Impact AI

In the traditional paradigm, it can take up to ten years for a drug to come to market. For this episode, I am joined by guest Aaron Morris, Co-founder and CEO of PostEra, to talk about using AI to accelerate medicinal chemistry and bring cures to patients faster than ever before.Aaron breaks down the medicinal chemistry process and explains how PostEra applies machine learning to drug discovery. The data landscape within drug discovery is particularly challenging and today, we learn about PostEra’s approach to gathering data, the data sets they build from, and how they find new uses for project-specific data. Hear about the importance of model interpretability and how to get a competitive advantage as an AI-powered startup.Key Points:Aaron Morris’ background in mathematics and how it led to the creation of PostEra.The scientific disciplines involved in developing a drug.PostEra’s focus: building the world’s most advanced ML platform for medicinal chemistry.Aaron explains the process of medicinal chemistry.How PostEra applies machine learning to the drug discovery process.The challenging data landscape within drug discovery and the data sets PostEra builds from.PostEra’s approach to gathering data, and how they use it.The challenge of finding new uses for project-specific data.How PostEra validates its models.Why PostEra makes its models less black box and how they go about it.The importance of model interpretability and how PostEra develops interpretable ML.Aaron’s advice for other leaders of AI-powered startups.His vision for PostEra’s impact in the next three to five years. Quotes:“Though being reasonably competent on the machine learning side, I had a very, very steep learning curve when it came to getting up to speed with drug discovery chemistry and the applications of AI in that domain.” — Aaron Morris“Drug discovery is going from biology to chemistry to medicine and PostEra squarely focuses, at least for now, on the chemistry angle. Our main focus is to build the world’s most advanced machine learning platform for what is referred to as medicinal chemistry.” — Aaron Morris“PostEra is really the first AI company to pioneer machine learning across all three stages of how to design a molecule, how to make the molecule, and how to select the optimal set of molecules to test.” — Aaron Morris“There is a lot of project-specific data that gets generated, and often what that means for PostEra is we’re having to be very inventive about how we try to get the most out of data even if it is not relevant.” — Aaron Morris“If you want to build defensibility as a company, you have to have more than just innovations on model architecture.” — Aaron Morris“Your typical drug today is taking anywhere between eight to ten years to come to market and obviously, we want to really accelerate that.” — Aaron MorrisLinks:Aaron Morris on LinkedInAaron Morris on TwitterPostEraPostEra on TwitterResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

I am excited to welcome the Head of AI at Precision AI, Amr Omar. Precision AI has recently taken to agriculture by using drone technology to deliver precise herbicide doses to crops that are in need.In today’s episode, Amr explains why this technology is crucial for the future of farming and how machine learning factors into the process. From being at the mercy of the weather to not being able to distinguish between good and bad crops when they are seedlings, there are many challenges involved with using drones and AI technology for farming, and my guest lays them all out whilst explaining the solutions that he and his team have come up with.You’ll learn about Amr’s process for developing new machine learning products and features, the non-negotiables he prioritizes in state-of-the-art reviews, what he looks for when building a successful team, his advice for other AI startup leaders, and so much more!Key Points:Introducing Amr Omar as he explains how he ended up as Head of AI at Precision AI.What Precision AI does and why this work is so important for farming. The role of machine learning in Precision AI’s technology. Challenges that arise when using drones for farming, and how Amr’s team overcomes them. How Amr makes the drone models generalizable without sacrificing other restrictions. A look at his process for developing a new machine-learning product or feature. What Amr and his team look for and prioritize when doing state-of-the-art reviews.The approaches to recruiting and onboarding that have been successful in building his team. How he measures the impact of his drone technology: the field test. Amr shares some advice for other AI-powered startup leaders.  How he sees Precision AI impacting the market in the next three to five years. Quotes:“What we offer here at Precision AI is putting only what needs to be sprayed in real-time speed, using drones to kill those that are unwanted, which eventually saves a lot of money. At the same time, it increases the value of the crops coming out of that process.” — Amr Omar“The flexibility to pivot within the development of a certain feature is what empowers any team that's developing AI-driven applications or products to scale and succeed without facing any unexpected challenges.” — Amr Omar“Most [problems have] solutions. It's just about how much you are willing to invest in that solution versus the value you're going to get out of that.” — Amr Omar“We all have this dream big mentality at Precision AI, from the leadership to the junior engineers. We all wish to make something big happen with what we are doing. To be able to achieve that, you need a team of believers.” — Amr Omar“Bet on the process [and] not on the product while working in machine learning or in AI-driven teams. The process is way more important than the product. The product will come at the end of the day.” — Amr OmarLinks:Amr Omar on LinkedInAmr Omar on Twitter  Amr Omar EmailPrecision AIResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Today on Impact AI we welcome the CEO and Founder of AI healthcare company Kepler Vision, Harro Stokman. Kepler Vision is using computer vision to aid the healthcare world in recognizing falls in elderly patients, and Harro explains why the specificity of this focus is such a strength for the company.Using computer vision and an ever-growing dataset to perfectly detect situations where personnel is needed is no small feat, and answers the staffing issues often associated with care facilities during the night. In our chat, Harro explains some of the technical aspects of the software and the major improvements he has overseen recently before going into some connected topics such as privacy concerns, hiring practices, and validating the accuracy of the models. Harro is also kind enough to offer some general comments and advice regarding AI startups, and the areas he believes are most vital for founders to attend to.So if you would like to hear about a great practical application of AI in the healthcare space, and some thoughts from a leader making waves in some uncharted waters, be sure to listen in with us!Key Points:Harro talks about his academic and professional background and his companies before Kepler Vision. The specific problems that Kepler Vision is solving.  Understanding the role of machine learning in Kepler Vision's service. Harro shares the biggest challenges that he and his company have faced.  The task of building trust and the hiring practices that contribute to this.Validating the accuracy of models; Harro unpacks the labor-intensive process. The improvements that have been made to the software through iterative updates.Measuring the impact of the software; Harro talks about customer satisfaction. Advice from Harro to AI startups about hiring and focus. Harro shares his vision for the next five years at Kepler Vision and where to find them online.Quotes:“Over time, we added more and more examples to our training sets, and we are now at a phase where our software pretty much works out of the box actually.” — Harro Stokman“So in the field of elderly care and hospital care, our software can look after the wellbeing of elderly clients and that is all we do and we do nothing more. But what we do, we do incredibly [well].” — Harro Stokman“We have stayed faithful to the healthcare vertical. So my advice would be to focus.” — Harro StokmanLinks:Harro Stokman on LinkedInKepler VisionResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Can AI be used to solve major planetary problems? Today I'm joined by the CEO and Co-Founder of Astraea, Daniel Bailey, to talk about leveraging geospatial data for sustainability pursuits. Astraea's platform uses satellite imagery and AI to enable customers to access and operationalize spatiotemporal insights across multiple industries including clean energy, agriculture, conservation, carbon finance, and real estate.Daniel fills us in on the issues Astraea aims to solve and the role of machine learning in its mission. We find out what makes satellite imagery unique (and uniquely challenging to work with) and how Astraea ensures that its models continue to meet customers’ needs over time. Daniel shares insight into the ML development process and advice for other leaders of AI-powered startups. Tune in to discover the balance between model accuracy and explainability, the importance of transparency when it comes to voluntary carbon markets, and more! Key Points:Daniel Bailey’s background and how it led him to create Astraea.What Astraea does; the planetary problems it aims to solve.The role of machine learning in Astraea’s technology.The insights Astraea extracts from satellite data and the models they use to do so.What makes satellite imagery unique (and uniquely challenging to work with).How Astraea ensures their models continue to meet customers’ needs over time.The balance between model accuracy and explainability.Astraea’s ML development process.The first steps to solving the business case with ML.The importance of involving stakeholders in the development process.Daniel’s advice for other leaders of AI-powered startups.Why it’s critical to stay focused on the business needs.The training data required to meet global needs.Daniel’s vision for the future impact of Astraea.Quotes:“We're in this golden age of measurement. There's more data than you can look at individually. You really have to have something like AI/ML to recognize those patterns and extract those valuable insights from the data.” — Daniel Bailey“Satellite imagery is a unique beast, for sure … The dimensionality of the data is completely unique.” — Daniel Bailey“We do champion challenger techniques so that when we have a model in production, we're constantly looking for a better model and innovating on that capability.” — Daniel Bailey“Without transparency, the voluntary market will collapse. We will never reach our goal of a two-degree Celsius without the voluntary carbon markets that are by nature a deregulated marketplace.” — Daniel Bailey“When we think about creating ML products and features within the product, we think about using the most simplistic approach first.” — Daniel Bailey“In the geospatial AI space, it is going to take a community to provide the capabilities we need to resolve some of these intractable problems we're facing as a planet.” — Daniel Bailey“We need more training data to build better models to meet the needs that we're seeing globally.” — Daniel BaileyLinks:Daniel Bailey on LinkedInDaniel Bailey on TwitterAstraeaAstraea on LinkedInAstraea on TwitterResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

In the words of Dave DeCaprio, “We need to move from a reactive healthcare system to a proactive one.” Dave is the CTO and Co-founder of ClosedLoop, a data science platform for healthcare that is using predictive AI to make this crucial shift.In this episode, we learn about the problem he identified in the healthcare system that he felt he was uniquely set up to solve given his background, and how ClosedLoop is working to solve it. Dave shares use cases for ClosedLoop’s predictive models and the challenges he’s encountered in applying predictive modeling to health data. We find out why model interpretability is so important and learn about the role of human mediation in ClosedLoop’s applications. Dave explains the ways in which biases manifest in the world of health data and how ClosedLoop measures and mitigates bias. To find out how ClosedLoop measures its models over time, as well as the impact of its technology, tune in! Dave closes with some astute advice for other leaders of AI-powered startups and his vision for the near-future impact of ClosedLoop.Key Points:Dave DeCaprio's background and how it led to the creation of ClosedLoop.The healthcare problem he felt he was uniquely set up to solve.What ClosedLoop does and why it’s important for healthcare.Use cases for ClosedLoop’s predictive models.The challenges of working with health data and applying predictive modeling to it.Why the model interpretability in ClosedLoop’s applications matters.Human intelligence mediation in the interpretation process.How ClosedLoop won the CMS AI health outcomes challenge.Examples of how bias manifests in models trained with health data; how to measure and mitigate bias.How ClosedLoop monitors its models over time and how COVID affected its accuracy.The way ClosedLoop measures the impact of its technology.Dave’s advice for other leaders of AI-powered startups.His vision for the near-future impact of ClosedLoop.Quotes:“There were a lot of things I felt were broken about healthcare that I couldn’t do anything about, but I kept coming back to this idea of using all the right data to make the right decisions and getting the right treatment to the right patient at the right time.” — Dave DeCaprio“[We] put ClosedLoop together to basically tackle this data science and AI in healthcare challenge.” — Dave DeCaprio“Where prediction in AI plays a huge role in healthcare is moving from a reactive to a proactive system.” — Dave DeCaprio“Healthcare data is very complex. There are tens of thousands of diagnosis codes, there are hundreds of thousands of drug codes, and they’re constantly changing.” — Dave DeCaprio“In almost all cases, the output of our model is mediated with human intelligence in order to actually make a decision about a patient’s care.” — Dave DeCaprio“The most powerful measures of the impact are the stories we get from our customers.” — Dave DeCaprio“If you want to build a robust company that’s going to be successful year after year and be able to grow and really tackle these problems, you eventually have to show tangible demonstrable benefits.” — Dave DeCaprioLinks:Dave DeCaprio on LinkedInClosedLoopClosedLoop on TwitterResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

AI and machine learning are growing in significance as far as adapting to climate change is concerned. During today’s episode, I welcome Max Evans, founder and CTO at ClimateAi, to discuss the topic of climate resilience and how technology is driving progress in this arena.Max begins our conversation with an overview of the important work he is doing at ClimateAi, before weighing in on the role of machine learning in the AI startup space. He describes in detail the chain of different machine learning models and the challenges associated with high dimensionality and quality in data of this nature. We touch on Max’s preferred methodologies, and he unpacks the role of literature searches, a lack of historical data, and the technological advancements he is able to leverage in his work at ClimateAi today.Key Points:An introduction to Max Evans, Founder and CTO at ClimateAi.What ClimateAi does and why it is important for adapting to climate change.The role of machine learning in AI startups.The chain of different ML models involved in making weather and climate data usable.Tackling the challenge of high dimensionality and quality in machine learning data.Projecting and adding broader impact functions to produce more meaningful data.The hybrid between Stanford design thinking and the lean methodologies Max prefers.What it means to include a literature search in the process.Technological advancements leveraged by ClimateAi today. Navigating a lack of historical data with synthetic data. Micro and macro perspectives on climate decisions.What the AI process is really about.Max’s goal for ClimateAi in three to five years.Quotes:“ClimateAi’s mission is to climate-proof our economic system. We want all businesses to make climate-informed decisions.” — Max Evans“ML is a core mindset of AI startups in terms of how you solve problems, how you start with the exploratory data analysis, the hypothesis building, the baseline and investigation, the modeling, and the many loops.” — Max Evans“Don't start with the technology or the product, but start with a customer need.” — Max Evans“It wouldn't have been possible before, and it is definitely possible now to start building forecasts of the climate.” — Max Evans“Newer methods are developing so rapidly that the edge of what's possible continuously shifts outward.” — Max Evans“[The AI process] is really about building a data-driven, hypothesis-driven, need-solving culture in both your technological team and in your broader team at large.” — Max EvansLinks:Max Evans on LinkedInClimateAiResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

New therapeutics refer to newly developed drugs, treatments, or interventions designed to prevent, treat, or cure diseases or medical conditions. The process of discovering new therapeutics is a complex and challenging task that requires significant resources and expertise.In today’s episode, I sit down with David Healey, the Vice President of Data Science at Enveda Biosciences, to discuss searching for new therapeutics in nature. Enveda Biosciences is a cutting-edge biotech company revolutionizing drug discovery processes using automation and machine learning. It has a unique approach involving mapping the vast unknown chemical space in nature to identify potential therapeutics. David is a data scientist with a knack for machine learning in life sciences. He has expertise in deep neural networks, computer vision, natural language, and graph models, including a solid background in drug discovery, cheminformatics, metabolomics, and experimental biology.In our conversation, we talk about the role of machine learning in drug discovery and the importance of developing treatments. We discuss using big data for drug discovery, the challenges and opportunities of the field, the hurdles of working with mass spectrometry data, and Enveda Biosciences’s approach to research. Hear how Enveda Biosciences finds the best talent, why drug discovery is an exciting field, and much more.Key Points:David’s background leading up to his role at Enveda Biosciences.What Enveda Biosciences focuses on and their approach to drug discovery.Learn about mass spectrometry, tandem mass spectrometry, and chromatography.The role of machine learning in biosciences and how it is used with mass spectrometry.Enveda Bioscience’s applies machine learning differently.He explains the challenges encountered when working with mass spectrometry data.Find out the value of large language models and other advances in the field.We unpack the niche nature of the work Enveda Biosciences is doing.Overview of the different types of experts that are working at Enveda Biosciences.David shares what recruiting approaches have been most successful for the company.Advice that David has for other AI-powered startups.He tells us about the impact he wants Enveda Biosciences to have in the future.Quotes:“Fundamentally, what we are doing at Enveda is looking for active molecules in nature. What that involves is trying to learn what the molecules are that nature produces, and what they do.” — David Healey“We are using machine learning to sort of interpret the language of the mass spectrometry, and in particular, to treat it like a natural language problem, or like a machine translation problem.” — David Healey“We do a lot of work on learning better representations of the spectra so that we can compare them with each other in a way that would better approximate the actual similarity of a molecule.” — David Healey“Really being deliberate about getting the best talent in the door at the very beginning, I think, is really crucial.” — David HealeyLinks:David Healey on LinkedInEnveda BiosciencesResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

The demand for trustworthy AI is increasing across all sectors. Today’s guest is committed to mitigating biases of AI models and ensuring the responsible use of AI technology. Yiannis Kanellopoulos is the Founder and CEO of Code4Thought, the state-of-the-art AI audit solution for trustworthy AI.In this episode, we discuss what it means for AI to be trustworthy and Yiannis explains the process by which Code4Thought evaluates the trustworthiness of AI models. We discover how biases manifest and how best to mitigate them, as well as the role of explainability in evaluating the trustworthiness of a model. Tune in to hear Yiannis’ advice on what to consider when developing a model and why the trustworthiness of your business solution should never be an afterthought.Key Points:Yiannis Kanellopoulos’ background; how it led him to create Code4Thought.What Code4Thought does and why it’s important for the future of AI.What it means for AI to be trustworthy.How Code4Thought evaluates the trustworthiness of AI models.Yiannis shares a use case evaluation in the healthcare sphere.Why Code4Thought’s independent perspective is so important.Yiannis explains how biases manifest in AI technology and shares mitigation strategies.The role explainability plays in evaluating the trustworthiness of a model.Why explainability is particularly important for financial services.Simultaneously optimizing accuracy and explainability.What to consider when developing a model.The increasing demand for trustworthy AI in various sectors.Yiannis’ advice for other leaders of AI startups.His vision for Code4Thought in the next three to five years.Quotes:“We are building technology for testing and auditing AI systems.” — Yiannis Kanellopoulos“The team that produces an AI system [is] tasked to solve a business problem. They're optimizing for solving this problem. They're not being optimized to test the model adequately [and] ensure that the model is working properly and can be trusted.” — Yiannis Kanellopoulos“One can say that by performing an explainability analysis, you can use it to essentially debug the way your model works.” — Yiannis Kanellopoulos“Don’t try to optimize only the business problem. The quality of your solution [and] the trustworthiness of the solution, should not be an afterthought.” — Yiannis KanellopoulosLinks:Yiannis Kanellopoulos on LinkedInYiannis Janellopoulos on TwitterCode4ThoughtCode4Thought on YouTube Code4Thought on LinkedInCode4Thought on TwitterResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Climate change poses significant risks and uncertainties, with far-reaching impacts on business operations, supply chains, and financial performance. By conducting a climate risk analysis, businesses can mitigate risks, develop savvy strategies, and masterfully manage and mitigate them.Joining me today is Josh Hacker, an atmospheric scientist whose career has spanned diverse research and science management roles. Josh is also the Co-Founder and Chief Science Officer at Jupiter Intelligence, the go-to expert for organizations seeking to strengthen their climate resilience through climate risk analytics. Josh has made his mark in both academic and laboratory settings and is helping to meet private sector demands for comprehensive and accurate information on the costs of climate change for individual companies and market sectors.In our conversation, we discuss why climate change is relevant for companies, what they can do about it, and how Jupiter Intelligence is leading the way. We unpack the various types of climate risks, the role of machine learning, and the validation process. Learn about the various uncertainties and errors in modeling, how to correct them, the role reanalysis plays, why a multidisciplinary team of experts is essential, and more.Key Points:Background about Josh and why he decided to start Jupiter Intelligence.The work Jupiter Intelligence does and how it relates to climate change adaptation.Why climate risks are being taken seriously by the private sector.Discover the role of machine learning tools in assessing climate risks.An outline of the various challenges faced when working with climate models.Learn about his approach to model validation and why it is crucial. Keeping a balance between model accuracy and explainability.Find out how model uncertainty and model errors are quantified.How bias manifests in climate models, and how to identify it.Josh shares advice for other leaders of AI-powered startups.What to expect from Jupiter Intelligence in the future.Quotes:“The vast majority of capital that we're using, and we will be using to adapt to climate change, is locked up in the private sector. But on the other hand, the government has a role in policy. These two things have an interplay that then feeds into the broader community.” — Josh Hacker“The reality is there's no one way. [Validation] is a complicated process that you have to build on to make sure that things are working right all along the way.” — Josh Hacker“The game in climate modeling is to actually pull the signal out from that noise. We want to pull out the slow stuff, how the climate changes, how the climate is changing relative to all the weather patterns that are going on underneath it.” — Josh Hacker“Because of that historical period and the existence of these reanalyses, we have something we can do to correct the historical statistics of the climate models.” — Josh HackerLinks:Josh Hacker on LinkedInJupiter IntelligenceResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Today’s guest uses AI for the personalized treatment of early-stage lung cancers and other lung diseases that need localized treatment. Eva van Rikxoort, a scientist in medical image analysis and the CEO and Founder of Thirona, started studying AI 20 years ago. Her interest in lung imaging and the translation of it with the help of AI led her to found her company which develops medical image analysis software based on deep learning.In this episode, she explains more about what Thirona does and the challenges they encounter when working with CT images. You’ll learn about the importance of online learning components for the future of AI applications for medical purposes and how the team at Thirona ensures that the technology it develops provides the right assistance to doctors, patients, and researchers. Tune in to find out more about the role of AI in the future of personalized medicine and lung disease treatments.Key Points:An introduction to Eva van Rikxoort, her experience in AI, and how she founded Thirona.What Thirona does and why this is important for treating disease.  How the company uses machine learning in multiple ways for different models to predict various outputs. The types of challenges Thirona encounters when working with CT images.How they deal with situations where clinicians disagree or annotations are not reliable. How the regulatory process affects the way Thirona develops machine learning models.The importance of online learning components for the future of AI applications for medical purposes. How Thirona ensures that the technology it develops provides the right assistance to doctors, patients, and researchers. Approaches to recruiting and onboarding that have been most successful for Eva’s team.Eva’s advice to other leaders of AI-powered startups about trusting your gut and asking for help. How Eva foresees the impact of Thirona in the future in terms of personalized medicine and lung disease treatments.Quotes:“[At Thirona] we don't make software that's aimed at radiology, even though medical image analysis is very much a radiology thing, but we really focus on breakthrough treatments that are being developed both by pharma companies but also by biotech companies.” — Eva van Rikxoort“If you look technically at AI, we could be learning every single day from what we do. I mean, we as [people] do, but our AI models learn in release cycles instead of on a daily basis.” — Eva van Rikxoort“Next to making something that's highly innovative, you’re also opening a market to this innovation. It's a twofold thing.” — Eva van Rikxoort“Don't be afraid to ask anyone for help. For example, opinion leaders, doctors, they are very, very happy to help. They really love the innovations in their field.” — Eva van Rikxoort“I really think the impact of Thirona will be the personalized medicine, the personalized treatment of early-stage lung cancers or any other lung disease that needs localized treatment.” — Eva van RikxoortLinks:Eva van RikxoortThironaResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.