Reinforcement Learning:

A type of machine learning in which the model learns by trial and error, receiving rewards or punishments for its actions and adjusting its behavior accordingly.

Spatial Computing:

Spatial computing is the use of technology to add digital information and experiences to the physical world. This can include things like augmented reality, where digital information is added to what you see in the real world, or virtual reality, where you can fully immerse yourself in a digital environment. It has many different uses, such as in education, entertainment, and design, and can change how we interact with the world and with each other.

Stable Diffusion:

Stable Diffusion generates complex artistic images based on text prompts. It’s an open source image synthesis AI model available to everyone. Stable Diffusion can be installed locally using code found on GitHub or there are several online user interfaces that also leverage Stable Diffusion models.

Supervised Learning:

A type of machine learning in which the training data is labeled and the model is trained to make predictions based on the relationships between the input data and the corresponding labels.

Unsupervised Learning:

A type of machine learning in which the training data is not labeled, and the model is trained to find patterns and relationships in the data on its own.

Webhook:

A webhook is a way for one computer program to send a message or data to another program over the internet in real-time. It works by sending the message or data to a specific URL, which belongs to the other program. Webhooks are often used to automate processes and make it easier for different programs to communicate and work together. They are a useful tool for developers who want to build custom applications or create integrations between different software systems.


מילון מונחים


ממשק תכנות יישומים (API): API, או ממשק תכנות יישומים, הוא קבוצה של כללים ופרוטוקולים המאפשרים לתוכנות שונות לתקשר ולהחליף מידע ביניהן. הוא פועל כמעין מתווך, המאפשר לתוכניות שונות לקיים אינטראקציה ולעבוד יחד, גם אם הן אינן בנויות באמצעות אותן שפות תכנות או טכנולוגיות. ממשקי API מספקים דרך לתוכנות שונות לדבר ביניהן ולשתף נתונים, ועוזרות ליצור חווית משתמש מקושרת יותר וחלקה יותר. בינה מלאכותית (AI): האינטליגנציה שמוצגת על ידי מכונות בביצוע משימות הדורשות בדרך כלל אינטליגנציה אנושית, כגון למידה, פתרון בעיות, קבלת החלטות והבנת שפה. AI מושגת על ידי פיתוח אלגוריתמים ומערכות שיכולים לעבד, לנתח ולהבין כמויות גדולות של נתונים ולקבל החלטות על סמך הנתונים הללו. Compute Unified Device Architecture (CUDA): CUDA היא דרך שבה מחשבים יכולים לעבוד על בעיות קשות וגדולות באמת על ידי פירוקן לחתיכות קטנות יותר ופתרון כולן בו זמנית. זה עוזר למחשב לעבוד מהר יותר וטוב יותר על ידי שימוש בחלקים מיוחדים בתוכו הנקראים GPUs. זה כמו כשיש לך הרבה חברים שעוזרים לך לעשות פאזל - זה הולך הרבה יותר מהר מאשר אם אתה מנסה לעשות את זה לבד. המונח "CUDA" הוא סימן מסחרי של NVIDIA Corporation, אשר פיתחה והפכה את הטכנולוגיה לפופולרית. עיבוד נתונים: תהליך הכנת נתונים גולמיים לשימוש במודל למידת מכונה, כולל משימות כמו ניקוי, שינוי ונימול של הנתונים. למידה עמוקה (DL): תת-תחום של למידת מכונה המשתמש ברשתות עצביות עמוקות עם רבדים רבים כדי ללמוד דפוסים מורכבים מנתונים. הנדסת תכונות: תהליך הבחירה והיצירה של תכונות חדשות מהנתונים הגולמיים שניתן להשתמש בהם כדי לשפר את הביצועים של מודל למידת מכונה. Freemium: ייתכן שתראה את המונח "Freemium" בשימוש לעתים קרובות באתר זה. זה פשוט אומר שלכלי הספציפי שאתה מסתכל עליו יש אפשרויות חינמיות וגם בתשלום. בדרך כלל יש שימוש מינימלי מאוד, אך בלתי מוגבל, בכלי בשכבה חינמית עם יותר גישה ותכונות שהוצגו בשכבות בתשלום. אמנות גנרטיבית: אמנות גנרטיבית היא צורה של אמנות שנוצרת באמצעות תוכנת מחשב או אלגוריתם ליצירת פלט חזותי או אודיו. לרוב זה כרוך בשימוש באקראיות או בכללים מתמטיים כדי ליצור תוצאות ייחודיות, בלתי צפויות ולעיתים כאוטיות. Generative Pre-trained Transformer(GPT): GPT ראשי תיבות של Generative Pre-trained Transformer. זהו סוג של מודל שפה גדול שפותח על ידי OpenAI. GitHub: GitHub היא פלטפורמה לאירוח ושיתוף פעולה בפרויקטי תוכנה

Google Colab: Google Colab היא פלטפורמה מקוונת המאפשרת למשתמשים לשתף ולהריץ סקריפטים של Python בענן Graphics Processing Unit(GPU): GPU, או יחידת עיבוד גרפית, הוא סוג מיוחד של שבב מחשב שנועד להתמודד עם המורכבות חישובים הדרושים להצגת תמונות ווידאו במחשב או במכשיר אחר. זה כמו המוח של המערכת הגרפית של המחשב שלך, והוא ממש טוב לעשות הרבה מתמטיקה ממש מהר. GPUs משמשים סוגים רבים ושונים של מכשירים, כולל מחשבים, טלפונים וקונסולות משחקים. הם שימושיים במיוחד למשימות הדורשות כוח עיבוד רב, כמו משחקי וידאו, עיבוד גרפיקה תלת-ממדית או הפעלת אלגוריתמים של למידת מכונה. מודל שפה גדול (LLM): סוג של מודל למידת מכונה שאומן על כמות גדולה מאוד של נתוני טקסט ומסוגל ליצור טקסט בעל צליל טבעי. Machine Learning (ML): שיטה ללמד מחשבים ללמוד מנתונים, מבלי להיות מתוכנתים במפורש. עיבוד שפה טבעית (NLP): תת-תחום של AI המתמקד בהוראת מכונות להבין, לעבד וליצור שפה אנושית רשתות עצביות: סוג של אלגוריתם למידת מכונה המבוססת על המבנה והתפקוד של המוח. שדות קרינה עצביים (NeRF): שדות קרינה עצביים הם סוג של מודל למידה עמוקה שיכול לשמש למגוון משימות, כולל יצירת תמונה, זיהוי אובייקטים ופילוח. NeRFs שואבים השראה מהרעיון של שימוש ברשת עצבית למודל של זוהר תמונה, שהוא מדד לכמות האור שנפלט או מוחזר על ידי אובייקט. OpenAI: OpenAI הוא מכון מחקר המתמקד בפיתוח וקידום טכנולוגיות בינה מלאכותית שהן בטוחות, שקופות ומועילות לחברה. Overfitting: בעיה נפוצה בלמידת מכונה, שבה המודל מתפקד היטב בנתוני האימון אך גרועים בחדשים, בלתי נראים. נתונים. זה מתרחש כאשר המודל מורכב מדי ולמד יותר מדי פרטים מנתוני האימון, כך שהוא לא מכליל היטב. הנחיה: הנחיה היא פיסת טקסט המשמשת לתכנון מודל שפה גדול ולהנחות את הדור שלו Python: Python היא שפת תכנות פופולרית ברמה גבוהה הידועה בפשטות, בקריאות ובגמישות שלה (כלי AI רבים משתמשים בה) למידת חיזוק: סוג של למידת מכונה שבה המודל לומד על ידי ניסוי וטעייה, מקבל תגמולים או עונשים על מעשיו ומתאים את התנהגותו בהתאם. מחשוב מרחבי: מחשוב מרחבי הוא השימוש בטכנולוגיה כדי להוסיף מידע וחוויות דיגיטליות לעולם הפיזי. זה יכול לכלול דברים כמו מציאות רבודה, שבה מידע דיגיטלי מתווסף למה שאתה רואה בעולם האמיתי, או מציאות מדומה, שבה אתה יכול לשקוע במלואו בסביבה דיגיטלית. יש לו שימושים רבים ושונים, כמו בחינוך, בידור ועיצוב, והוא יכול לשנות את האופן שבו אנו מתקשרים עם העולם ואחד עם השני. דיפוזיה יציבה: דיפוזיה יציבה מייצרת תמונות אמנותיות מורכבות המבוססות על הנחיות טקסט. זהו מודל AI של סינתזת תמונות בקוד פתוח הזמין לכולם. ניתן להתקין את ה-Stable Diffusion באופן מקומי באמצעות קוד שנמצא ב-GitHub או שישנם מספר ממשקי משתמש מקוונים הממנפים גם מודלים של Stable Diffusion. למידה מפוקחת: סוג של למידת מכונה שבה נתוני האימון מסומנים והמודל מאומן לבצע תחזיות על סמך היחסים בין נתוני הקלט והתוויות המתאימות. למידה ללא פיקוח: סוג של למידת מכונה שבה נתוני האימון אינם מסומנים, והמודל מאומן למצוא דפוסים ויחסים בנתונים בעצמו. Webhook: Webhook הוא דרך של תוכנת מחשב אחת לשלוח הודעה או נתונים לתוכנית אחרת דרך האינטרנט בזמן אמת. זה עובד על ידי שליחת ההודעה או הנתונים לכתובת URL ספציפית, השייכת לתוכנית האחרת. Webhooks משמשים לעתים קרובות כדי להפוך תהליכים לאוטומטיים ולהקל על תוכניות שונות לתקשר ולעבוד יחד. הם כלי שימושי למפתחים שרוצים לבנות יישומים מותאמים אישית או ליצור אינטגרציות בין מערכות תוכנה שונות.

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