In pharmacovigilance, post market surveillance and many other areas of drug development, highly skilled team members spend an enormous amount of time systematically mapping out publications. This includes going through clinical trial reports and real world evidence, and extracting key data points such as adverse effects.
In this webinar, we covered how to:
👉 Automatically filter based on entities of interest and specific context.
👉 Extract data such as adverse effects, treatment and patient baseline characteristics.
Starting a PhD degree is daunting. You’re probably beginning your first full-time job in academia and you have to write numerous papers in a research field you’re not fully familiar with. Day-to-day you’ll spend a lot of time discovering new knowledge, writing literature reviews and publishing papers, which will ultimately form the foundation of your PhD degree. But as a new researcher, it’s often hard to know where to start your research discovery.
At Iris.ai, we’ve spoken with our users about their literature review process. They gave us tips and tricks on how to improve the research discovery, which have helped them find more relevant papers, save time and organize research in visual maps.
Traditional keyword-based literature search is constrained to what you already know as a researcher, and finding the most relevant papers becomes an exercise in pairing the perfect match of words. That isn’t to say that keyword search is outdated, but that it must be coupled with additional powerful research discovery tools. Only then can you be sure you’ve found the most relevant papers.
4 steps to boost research discovery
So here’s a step-by-step guide on how to research like the pros (aka Irisians aka our users).
1. Find a research paper
Head over to our friends at PubMed, and look up an existing research paper or systematic review which covers the topic you want to discover. For example, on the image below I’ve searched for a systematic review for autonomous vehicles.
2. Copy the link
When you have found a paper, copy the URL or DOI of the article.
3. Search in Iris.ai
Go to your account at Iris.ai (available for free), and paste the URL or DOI into Explore. Iris.ai will now look for related articles.
To get a bit techy: Iris.ai builds a fingerprint of your chosen paper based on the most meaning-bearing words in the abstract and contextual synonyms and hypernyms. Then, Iris.ai matches the fingerprint against more than 200 million papers.
Reading a lot of research papers is daunting (and sometimes boring), we know. That’s why we built Iris.ai — to help researchers get an overview of the seemingly unsurpassable mountain of published papers, and seamlessly identify the relevant ones. Good luck exploring!
The development of innovative technology has reshaped the way we consume, access, process, and distribute information. Academic and research libraries are adopting new technology, searching to improve their services and competitive advantage. Artificial intelligence has been a major force driving this change, and in this article we are going to answer: how is AI shaping the world of libraries and researchers?
What is AI? And what are the different categories of artificial intelligence?
Artificial intelligence is a wide-ranging branch of computer science that attempts to build smart machines with human intelligence aspects. It’s so far one of the most complex and impressive human inventions but the field remains mostly unexplored and with huge growth potential. AI is divided into 3 categories:
Artificial narrow intelligence (ANI)
Referred to as weak AI with a narrow range of abilities, it is the only type of AI we have available for now. Artificial narrow intelligence is used in facial recognition, speech recognition/voice assistants, and driving cars.
Artificial general intelligence (AGI)
Referred to as strong AI with the ability to mimic human intelligence or behaviors to solve any problem. For now, strong or deep AI is not yet available but currently researchers are working on improving machines’ ability to see, understand, and learn as humans do.
Artificial superintelligence (ASI)
It is the hypothetical AI that surpasses human intelligence and abilities. It has always been a source of inspiration for science fiction in which robots take over the world. Having powerful and self-aware super-intelligent machines may be an exciting idea, but their impact on humanity remains uncertain. For now, there are still many years before artificial superintelligence will be achievable.
How AI will change the job of librarians
AI has been implemented in more and more libraries, and here are some ways in which AI will have a significant impact:
1. Content indexing
Up until today, indexing has been a tedious and manual task. It is done partly by publishers and partly by authors. Indexing provides an overview of the context in which the book, journal, or paper was originally thought up. However, indexing says very little about, for example, other fields the information could potentially be useful for, and human-made labeling and indexing is hampering interdisciplinary discovery. It also limits the literature’s ability to stay relevant over time because the indexing was done in a specific category in a specific context, and over time that context of what we know about the world will change.
AI tools for indexing will improve consistency and quality. It can identify concepts and assign them corresponding keywords. Index automation will also help the reader discover new literature and navigate through different disciplines, which is not applicable through manual indexing. This type of AI tools will surpass human capabilities in indexing by providing more specific and accurate material for the readers and as a result, help university librarians improve their job.
2. Document matching
AI machines are better at processing documents fast and accurately than humans. Thanks to automatic proper indexing, AI tools are now identifying similarities and differences between documents or patents. Matching documents with similar ones or connecting sections that are describing the same topics, solutions, or phenomena is now possible. When a document can be indexed based on its actual content, it means that you can compare the content of thousands of documents that are contextually relevant to the search topic. It can be limited to only sections of a document, such as certain book chapters or research paper sections. Then you compare the content in these sections to find exactly what you’re looking for in the literature rather than doing a five keyword summary in the indexing. It is an essential operation that helps researchers and libraries to get to their knowledge easier and faster.
3. Death of citation
The citation system can be perceived as a popularity contest, but it doesn’t do much more than providing a very biased overview of a researcher network. When doing research landscape mapping and literature reviews, it is clear that using the citation system for snowballing is not an ideal method for covering everything. AI algorithms, which are based on the actual content of papers, will create far better mapping systems of the actual research, and be of major help to librarians and researchers alike (as opposed to the network of researchers presented in the citation system).
4. Content summarization
Automatic content summarization is about condensing documents to a shorter version, independently from human interference, while preserving the key elements and the meaning of the original text. Instead of summarising the whole article or book, AI tools are able to summarize just a section of a book or five documents into three sentences. AI tools for content summarizations are already available online and gaining popularity as well as machine learning algorithms that are continuously improving this task.
There are two types of automatic summarization: extraction and abstraction
Extractive summarization depends on extracting sentences from the original text based on a scoring function. It selects the most important sections of the input based on the statistical survey and rearranges them together to produce a new condensed version of the document.
Abstractive summarization used advanced natural language techniques to produce a new summarized version of the document that is different from the original one. It aims at preserving the most important sentences while rephrasing them and incorporating critical information, like a human-written summary.
Most of the summarizations today use the extractive approach as it is easier and requires less linguistic analysis.
5. Quality of service
AI has penetrated the world of librarians and researchers in the form of chatbots that can answer directional or simple questions, alert when a new book is published, and direct a customer to specific library resources. The automation of conversations between a user and a machine will enable librarians to embed their focus on more difficult questions and save time answering repetitive ones. This will also enable libraries to extend the opening hours of both in-person and online services.
6. The Impact Factor of the Future
The impact factor is a measure of the relative importance and quality of the individual publication, journal, or researcher to literature. In the future, the algorithm will be capable of breaking down scientific research into arguments and validating them against other pieces of research. Or it could build for each document a truth tree of arguments and evidence, verify each branch, and then find the overall validity score. Having validated or rephrased research is more important than the number of its readers, as it is the solid and validated research that deserves a broader readership.
7. Better Operational Efficiency
Libraries can identify and magnify operational efficiency by improving service effectiveness and reducing operational costs with process automation, optimized research data management, and digital asset management (DAM). Implementation of machine learning in the library’s processes and digital resources can optimize collection analysis, visualization, and preservation, and reduce expenses associated with the provision of services. The adoption of advanced library service platforms can help in the development of operational efficiency.
The road ahead for libraries
Artificial intelligence is changing the information landscape while disrupting librarians’ traditional jobs. They are required to embrace AI not as a user but as an active leader to better serve the new upcoming generations. However, some reservations hinder the integration of AI in the world of libraries. The fear of being replaced by AI robots is totally understandable but we cannot neglect that advanced technologies will open up new horizons for librarians. It will help them maintain new innovative positions and roles, solve current challenges, and prevent them from becoming old fashioned. The focus on traditional tasks should be shifted to a new direction that embraces the advanced technologies and assists the upcoming generation with their evolving needs.
“There are many research tools that I recommend to my students, but Iris.ai is maybe the best one.”
Name: Josmel Pacheco Mendoza
Position: Researcher and Veterinarian
University: Universidad San Ignacio de Loyola
Region: Lima, Peru
My name is Josmel and I am currently working with multiple universities, besides being a partner of the editorial team of the Journal of Veterinary Research of Peru (RIVEP).
I found Iris.ai when I needed a tool to help me find the right documents for my COVID-19 research in a short time frame.
In one of the projects where I used Iris.ai, I worked with Mexican universities on two COVID-19 papers. There is a huge number of papers and patents published and available online, which makes it difficult and time consuming for my students and me to find the exact information that is related to my research.
How did Iris.ai help your research?
Iris.ai helped me to find the right collection of papers. One of the most powerful features is how you can add one paper into Iris.ai, and in return the software gives you a map of relevant papers. For me that’s like magic!
The Iris.ai tools also organize my work by clustering and filtering the papers. It is important for my work to have a map that organizes documents by their concepts and content.
Finally, the Iris.ai tools saved me and my students a lot of time, which is an important factor while doing research in medical fields.
It is exciting to use an academic research tool that relies on artificial intelligence to better serve students and researchers. There are many research tools that I recommend to my students, but Iris.ai is maybe the best one.
To get started with Iris.ai, sign up for a free account.
“Thanks to Iris.ai I found useful resources to build the thematic background of my paper. I ended up with around 20 papers that were immensely important and relevant.”
Name: Diego Raza Position: Lecturer – Research methodology
University: Universidad Andina Simon Bolivar Region: Quito, Ecuador
My name is Diego Raza and I am a lecturer at Universidad Andina Simon Bolivar in Quito, Ecuador. I teach subjects related to research methodology and help my students with their main thesis. When I was recently doing research for my literature review, I needed a tool to help me find research papers. I found that Iris.ai has higher quality sources in comparison to other tools on the market, and that it has access to papers that are of better academic quality.
How did Iris.ai help your research?
I was using Iris.ai for my own research in self-efficacy. I began my research by doing a broad exploration of papers in the Explore tool, then I imported the research map into the Focus tool to narrow down the results into a reading list. The tool helped me get an overview of the topic. Thanks to Iris.ai I found useful resources to build the thematic background of my paper. I ended up with around 20 papers that were immensely important and relevant.
Finally, I want to mention that I use Iris.ai to help my students identify literature that they may have overseen when preparing their main theses.
The biggest value with using Iris.ai, is how much time it’s saving. It’s doing a part of my work for me.
To get started with Iris.ai, sign up for a free account.
Map subscriptions, BibTex exports, patents, dark mode and algorithm improvements – and some changes to free and premium versions.
It’s that time yet again – last night we pushed out some new and exciting changes to the academic tools! There’s a long feature list below, some of which (including map subscription and BibTex export) are long time favourite feature requests. We hope you enjoy them!
The main change with this launch is in the logic of free and premium accounts. We’ve made some choices on this front driven by a few reasons: a) We’ve now properly established ourselves with an offering toward university libraries, and found a collaboration model that universities can afford and that we can live with. That means more and more users are coming in through University licenses and have access to the full premium tools, which makes us very happy! b) We see that the decidedly best experience one can have with the tools is to have access to it all. c) At the same time, we wanted to give free users the ability to get a feeling for all of the tools, not just the Explore tool.
In short, as a free user we’re limiting some more of the features in the Explore tool, but opening up the ability to try out the Focus tool as well – and we’re starting to Beta test an individual payment model, if you want easy access to all of the tools and features! (Send an email to email@example.com for more information)
With that said, let’s dig into the details. The ability to use the tools without being registered even with a free account is heavily reduced. You can engage with shared Exploration maps and Focus studies, but you can not create them nor edit them.
For free user accounts, we chosen to limit some features including the ability to input your own problem statement and search in patents. You will see the Focus tool now being available in your account, and be able to import single Explore maps into a Focus study.
If you urgently miss some of the features we have had to remove from the Free tools, or want full access to the features of your new Focus tool, there are two ways to go about it: 1) Talk to your librarian about getting the entire university/department access to our tools or 2) Contact firstname.lastname@example.org to inquire about individual premium access.
Dark mode: Let’s face it – we’re all different, and we figured we’d add a neat little feature for those of you who prefer the darker side of life. Toggle on dark mode on top of the left hand menu for a new Iris.ai experience.
Subscribe to maps: Have you built the perfect map that contains state of the art of what you need, and would love for us to let you know whenever new relevant content is found? It’s one of our most requested features and it’s here! Subscribe to your favourite maps and receive notifications whenever the content change with new relevant papers.
Easier content selection: Do you want to search by patents, papers, or both? This is now a lot easier to choose – either as you input your research question or starting paper, or in the map itself, which will regenerate the map.
Export your map results: Another highly cited request – this feature allows you to export the list of papers in your result map as a .csv file, or in BibTex format for uploading to your favourite reference manager system.
Bye-bye, TED talks! When we started building Iris.ai, we found a fun way to build something that made sense and demonstrated our intention and vision – but that didn’t require the full capability of reading millions of research papers. The concept we launched early 2016 was “see the science around a TED talk” and allowed you to pop a TED link into the tool, Iris.ai would machine read the script and show you a science map. Years later, while we will care deeply for our little hack, we figured it was time to retire support for this practically unused feature. So goodbye, little TED-Science-tool, it was a pleasure having you with us!
Algorithmic and capability improvements. We keep on improving the core engine, and the new improvements this time around are connected to an improved keyword extractor module (using tf-idf to propose candidates for key words, and then topic modeling to evaluate the relevance of those candidates) and the first part of better word disambiguation (w2v, currently disambiguating on part-of-speech, which helps finding better synonyms for e.g. words that are both noun and verb or other variations), and a new algorithm for hierarchy building to create smarter concept maps for the Explore tools (uses not only topic information and similarity, but also generality and concept dependencies to form the final output), freshly tuned hyperparameters that give better results overall – and some general system performance upgrades and updates.
The world needs science. Complex challenges ranging from climate change to preventive medicine require us to put our best minds together to solve them. And we do live in a world where more scientific knowledge is available to us than ever before — but the irony is that our politicians doubt its legitimacy, our researchers are pressed for time and resources do not have capacity to communicate across even the closest alleyways of a university, publishing houses generate profit by keeping vital results hidden behind heavy walls, and go after those who breach them with deadly force. In addition, the big software players are opaque and seemingly impossible to hold accountable for their data, their algorithms and the implications of these. In spite of Tim Berners-Lee creating the internet to share scientific knowledge, it seems we have only come marginally further today than we had back then.
Two years ago at Iris.ai we sat out on quite the ambitious journey: to build what we call an “AI Scientist”, a system that can augment our human intelligence by connecting the dots of all of the world’s research. The months since have been filled with hard work, progress, setbacks, a lot of rejections — but also so much love, support, understanding and encouragement from our wonderful community.
In these two years we’ve built a system that reduces a human’s time to map out existing scientific knowledge with up to 90% , while increasing serendipity and interdisciplinary discovery. This is the first important step towards what we call the Knowledge Validation Engine, a core feature of the AI Scientist. We have a dedicated team that has built this, we have a number of budding university collaborations and we have a group of lovely investors who believe in us and we’ve published several open access research papers. Most important, this past year we’ve seen an amazing community of AI trainers grow up around us — more than 8,000 individuals who volunteer their time to help Iris.ai learn. We’ve seen a desire to be part of our journey, a wish to help us achieve our mission, a community coming together to tell us that what we do is important. We have done our best to honor their help, but we have not done enough.
Transparency, openness and fighting bias have been our core values from day one, but we find ourselves not living up to our own standards. We find ourselves torn between servicing big corporate clients and satisfying a European venture capital community single-mindedly focused on revenue (with some very honest impact focused exceptions) on one hand — while also trying to bring what we build out to as many people as possible.
For us to truly make impact in the world, it is not enough to build some great tools, we need to disrupt and uproot an entire industry. We can not do that on our own — it’s a grassroots challenge. We need your help.
Scientific knowledge is arguably the ultimate decentralized application. It is not controlled by a central agent, is individual node-independent, is exposed to public scrutiny and constant challenge and is preciously valuable for a large and fast growing cohort of current and future users.
The technology development of this decade is thrilling, and today we are taking advantage of a new opportunity. Utilizing the decentralized nature of the blockchain, we have decided to give power to our community by tokenizing our functionalities— allowing anyone who contributes to the tool to generate tokens by doing so — tokens they can later use directly to access our core services.
An AI Trainer who annotates research papers, a coder who commits to our increasingly open source code, a user who reports a bug or a researcher who uses the Iris.ai Knowledge Validation Engine to publish their research Open Access — they will all be rewarded with tokens for contributing. The tokens can be used to access any of the Iris.ai tools. All token holders will have a voice, have transparent insight into our core technology and will be asked to hold us accountable to openness and de-biasing our algorithms and data. And as both corporates purchase access to the Iris.ai tools and the the algorithms of Iris.ai improve over time, the value of the tokens held by our community will increase.
We’re excited, thrilled and a little scared — as per usual, we’re traversing unchartered territory. And we can not do this alone. Please join us in making science transparent, open and accessible.
Our white paper with the details will be made available early 2018. Until then we would love your ideas, thoughts and feedback — on email@example.com or our Telegram channel.
Gaining context for the research problem we’re trying to help solve
Last week, we hosted a Scithon in collaboration with Stryker, Augmented Vision and Freiburg University Medical Center. We put a group of medical engineers, IT experts, neurosurgeons and urologists in a room to discuss and research methods for advancing Augmented Reality/Virtual Reality in surgical settings. What made this particular group of Scithon Participants unique was the high level of expertise related to the research topic subject matter. Even before beginning to research the feasibility of AR/VR surgical innovations, almost all of the doctors already had a good baseline of specific features or use cases that would be most beneficial to them. And the views between neurosurgeons and urologists regarding the usability and implementation of innovative technology in the Operating Room varied greatly. It seemed clear that no matter the outcome of the day’s research, the feasibility of using AR/VR in the OR wasn’t the biggest obstacle to overcome. So, before we outline the results of the research, we want to the highlight a few of the learnings from the discussions that took place over the course of the Scithon. These learnings provided valuable context for the day’s research and gave us better insight into the pain points behind the research question.
Coming into the Scithon, it was interesting to see what types of technologies the doctors had begun to familiarize themselves with and which ones they preferred. One of the urologists touted his ‘bootstrapped’ method of incorporating Google’s Cardboard into his work while other teams of neurosurgeons already begun investigating how best to use Microsoft’s HoloLens. Other doctors expressed frustration at the speed of their colleagues’ ability to embrace and adapt to new technologies. And still, others, questioned the practicality of implementing a new technology that will need to be replaced in 3 years or even worse, could be obsolete in that same period of time. Even with the ability to use groundbreaking technology, there are other issues creating barriers for implementation. Hospital budgets, time spent training doctors, access to up to date research and data, IT maintenance, etc are all factors in delayed or even non-use of state of the art technology.
How residents and doctors time is spent was a recurring theme mentioned throughout the day. The finite preciousness of doctor’s time is a never ending issue. Several participants stated it was difficult being able to attend today’s Scithon, let alone focus on their own research. The sacrifice of their time did pay off, as all they agreed that the Scithon was a valuable use of their time. It allowed for a ‘meeting of the minds’ type atmosphere facilitating extensive discussions among a range of medical specialties. And many were interested in the long-term possibilities of using Iris.AI’s platform to expedite their institution’s research. Ability to save time was a vital feature that all participants were interested in, regardless of specific technology.
We gained numerous insights from the medical technology provider and manufacturer group of participants as well. We could relate to them as we were both in the position of watching, in real time, others using our products and platform. The participants from Stryker shared a few industry insights like Microsoft’s shift from AR back to their primary Cloud based product offerings and how that might affect their future involvement with the AR field. They also discussed a slight gap between product features and product implementation in the medical field, as some doctors had mentioned their products were too complex for what they needed. Simply put, the current technological solutions were overbuilt. The feedback is at the forefront of their minds as they continue to create products for the medical community.
They say if you build it, they will come it. Well, now the question is, can they build it? Does AR/VR have a place in surgical settings and if so, how can it best be implemented?
Can Virtual and Augmented reality improve the medical training process?
Virtual Reality & Augmented Reality are changing the world as we know it, every day we are discovering new uses for these innovative technologies. The medical field is one that is especially excited to see the applications of VR and AR evolve, as the implementation of these frontier technologies could improve medical education considerably. The nature of practical medical training has changed minimally over the last decades. However, the feasible application of VR & AR for medical education purposes could modernize the training process for medical students, requiring less training performed on patients. Understandably when education for treatment methods improves it allows for medical staff to address the needs of patients in an increasingly comprehensive manner. And it It is these notions that form the basis for our upcoming Scithon.
Next week researchers from the Division of Urotechnology, University Medical Center Freiburg, in cooperation with the Department of Augmented Vision, German Center for Artificial Intelligence, and the Stryker Stryker Leibinger GmbH & Co. KG, will gather to compete to find the best approach to implementing these technologies. VR & AR are ever growing fields, with new information published daily, so the teams will us Iris.AI to find the most relevant papers to this query. Because Iris.AI shortens the amount of time needed for research discovery, it enables teams to move to the next stage in research & development more quickly. This correlates to identifying new solutions and avenues for treatment of patients more quickly which can ultimately save lives.
We are excited to put Iris to work on such a considerable task with far reaching results and benefits. We look forward to sharing these promising results with you next week.
Hitting a roadblock in your research? Struggling with the drudgery part of the R&D process?
Here at Iris.AI we thrive on addressing multifaceted issues head on and love being able to use our AI to help others elevate their innovation process. In fact, we love it so much that we are dedicating our summer to do just that by running a tour of Scithons in London, Stockholm and San Francisco in collaboration with Founders Factory.
Each Scithon (think of Hackathons for research!) is a day-long research sprint where teams of driven researchers use Iris.AI to summarize the most relevant research on an R&D challenge in one day. Teams, invited to the event by Iris.AI, compete to win a cash prize by pioneering a tangible solution to the question at hand.
At the end of the day you will receive the winning team’s solution which will contain:
– A full overview of all identified cross-disciplinary research on the challenge
– Their conclusion on how to go about solving the problem
– The key papers outlining this solution.
– You will also have contact details of the team’s participants in order to continue the dialogue with them.
We are now looking for innovation-driven organizations to join us for the sprint in one of the three cities. Simply send us your research question. We’ll take care of everything else including the sourcing of multidisciplinary and driven researchers.