PictureHealth

🎉Congratulations to our co-founder @anantm, 𝟮𝟬𝟮𝟱 𝗔𝗔𝗔𝗦 𝗙𝗲𝗹𝗹𝗼𝘄. Dr. Madabhushi’s dedication to 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗽𝗮𝘁𝗶𝗲𝗻𝘁 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀 𝗮𝗻𝗱 𝗽𝗿𝗼𝗺𝗼𝘁𝗲 𝗵𝗲𝗮𝗹𝘁𝗵 𝗲𝗾𝘂𝗶𝘁𝘆 is at the heart of what we do. #AAASFellow #AIinHealthcare #PrecisionMedicine

Diving deep into AI imaging biomarkers for immunotherapy at #IO360Summit! Our team is on-site having great conversations about accelerating oncology clinical trials. #IO360 #ImmunoOncology #ClinicalTrials #Oncology #AI #PrecisionMedicine

Exciting News! 🚀 Introducing QVT phenotypes—AI imaging biomarkers quantifying tumor vascular properties non-invasively. Join us at ESMO IO 2024 for our mini oral presentation on NSCLC ICI prediction results. #PrecisionOncology #ESMOImmuno24 #Cancer #AIinHealthcare

Today only! Experience our AI-based therapy response prediction (for research use only) live on the #RSNA24 floor. We will be demoing CheckpointPx* ICI treatment response prediction for NSCLC at #RSNA24 at the North Hall Level 3- Booth 101.

Showing our advanced #radiomics that quantify biological properties of tumors, draw new insights on therapy response and build interpretable AI biomarkers at #RSNA24 on Monday @ Booth 101. Want to test this technology works on your data? Contact us and we’ll make it happen!

Overwhelmed by the interest in our interpretable response prediction biomarkers at #SITC2024! Thanks to all who shared images to see it in action. Missed us at SITC? Email us at partners@picturehealth.com to see what the buzz was about. #Immunotherapy #SITC24 #PrecisionOncology

Showing live demos at #SITC, booth 1223- of advanced #radiomics and explainable #AI biomarkers that quantify biological properties. Also our poster session, abstract 112 (hubs.li/Q02XdYwQ0) will show how #radiomics and #mulitmodal AI improve response prediction in LuAd.

See how our interpretable, predictive AI (RUO) works with your data at #SITC24. Send us a Lung CT image (hubs.li/Q02W9yph0) and visit us at booth 1223 during SITC to see the results! Also visit poster 112 showing #multimodal #AI improving response prediction in LuAD

Today’s IO biomarkers are not working. See how radiomics and multimodal AI can add missing information to current, incomplete biomarkers at #SITC22 Also visit abstract 112 (hubs.li/Q02VfC760) showing how #radiomics and #multimodal #AI improve response prediction.

#Cancer is #multimodal and so are we! We'll be presenting our poster in advancing #immunotherapy response prediction for cancer patients via digital pathology at #ESMO24 on Sunday, September 15 in Hall 6: tinyurl.com/esmo24PH Book a time with us: hubs.li/Q02PBhjj0

Exciting news! We'll be presenting a poster presentation at ESMO. Thrilled to showcase our first pathology paper, on our journey to build multimodal AI biomarkers. Attending? Let's connect and discuss potential collaborations. #ESMO2024 #AI #Biomarkers

It was a pleasure to be in the 6th @pharmstars spring cohort - we connected with excellent colleagues, benefited enormously from our mentors, and graduate with clear focus on the value of interpretable, multimodal biomarkers for the future of cancer care. bit.ly/3zsOd7V

@iScienceLuvr @anantm @trishan But I do think the early discovery space will benefit tremendously from foundation models. Allowing us to accelerate research such as adding new indications more quickly.

@iScienceLuvr @anantm @trishan Biopharma clients want to understand the rationale for an AI prediction & correlate with traditional biomarkers + known disease characteristics. And the FDA regulatory process (which we've started) is so long that TBD if speed/flexibility of a foundation model is very beneficial.

I am seeing a lot of companies that want to solve biology with AI and foundation models. Some examples include: Isomorphic Labs, EvolutionaryScale, DiffuseBio, Xaira Therapeutics, Recursion, Inceptive, etc. But I feel like I don't see as many companies trying to solve healthcare with AI. Sure there are medical AI companies like Abridge, Nabla, Hippocratic AI, etc. and they're all doing great work. However they are often limited to narrow administrative use-cases of LLMs, and do not seem to aim for more ambitious and innovative use-cases of foundation models in healthcare. Sure, administrative use-cases like clinical transcription/notetaking are very useful and important low-hanging fruits for AI applications, but there's so much more that foundation models can do to revolutionize healthcare. And I do not see many companies doing so. Of course the reason why is completely obvious: healthcare is a difficult industry to disrupt due to regulations and scale and data and so many more challenges. But just because it's hard doesn't mean it's not worth trying, right? Would love to hear others thoughts on this and if other people are working on this...

@iScienceLuvr @anantm @trishan We are exploring them but (so far) our imaging features perform better. Biologically tethered to the tumor and selected for patient response enables indication specific (i.e. definitive treatment response for late stage NSCLC without ALK mutations) interpretable AI models.

Excited to share these three posters tomorrow at #ASCO24 . Visit us to learn more about our explainable AI signatures and how we can build custom systems predictive of response to therapy and adverse events #AI #Radiology #Biomarker #explainableai #airadiology

We'll exhibit three posters at ASCO (picturehealth.com/asco) showing how our AI biomarkers can better predict response to therapy, and identify patients at risk of progression and adverse events. Meet with us: calendar.app.google/4mDVpuN76qEXFy… #ASCO2024 #AIinHealthcare #CancerResearch

Thank you @ClevelandClinic for selecting Picture health as a winner to the Quantum Innovation Catalyzer Program- delighted to work with the Cleveland Clinic and @IBM to further Precision Oncology loom.ly/CLwhb9o

Congratulations to our Founder, @anantm for a compelling #AACR plenary presentation: Integrating AI, Radiology and Pathology for Affordable and Equitable Precision Medicine

Presenting 2 external validations with our partners at @NUFeinbergMed 9:00A-12:30P. Automated segmentation (2595/22) and pneumonitis risk prediction models (2594/21) #AACR #AACR2024 #Immunotherapy #AI #ClinicalTrials #Biomarkers loom.ly/pFA4tEg loom.ly/sVFdA1M