Douglas Flora, MD, LSSBB, FACCC’s Post

AI is not only transforming healthcare by streamlining diagnostics but also enhancing patient outcomes through personalized care and predictive analysis. 🌟 At Lumy Digital, we believe in the transformative power of AI in healthcare and beyond. Explore, through this case study, how IBM Watson and Memorial Sloan Kettering used AI to personalize cancer treatment, revolutionizing patient care and decision-making. Let's reimagine possibilities for industries powered by AI innovation! 🌐💡 Discover how AI is reshaping the healthcare landscape and the incredible potential it holds. 📈 https://buff.ly/3USsJsY #AI #HealthcareInnovation #DigitalTransformation #LumyDigital

$62,000,000. That is the ballpark amount spent from 2013-2017 with IBM Watson and MD Anderson on the artificial intelligence cancer moonshot project. The results? Being $62,000,000 poorer and throwing it out. My feed is filled with AI discussions, and each one of them promises something or another to improve healthcare. My customers and prospects often discuss AI and what they are looking to accomplish, and my role is to accurately line up the experts for these discussions, and not fall victim to the hype train. This story is now 7 years old, but I recall that while the project was ongoing, the hype machine was off the chart, and you couldn't find articles or stories describing what was actually taking place on the project. Therein lies the challenge with AI that is facing my customers. Does the algorithm work and benefit the patient? There are over 1,500 vendors in healthcare AI and over half of them have been founded since this project failed. While some offer clear benefits, there are a lot that are in the morass. #healthcareit #artificialintelligence https://lnkd.in/g7kPzfMQ

The American Society of Clinical Oncology (ASCO) has set six guiding principals for use of AI in Oncology :::: 👥 👀 Transparency – AI tools and applications should be transparent throughout their lifecycle. 💡 Informed Stakeholders – Patients and clinicians should be aware when AI is used in clinical decision-making and patient care. ⚖ Equity and Fairness – Developers and users of AI should protect against bias in AI model design and use and ensure access to AI tools in application. ☑ Accountability – AI systems must comply with legal, regulatory, and ethical requirements that govern the use of data. AI developers should assume responsibility for their AI systems, its decisions, and their adherence to legal, regulatory, and ethical standards. 🔐 Oversight and Privacy – Decision-makers should establish institutional compliance policies that govern the use of AI, including protections that guard clinician and patient autonomy in clinical decision-making and privacy of personal health information. 👤 🫶 Human-Centered Application – Human interaction is a fundamental element of health care delivery; AI does not eliminate the need for human interaction and should not be used as a substitute for sensitive interactions that require it. We applaud ASCO for being at the forefront of setting this type of guidance! The Realyze Intelligence solution fits the bill - we support & adhere to all six of these guiding principals ✅ ✅ ✅ ✅ ✅ ✅ We're ready to help you accelerate clinical trial screening & turbocharge your clinical research. https://lnkd.in/gp3-MH7h #AIOncology #AIinPrecisionOncology #ClinicalTrialInnovation #NoMoreZombieTrials #RealyzeIntelligence

In today’s leadership landscape, where access to information is increasingly open source, keeping our workforce informed is imperative. To ensure your team remains agile, proactive, and knowledgeable about developments in their field, encourage them to utilize generative open AI sources for the latest updates. The number of scientific clinical trials and research papers informing systems, laws, and healthcare advancements demands an immense amount of man-hours. The human brain alone cannot efficiently read, analyze, and extract valuable insights from these papers on a large scale. Here, artificial intelligence serves as a powerful tool, capable of analyzing vast numbers of scientific documents in an unprecedented manner. Artificial intelligence not only enhances our ability to process and understand large volumes of information but also ensures we remain at the forefront of innovation and knowledge in our respective fields. Below is a recent paper published by Harvard Medical school using AI to predict Future Pancreatic Cancer https://lnkd.in/dkGcuuvk Whats your thoughts 💭? Do you think Machines learning and deep learning in clinical research might be a field where AI could help!

Eight in 10 (81.4 percent) supported patient consent for AI model use during treatment decisions. In a scenario in which an AI decision model selected a different treatment regimen than the oncologist planned to recommend, most respondents said they would present both options and let the patient decide (36.8 percent), with those from academic settings more likely than those from other settings to let the patient decide (odds ratio, 2.56). Three-quarters of respondents (76.5 percent) agreed that oncologists should protect patients from biased AI tools, but only 27.9 percent were confident in their ability to identify poorly representative AI models.

Medical Affairs leaders, Artificial intelligence (AI), and machine learning (ML)- based tools are rapidly becoming more mainstream. Are you having conversations in the fields on this topic? What does that mean for oncologists and their patients? [Or any specialty] Are KOLs comfortable using AI decision models in practice? What concerns them most if you’re hesitating to use these models? #medaff #insights #oncology #AI https://buff.ly/3JuvJ8U

In the September Edition Journal of AMIA (American Medical Informatics Association), an article was published on AI for clinical trials. Mendel was highlighted as one of two companies, along with IBM watsonx, and the article highlighted Mendel's transformation of clinical trials: "For instance, in an ongoing oncology trial, the utilization of Mendel.ai led to a 24%-50% increase in the number of patients accurately identified as potentially eligible compared to the standard practice. Furthermore, using Mendel.ai to determine potential eligibility only takes minutes, while it takes an average of 19 days for standard prescreening of breast cancer patients and 263 days for lung cancer patients." Try our product today: https://lnkd.in/gxMa-Tws Read the full article here: https://lnkd.in/gXXgAzmQ

Earlier this year, the American Society of Clinical Oncology (ASCO) set six guiding principles for use of AI in Oncology :::: 👥 👀 Transparency – AI tools and applications should be transparent throughout their lifecycle. 👤 🫶 Human-Centered Application – Human interaction is a fundamental element of health care delivery; AI does not eliminate the need for human interaction and should not be used as a substitute for sensitive interactions that require it. 💡 Informed Stakeholders – Patients and clinicians should be aware when AI is used in clinical decision-making and patient care. ⚖ Equity and Fairness – Developers and users of AI should protect against bias in AI model design and use and ensure access to AI tools in application. ☑ Accountability – AI systems must comply with legal, regulatory, and ethical requirements that govern the use of data. AI developers should assume responsibility for their AI systems, its decisions, and their adherence to legal, regulatory, and ethical standards. 🔐 Oversight and Privacy – Decision-makers should establish institutional compliance policies that govern the use of AI, including protections that guard clinician and patient autonomy in clinical decision-making and privacy of personal health information. We applaud ASCO for being at the forefront of setting this type of guidance! Even better?! The Realyze platform meets ALL SIX of these guiding principles ✅ ✅ ✅ ✅ ✅ ✅ We're ready to help you accelerate clinical trial screening & turbocharge your clinical research. https://lnkd.in/gp3-MH7h #AIOncology #AIinPrecisionOncology #ClinicalTrialInnovation #NoMoreZombieTrials #RealyzeIntelligence #HLTHUSA #HealthData #DataAnalytics #DataDrivenCare #AccurateData #HLTH24

𝗔𝗜-𝗗𝗿𝗶𝘃𝗲𝗻 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗖𝗮𝗻𝗰𝗲𝗿 𝗧𝗿𝗲𝗮𝘁𝗺𝗲𝗻𝘁: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗢𝗻𝗰𝗼𝗹𝗼𝗴𝘆 🧬 Imagine a world where every cancer patient receives a treatment plan tailored specifically to their unique genetic makeup, health profile, and tumour characteristics. Thanks to AI, this vision is no longer a distant dream but an emerging reality. 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗖𝗮𝗻𝗰𝗲𝗿 𝗧𝗿𝗲𝗮𝘁𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗔𝗜 Artificial intelligence is revolutionizing personalized cancer treatment by analyzing vast datasets—including genomic information, clinical records, and medical images—to determine the optimal therapy for each patient. Using deep learning models and predictive analytics, AI can identify patterns across millions of data points, enabling oncologists to create personalized plans that offer the best chance of treatment success while minimizing side effects. 𝗞𝗲𝘆 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗔𝗜 𝗶𝗻 𝗢𝗻𝗰𝗼𝗹𝗼𝗴𝘆 𝗚𝗲𝗻𝗼𝗺𝗶𝗰 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: AI-driven genomic analysis helps identify mutations that could influence a patient's response to certain drugs, guiding the selection of targeted therapies. 𝗥𝗮𝗱𝗶𝗼𝘁𝗵𝗲𝗿𝗮𝗽𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Reinforcement learning algorithms are being employed to optimize radiotherapy schedules, maximizing effectiveness while reducing damage to healthy tissue. 𝗗𝗿𝘂𝗴 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆: AI is also accelerating drug discovery for cancer by analyzing biological data to identify new compounds that could be effective against specific tumour types. 𝗘𝗻𝘀𝘂𝗿𝗶𝗻𝗴 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗔𝗜 However, as we move towards a future where AI plays a central role in treatment decisions, it is crucial to ensure that these systems are implemented ethically, with transparency and patient-centred care at the forefront. 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗲𝗻𝘃𝗶𝘀𝗶𝗼𝗻 𝘁𝗵𝗲 𝗿𝗼𝗹𝗲 𝗼𝗳 𝗔𝗜 𝗶𝗻 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗼𝗻𝗰𝗼𝗹𝗼𝗴𝘆? 🤖💡 Are we ready to fully integrate AI into our treatment decision-making processes, or are there still hurdles we need to overcome to make this the new standard of care? Let's collaborate and shape the future of cancer care! 🗣️💬 #AI #PersonalizedCancerTreatment #HealthcareAI #PrecisionOncology #CancerScience #FutureOfMedicine #DeepLearning #InnovationInHealthcare

Riding the wave in Healthcare Innovation with AI in Genomics: A Market on the Rise 🚀 The fusion of AI and genomics is shaping up to be one of the most groundbreaking shifts in healthcare. ✅ According to recent market projections, the AI in Genomics Market is expected to skyrocket from $2.5 billion in 2023 to over $13 billion by 2029. This exponential growth highlights the increasing adoption of AI-driven tools to accelerate drug discovery, enhance precision medicine, and transform genetic research. 📈 While the chart captures the scale of this momentum, the true impact lies in the potential to revolutionize how we understand and treat diseases. ✅ Pathomics Health is at the forefront of this change, leveraging AI and advanced data analytics to redefine cancer diagnosis and treatment. By integrating AI with pathology, Pathomics enhances the accuracy and speed of diagnostics, paving the way for personalized treatment options that are tailored to a patient’s unique genetic makeup. Pathomics Health plays a critical role in: 1️⃣ Providing AI-powered pathology tools that help in earlier detection and more accurate diagnostics, particularly in oncology. 2️⃣ Supporting the shift towards precision medicine by combining genetic data with AI algorithms, which leads to more targeted therapies and better patient outcomes. 3️⃣ Offering clinical-grade solutions that align with the broader growth of AI in genomics, creating a direct impact on improving healthcare efficiency. ✅ Key takeaways: 👍 The AI in genomics market is set for explosive growth, expected to multiply by 5X over the next six years. 👍 Pathomics Health exemplifies how AI can transform diagnostics, significantly improving accuracy and speed in complex areas like cancer pathology. 👍 This acceleration underscores the urgent need for healthcare professionals and investors to prepare for the coming wave of AI-driven innovation. 👍 Strategic partnerships, cross-disciplinary knowledge, and a forward-thinking mindset are essential to navigating this transformation successfully. As we stand on the precipice of this exciting evolution, it’s clear that those who invest in AI and genomics today, including companies like Pathomics Health, will be the leaders of tomorrow. The future of healthcare is being written by the data we decode today.