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🌟 Excited about the future of cancer screening? Call for Papers: Special Issue on the Future of Cancer Screening: Leveraging AI and Precision Oncology 🌟 Join us in shaping the future of oncology! Submit your research on leveraging AI for cancer screening to our upcoming special issue. Guest Edited by the fantastic Dr. Toufic Kachaamy MD FASGE AGAF FACG of City of Hope, Phoenix and the inimitable Editor-in-Chief Douglas Flora, MD, LSSBB, FACCC, this publication seeks innovative studies that enhance early detection and treatment strategies across various cancers. We're looking for comprehensive reviews, in-depth research papers, and forward-thinking commentaries. This is a wonderful opportunity for expert peer review, open access benefits, and global visibility on a growing readership internationally. 📅 Deadline: March 20, 2024 Link to Special Issue: https://lnkd.in/e2M5TqSP Link to Author Submission: https://lnkd.in/e8RJMqNT Areas of interest include, but are not limited to AI in cancer screening, current practice, gaps, and where future innovation should focus in the following areas: Colorectal cancer  Breast cancer  Cervical cancer  Lung cancer  Skin cancer  Prostate cancer Additional areas of interest include: Wearables and cancer screening Multicancer early detection: how AI and precision oncology are revolutionizing cancer screening Personalized screening plans: AI role in identification of patient at higher risk for malignancy Ethical consideration and strategies to ensure diversity in AI screening solution.  Integration with Clinical Practice: how to best assure the integration of AI-driven screening tools into routine clinical practice, addressing issues such as cost effectiveness, workflow integration, user-friendliness, and the role of healthcare professionals including human machine interactions In todays’ complex healthcare systems, what discerning criteria and deliberative considerations must be employed to judiciously choose an AI-based solution We invite researchers and experts in these fields to submit their manuscripts to contribute to this important and groundbreaking special issue. We also encourage submission of papers that have been published in conference proceedings, with the requirement that the authors have made significant extensions as compared to the already published version of the study. Let's advance cancer care together! #CancerScreening #PrecisionOncology #CallForPapers, Sophie Reisz, Douglas Flora, MD, LSSBB, FACCC, Tim Showalter, Trevor Royce MD MS MPH, Adam Dicker

We are standing at the cusp of a transformative era in healthcare, where the paths of artificial intelligence and precision oncology intersect, heralding a future where cancer prevention, life preservation, resource optimization, and the betterment of humanity are not just ideals, but achievable realities. We invite you to be an integral part of this journey by contributing to a comprehensive issue dedicated to exploring these groundbreaking advancements. Contact me, Douglas Flora, MD, LSSBB, FACCC or Nikhil Thaker, MD to contribute and share your insights and research in this pivotal field. #cancerscreening #precisiononcology #ai #healthcareonlinkedin

Triomics (YC W21) has raised $15 million in Series A funding to accelerate cancer care with generative AI. Despite thousands of trials in the United States each year, only 3% to 5% of eligible patients enroll in investigations of new treatments. Cancer specialists in the US have access to many innovative trials and treatments, but finding the right one for each patient is time-consuming. Only 20% of patient data is easily accessible, while the other 80% is in unstructured formats like doctors’ notes that require manual searching. Founded in 2021 by former MIT biotech researcher Sarim Khan and AI scientist at Adobe Hrituraj Singh, Triomics developed a family of oncology-focused large language models LLMs (called OncoLLM) to automate this process, saving oncologists time and improving patient outcomes. Their AI solution matches patients to trials with 95% accuracy in minutes— a process that usually takes hours. Triomics also helps healthcare providers automate other tasks like quality assurance and clinical pathway tracking. However, the focus remains on giving patients quicker access to trials and treatments with its ability to convert complex cancer data into usable insights. Today, six cancer centers and hospitals are actively using or piloting the company's LLM, and it plans to double that number by the end of the year. Congrats to the team on the Series A!

Can we save 2 million cancer patients per year? Lung cancer is the deadliest of them all. Early-stage diagnosis can significantly improve survival. But, only 20% of lung cancers are early-stage diagnosis. If we only had a way to diagnose lung cancer earlier… Oh, wait, there is! Here are some ways AI could support: 💡 1. 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐢𝐧 𝐄𝐯𝐞𝐫𝐲 𝐒𝐜𝐚𝐧: AI algorithms can improve diagnostic accuracy. Imagine detecting 94,4% through diagnostic imaging. Or 81% accuracy in histopathological diagnosis. Not to forget biomarkers, achieving a 93,3% sensitivity and 92% specificity, improving efficiency and decreasing misdiagnosis. 🌍 2. 𝐒𝐭𝐚𝐠𝐢𝐧𝐠 𝐀𝐝𝐯𝐚𝐧𝐜𝐞����𝐞𝐧𝐭𝐬: Accurate staging is key for lung cancer prognosis. AI, particularly with PET and CT scans, has 84,6% accuracy in classifying the lung cancer stage. Accurate staging plays a crucial role in creating the most appropriate treatment strategies and prognosis. 💪 3. 𝐓𝐫𝐞𝐚𝐭𝐦𝐞𝐧𝐭 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧: AI isn't just a tool; it's a treatment partner. It can optimize radiotherapy, predict immunotherapy responses with up to 76% accuracy, and guide personalized therapy decisions. With potential in even advanced stages. 🔮 4. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐏𝐨𝐰𝐞𝐫𝐬: AI can predict treatment responses accurately. Models forecast therapy responses, predict EGFR mutation probability, and analyze longitudinal data, offering insights into treatment response, educating on treatments, and marking a significant change in how we do lung cancer care. 🌐 5. 𝐎𝐯𝐞𝐫𝐜𝐨𝐦𝐢𝐧𝐠 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: Even though we have come far, AI still faces some challenges: data quality, standardization, validation, and ethical dilemmas. Some ways to overcome these are: Improved data sharing, robust AI models, clear guidelines, and prioritizing patient safety and privacy. What would earlier lung cancer detection mean to you? Read the full article in the link below 👇

Can we save 2 million cancer patients per year? Lung cancer is the deadliest of them all. Early stage diagnosis can significantly improve survival. But, only 20% of lung cancers are early-stage diagnosis. If we only had a way to diagnose lung cancer earlier. Oh, wait, there is! Here are some ways AI could support: 💡 1. 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐢𝐧 𝐄𝐯𝐞𝐫𝐲 𝐒𝐜𝐚𝐧: AI algorithms can improve diagnostic accuracy. Imagine detecting 94,4% through diagnostic imaging. Or 81% accuracy in histopathological diagnosis. Not to forget biomarkers, achieving a 93,3% sensitivity and 92% specificity, improving efficiency and decreasing misdiagnosis. 🌍 2. 𝐒𝐭𝐚𝐠𝐢𝐧𝐠 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭𝐬: Accurate staging is key for lung cancer prognosis. AI, particularly with PET and CT scans, has 84,6% accuracy in classifying the lung cancer stage. Accurate staging plays a crucial role in creating the most appropriate treatment strategies and prognosis. 💪 3. 𝐓𝐫𝐞𝐚𝐭𝐦𝐞𝐧𝐭 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧: AI isn't just a tool; it's a treatment partner. It can optimize radiotherapy, predict immunotherapy responses with up to 76% accuracy, and guide personalized therapy decisions. With potential in even advanced stages. 🔮 4. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐏𝐨𝐰𝐞𝐫𝐬: AI can predict treatment responses accurately. Models forecast therapy responses, predict EGFR mutation probability, and analyze longitudinal data, offering insights into treatment response, educating on treatments, and marking a significant change in how we do lung cancer care. 🌐 5. 𝐎𝐯𝐞𝐫𝐜𝐨𝐦𝐢𝐧𝐠 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: Even though we have come far, AI still faces some challenges: data quality, standardization, validation, and ethical dilemmas. Some ways to overcome these are: Improved data sharing, robust AI models, clear guidelines, and prioritizing patient safety and privacy. What would earlier lung cancer detection mean to you? Zainab G. Priyatham Gurram Birendra Amgai Sai Prasanna Lekkala Suvidha Manne Mohammed Adil Hiren Koshiya Nakeya Dewaswala, MD Rupak Desai Shyam Ganti Salim Surani Vijay K. Luthra FRSA ChPP MCMI ChMC MBCS Samuel Oliveira Read the full article here: https://lnkd.in/dCvaCp3Z

🤖 AI Copilot for Personalized Cancer Care ⚕ OpenAI and Color Health have teamed up to develop an AI-powered assistant for clinicians, aiming to revolutionize cancer care. This AI copilot, based on OpenAI's GPT-4 model, leverages patient data and medical knowledge to create personalized cancer screening plans and treatment options. The AI copilot acts as a valuable tool for clinicians, assisting in generating individualized treatment plans, identifying missing tests, and even producing necessary documentation. While it’s designed to augment, not replace, clinicians, this AI-powered assistant promises to streamline cancer care processes, reducing wait times for initial tests and ultimately improving patient outcomes. The "human-in-the-loop" approach, where clinicians actively refine the AI over time, fosters a collaborative and continuously improving system. This approach also addresses ethical concerns, ensuring that AI acts as a powerful tool, not an independent decision-maker. The copilot's potential impact is significant: it's already being integrated into clinical workflows for new cancer cases at UCSF, and Color plans to use it for over 200,000 patients by the end of 2024. This news is a game-changer for the healthcare industry and holds profound implications for the future of personalized medicine. The potential of AI to personalize cancer care, improving patient outcomes and reducing costs, is a powerful testament to the transformative potential of AI. This development is not just about AI's technical prowess but its potential to enhance human capabilities and empower healthcare professionals to deliver better, more individualized care. It's a sign that the future of medicine is not about replacing humans but about augmenting our abilities, leading to a brighter future for all.

Wow! This is a remarkable step forward in the fight against cancer by incorporating AI into Qatar’s National Cancer Screening Program! 🧬 In a recent interview with The Peninsula, Dr. Sheikha Abu Sheikha, the Director of Early Detection Programs at the PHCC, shared her thoughts. 👩🔬 In summary, AI technology will help physicians quickly analyze cancer screening images. This will speed up diagnosis and treatment, leading to better patient outcomes and potentially saving lives! Statistics from the news article: Breast cancer: 16.58% of all cancer cases Colorectal cancer: 9.44% of all cancer cases Thyroid cancer: 6.33% of all cancer cases Among females: Breast cancer: 39.15% of all female cancer cases Thyroid cancer: following breast cancer Among males: Colorectal cancer: 10.93% of all male cancer cases Prostate cancer: 9.52% of all male cancer cases It is fascinating to see how technology is revolutionizing early detection efforts and enhancing the efficiency and accuracy of the screening process. What are your thoughts on AI aiding in early detection for cancer? Do you believe it will significantly impact cancer care in Qatar and beyond? To learn more, visit: https://lnkd.in/daD2aTwA

Reading your post piqued my interest in the use of AI in cancer care. So I decided to search for more and found that Fatima bint Mubarak Center in the UAE has also introduced the use of AI during Adaptive Radiotherapy. It has been seen to significantly reduce the adaptive treatment time and radiation exposure compared to more traditional treatments! They also have other cutting-edge technology such as the chemotherapy robot which employs an automated compounding process to guarantee patient and caregiver safety as it prepares patient-specific hazardous doses with precision and efficiency. If this is what AI can contribute to cancer care now, what more in the next decade or so? https://lnkd.in/gXY8hGS5

AI can now determine where a patient's cancer arose. Here's everything you need to know: Cancer patients with unknown tumour origins currently face limited treatment options. A breakthrough approach from MIT and Dana-Farber Cancer Institute changes this. Machine learning now helps to pinpoint where these hard-to-identify cancers began. 1/ Mysterious origins In 3-5% of cancer cases, the origin of the tumour is unknown. These are called cancers of unknown primary (CUP). Knowing the origin is crucial for precision drugs, which are more effective and have fewer side effects. 2/ The prediction model: OncoNPC A team of researchers developed a machine-learning model, OncoNPC. It analyses 400 genes to predict a tumour's origin. Training on nearly 30,000 diagnosed patients, it predicted origins with 80% accuracy. High-confidence predictions = 95%. 3/ Results on unknown tumours Testing the model on 900 CUP tumours from Dana-Farber, 40% were classified with high confidence. The predictions matched the patients' genetic predisposition and survival times. Takeaway: This model could be a guide to personalised treatments. 4/ Helping drug decisions The model's predictions were consistent with the type of cancer treatments received by CUP patients. 15% more patients could have received targeted treatment if their cancer type had been known. 5/ Future possibilities The researchers plan to expand OncoNPC to include pathology and radiology images. This could predict not just the tumour type but also the optimal treatment—I can only feel this is a real step forward for anyone facing unknown cancer types. --- We're transitioning from a period of uncertainty to targeted, personalised care. This gets me really excited. If you liked this post, you'll love the newsletter. Cut through the noise in AI each week, subscribe here: https://lnkd.in/ejnEDaFj

Optimizing Lung Cancer Detection with AI: A Game-Changing Collaboration between GE HealthCare and Optellum! Lung cancer, a major global health concern, remains a silent threat with low screening rates among eligible individuals. However, a groundbreaking collaboration between GE HealthCare and Optellum is set to change this narrative by harnessing the power of AI in lung cancer detection. Here are the main take aways for the time constrained among us: 1. Early Detection Crisis: Less than 6% of those eligible undergo recommended lung cancer screenings. 2. Innovative Partnership: GE HealthCare and Optellum combine CT imaging with AI to identify lung cancers earlier, potentially saving lives. 3. The Power of AI: Optellum's AI can distinguish between malignant and benign lung nodules, offering a Lung Cancer Prediction score in seconds. 4. ED Opportunity: 30% of chest CT scans reveal pulmonary nodules, with lung cancer being a significant finding. AI can aid in early diagnosis. 5. Global Impact Goal: The collaboration aims to make AI algorithms widely available in healthcare facilities for broader lung cancer care. This strategic partnership marks a pivotal step towards leveraging CT and AI for improved lung cancer detection, offering renewed hope to patients worldwide. Here is what our leader and expert Ben Newton says: “Our collaboration opens doors to accelerate the deployment of cutting-edge technologies and innovative AI within large health systems, allowing us to make significant strides in the realm of early lung cancer detection. We are thrilled about the potential this technology holds for improving early detection for patients. Leveraging the integration of CT imaging with Optellum’s AI solution, we aim to transform the way we combat this forbidding disease.” Read the full article here: https://lnkd.in/efwrvMVP #LungCancerAwareness #AIinHealthcare #InnovationInMedicine