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.
Iris.ai gives students, researchers, and engineers alike, the tools to realize and actualize their (spatial) dreams. And in this case, dreams of making aerospace history.
Two months after SpaceX’s Dragon launch mission, Iris.AI hosted a science hackathon or Scithon as we like to call it, in partnership with the leading composite materials research institute Swerea SICOMP. At the Scithon, we challenged researchers to find solutions to this question, “Can you create a reusable rocket using composite materials?” A concept that SpaceX has been pursuing for 15 years while the research community has been exploring such possibilities for much longer than that. Yesterday’s historic launch was the first step in achieving that milestone by attempting to mimic the reusable nature, lifespan and quick turnaround of planes used for air travel.
Before launch, SpaceX engineers pointed out that there are a thousand ways to fail, but only one way to succeed and identified the areas of possible failure; manufacturing error and material fatigue and wear out. The areas identified by SpaceX were a generalization of the known obstacles at the time of the Scithon; composite performance issues at extreme temperatures, limited durability against UV exposure and space radiation, and chemical resistance issues with rocket fuels. Over the course of the Scithon researchers uncovered hundreds of papers to support their claims around creating reusable rockets. One of the conclusions reached suggested that nanostructured coatings provide better high-temperature performance and corrosion protection, based on this paper on the development of radiation shielding of composite space enclosures as a core element of their solution.
The Falcon X launch was supported by multitudes of research, some of which is similar to the conclusions of the Scithon teams. Two factors aiding to the success of the mission were the ability to quantify the catalytic properties of reusable thermal elements and simulating data for the design of high-temperature composites, method designed by NASA. And both of these principles relate to the conclusions reached by one of the Scithon teams.
The success of Falcon X coupled with the Sciton results argues that using Iris.AI individuals with no direct background expertise were also able to conduct innovative and groundbreaking research, often reaching similar conclusions to those of dedicated scientists, engineers, and researchers.
Addressing the challenge of achieving healthier lifestyles
Last week, we were at the Lapland University of Applied Science, in Finland, where we held a Science Hackathon in collaboration with Hotus and Skhole Oy. The goal of this intensive day long research sprint was to identify the most effective interventions to sustain healthy lifestyles in healthcare. During the sprint, teams of students and researchers collectively sourced over 200 research papers. As a single source, Iris.ai accounted for more than half of all papers. The final results showed that the more time the team spent using Iris.ai, the better it performed during the Scithon.
Each year noncommunicable diseases (NCDs) kill roughly 38 million people, according to World Health Organization estimates. We are well informed about the cause of those deaths, which are driven primarily by four major lifestyle factors: 1) tobacco use, 2) lack of physical activity, 3) the harmful use of alcohol and 4) unhealthy diets. Howeve, we lack extensive information on the most impactful health care interventions to prevent NCD’s.
Working in collaboration with WHO, Hotus, the nursing research foundation, and Skhole, a healthcare eLearning platform, strive to produce reputable evidence-based frameworks and teaching materials to better inform clinical decision-making. NDC’s are one of the major areas of focus for both organisations. Therefore, to accomplish impactful frameworks, researchers devote countless hours to analyzing hundreds of research articles, a daunting task for human minds. The artificial intelligence of Iris.ai is optimized for precisely such tasks. This created a great opportunity to co-host a Scithon.
“Our plan is to create a nursing guideline and evidence summaries related to impacting lifestyles in healthcare. I think that the results from the Scithon are a great starting point for that work. We will use the material as one of the sources for the drafting process.” — Virpi Jylhä Researcher, Hotus Nursing Research Foundation
At the Scithon, seven teams comprised of cross-disciplinary students, researchers and professionals gathered to identify impactful measures that enable healthy lifestyles. The teams were asked to use Iris.ai exclusively for the first hour and then were welcome to use any outside research tools of their choice. To find the best possible solutions to the problem statement, the teams were evaluated on both qualitative and quantitative fronts. Judges looked at the team’s research strategy, as well as the quality of their findings and insights (e.g., Did they follow current research trends? Was their conclusion well-supported?). Additionally, the number of papers they found, as well as the relevance of those papers, was factored into their scores.
“What surprised me and the other judges was the versatility and the scope of the results. The teams managed to find a wide range of relevant knowledge within a short period of time.” –Virpi Jylhä Researcher, Hotus Nursing Research Foundation
The winning team, Mindhack, concluded that change is a process, not an event. The team is comprised of Annika Lehmus-Sun, a Master student at the University of Helsinki, Johanna Töyräs, a student Nurse at the Lapland UAS and Niko Männikkö, Ph.D. Candidate at the University of Oulu. They asserted that both proper stress management and healthy gut bacteria contributed to a healthier lifestyle. A substantial portion of their conclusion was based on research from Plos One on the combined impact of lifestyle changes in body weight and Jama Internal Medicine’s paper on meditation programs for psychological well-being and stress.
Coming in second was Team Etelä-Savon Digiloikkarit, consisting of team members Santeri Seppälä, Licentiate of Medicine, Mikko Lampi, M.Cs. Information Systems, Anu Salpakoski, Doctor of Health Science, and Pirjo Hilama, a Master student at the University of Eastern Finland. Rounding out the top three was Anagrammi Imperfektissä whose team members included Aarni Karjalainen, Markus Sillanpää and Ville Vilén, all students at Lapland UAS.
Based on the efforts of the seven teams, it was evident that more the maps the team created, the better they fared in the competition.On average teams spent a quarter of the allotted time using Iris.AI, with the winning team spending over half of their time, on the tool. The overall average number of papers found by all teams was about 30 and when looking at the top three teams the average jumps to 48 papers. In total, over 85 research maps were created during the Scithon. The winning team generated the highest number of maps, 18, further proving that the increased use of Iris.ai increases research productivity.
Let’s face it: changing lifestyles is hard. Major lifestyle diseases, including adulthood diabetes and heart disease, take way too many lives each year. The problem is not the lack of information in healthcare, but fragmentation and poor implementation of information.
When we first sat down a couple of months ago with Skhole Oy andthe Nursing Research Foundation, we quickly came to realize the breadth of opportunity for collaboration to address the massive data challenge in healthcare, including the one mentioned above.
The mission of the Nursing Research Foundation is to promote the effectiveness of nursing by developing evidence-based guidelines. To ensure that the most relevant knowledge is the core of those guidelines, the Foundation’s researchers spend thousands of hours skimming through research articles each year with tight deadlines. The sheer volume of papers published every single day makes it a tough task, one that our human brains are not optimized for.
The good news is that the artificial brain of the learning machines are. The Iris.AI tool is built to automate the drudgery part of the research process including surfacing articles, finding structure in that data, and providing human experts with the key findings. And the benefit of all that? Well, researchers and students can focus on understanding the most important contents instead of spending hundreds of hours searching for them.
Jumping back to the initial question of impacting lifestyles, we are excited to announce our next Scithon to be held in collaboration with the Nursing Research Foundation, Skhole Oy and the Lapland University of Applied Science. The goal of the 12-hour research sprint is to identify the most effective interventions implemented to sustain healthy lifestyle in health-care.Iris.AI will be one of the tools made available to the teams of students and researchers tasked to map out the relevant research content in 12 hours.
We are currently looking for participants with a curious mind from across research disciplines to participate the sprint. Cash prizes of 3000€, 1000€ and 1000€ will be awarded at end of the day to the TOP 3 teams selected by the Scithon jury.
Helping researchers with problem validation at the Helsinki Challenge
Recently we were asked ‘what are the happiest moments in an entrepreneur’s life?’ For us at Iris.AI, one of the ways we experience those moments is when we are able to watch researchers finding something useful with the AI tool we built.
This is why the past two days have been so special.
We organized a two-day Science Hackathon, or Scithon as we call it, in collaboration with Helsinki Challenge for a group of 40 researchers working to solve major global problems. The Scithons help R&D focused companies and research institutions address their scientific research challenges in a compressed time frame.
One of the things that sparked our enthusiasm to work with the Helsinki Challenge is their mission. Working off of the framework of the UN’s 2030 Agenda for Sustainable Development, the Challenge focuses on three major themes; Sustainable Planet, Urban Future and People in Change. Partly idea accelerator, partly science-based competition, the Helsinki Challenge helps the 20 teams working on reaching the SDGs to pull off their projects.
Over the course of the week, we met over a dozen brilliant teams dedicated to finding solutions to societal issues like access to diagnostic healthcare services, eliminating malaria, addressing loneliness and isolation in youth and creating eco-friendly textiles for the growing population.
The Helsinki Challenge teams used Iris.AI to conduct multidisciplinary research and explore the connections of different fields. Some teams found new topics and concepts that they might not have not initially thought of.
Here’s what a few of the teams had to say about using the tool.
“I do a lot of multidisciplinary research and don’t always know the keywords for those topics or industries. Iris.AI showed me concepts that might not be intuitive and I was able to get a bigger picture of the topic.” — Reconfigure Mobility
“I like the way the results are presented, it’s great. The data map is very appealing” — Futurena
“I like that I can input a larger block of text, rather than just a few keywords, when searching for relevant articles” — Heatstock
The research teams weren’t the only ones who learned a lot this week. We did too!
The Helsinki Challenge teams gave us valuable feedback about the product and the user experience. It’s also clear that Iris.AI still has a lot to learn!
Lastly, we learned that these teams aren’t just researchers and scientists, they are also social entrepreneurs. And for most entrepreneurs, there is great joy in seeing your creative solution come to life. We wish these teams the best in their scientific, entrepreneurial problem-solving journey and hope that each of them gets to experience their own moment of happiness.
Our co-founders Anita and Victor just got off the stage from TechCrunch Disrupt’s Battlefield! During which they announced a new model, launching in 2017, for solving corporate R&D quests through Science Hackathons (or Scithons™).
In a Scithon™, groups of interdisciplinary researchers compete using Iris.AI’s AI-powered science assistant to quickly map out and digest the relevant research around a given challenge.
If your company wants to be among the first ones to apply a combination of human and AI intelligence to solve open-ended science challenges, contact jacobo@iris.ai.
Better move fast, we’re nearly sold out for the next 8 months.
On Friday we held our second Scithon, in Stockholm, in partnership with the Stockholm Resilience Centre, an international center focused on trans-disciplinary research for governance of social-ecological systems, and Future Earth, a research initiative on global environmental change and global sustainability.
Four teams of interdisciplinary researchers, randomly assigned, tackled two different research questions, with associated lengthier problem statements, around biosphere positive impacts:
Teams 1 & 2 were asked: In which time frame can global urbanization become biosphere positive?
Teams 3 & 4, on the other hand, dug into: Can biosphere positive fibres and textiles clothe the world?
Over a four hour timeframe, teams 2 & 4 used Iris.AI as their only research discovery tool. Teams 1 & 3 only used Google Scholar.
While we wait for the jury to evaluate the performance of the different teams, which will require more work than in our previous exercise, we were able to draw some interesting conclusions:
– Teams using Iris.AI obtained a broader perspective over the problem space.
– The problem statements were probably too broad for the allotted time frame.
– Scientific research, particularly around the global urbanization and the biosphere, was scarcer than optimal.
– General familiarity with the sustainability space flattened the difference in the performance of the two tools.
We will report more on results as soon as we hear back from the jury!
Passionate about solving global sustainability problems?
Interested in artificial intelligence and its potential to support interdisciplinary science?
Curious to test out tools that are being developed on that front?
Yes??
We want to you to join our next Scithon! This collaborative event, coordinated with the Future Earth Media Lab and Stockholm Resilience Centre, will be held on October 28th in Stockholm.
Questions??
We’ve got answers!
What: This Scithon, aka Science Hackathon, is a 6-hour sprint focused on exploring solutions to global sustainability.
Goal: To map out solutions to a pre-determined research problem and to assess the usefulness of various research tools in conducting fast and effective research mapping exercises.
What you’ll be doing: Search the academic literature to answer a broad multidisciplinary question using our tool.
About our collaborators: Future Earth Media Lab creates pioneering, experimental approaches to connect science to society. It is part of Future Earth is a research initiative for global sustainability.
Stockholm Resilience Centre is an international center that advances transdisciplinary research for governance of social-ecological systems.
How to build a rocket with composite materials? Together with the leading European research institute Swerea SICOMP and Chalmers University we organised a science hackathon, or scithon, as we call it. The goal of the 4-hour sprint was to map out solutions to this space challenge and, in the process, get a grasp of where Iris.AI 2.0* stands compared to traditional science discovery tools.
On September 20th two teams of cross-disciplinary Masters and Ph.D. students from fields spanning from mechanical engineering and industrial design to computer science, astrophysics and entrepreneurship were handed a research challenge: Is it possible to make a reusable rocket made completely out of composite materials?
This challenge provided by Swerea SICOMP is particularly difficult due to issues like the performance of composites at extreme temperatures, the limited durability against UV and space radiation, chemical resistance issues with rocket fuels, and oxidation in high concentrations of oxygen.
After introducing the challenge and the rules of the game, the teams were pitted against each other. They both had four hours to achieve two goals: (1) map and categorise related scientific articles; and, (2) summarise the key findings by skimming through the categories and papers. Only one of the teams had access to Iris.AI.
The specific criteria they would be evaluated on were the relevance, breadth and completeness of the research papers identified. Teams’ work was also assessed based on the quality of the conclusions drawn, including elements like issues surfaced, key trends and current research directions identified.
After the sprint, an expert panel evaluated the results obtained by both teams. Team 2, using Iris.AI as the tool, generated a score of 95%.Team 1, using the current market standard product, scored 45%.
The number of generally relevant papers identified was similar for both teams. The different angles covered by these papers (with categories like validation research vs. evaluation research vs. solution proposals vs. philosophical papers vs. opinion papers vs. experience papers) was broadly similar, too.
The scithon jury attributed a significantly higher score to the Team 2, i.e. the team that had used Iris.AI, on three accounts: (1) finding three papers with a top score in terms of fitting the problem statement; (2) showing higher quality of key findings structured around identified topics; and, (3) drawing superior conclusions and summarising the relevant knowledge.
While the team using an existing market standard tool struggled to formulate the relevant keywords to optimise their searches, i.e. facing issues around dated terminology, members of the jury from our co-organisers Swerea SICOMP were particularly impressed by the papers identified by the team using Iris.AI. More specifically, the team using Iris.AI found papers around silicon-based nanoparticles and a distributed health monitoring system for reusable liquid rocket engines. These two key avenues of research could bring us a lot closer to building reusable rockets made of composite materials!
This means that version 2.0 of Iris.AI, with its full text search, unbiased mathematical architecture, neural topic modelling and visual navigation interface features, is beginning to show significant value added for researchers looking to speed up the effective discovery and deployment of scientific research.
The scithon also allowed us to gather invaluable feedback from researchers around the importance of features like filtering (including search criteria refinements) and interaction (including discarding concepts presented by Iris.AI in results maps), which will be included in our near term product roadmap.
The next scithon will be organised on October 28th in Stockholm in collaboration with Iris.AI and Future Earth. If you are in the area and would like to join us to identify solutions to climate change, contact Maria at maria@iris.ai.
*The new version of Iris.AI will be launched on the 22nd of September. Be the first one to hear about it by signing up to our newsletter at www.iris.ai.
Interested in having a look at the Scithon material? Here’s the Dropbox link to view the results delivered by both teams as well as the full version of the problem statement.