Jayne Woolf, BCMAS’ Post

Wednesday Vibes 😎   ASCO is behind us, time flies. According to some information shared in an ASCO AI Community of Practice discussion, there were more abstracts submitted related to AI than ever before. So many great sessions!!   With all this activity, you may have missed that they released Guiding Principles for AI in oncology https://lnkd.in/ebCUBGDB  These include, transparency, informed stakeholders, equity and fairness, accountability, oversight and privacy and human-centered application   I especially appreciate the discussion around the need for understanding of AI and human-centered application. The importance of patients having a seat at the table as well in these discussions is critical.    I do hope that we'll also see more discussion around the implications of the use of AI for communication of information and dissemination of data. This, too, impacts the entire healthcare community. Our words matter, content matters -- and language is the 'currency' of these systems.

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.

Can’t wait to hear more about this research - feels like a new CPT code should be in the works- probably several and some new regulations or at least guidelines on AI for healthcare. #aiforgood #bcs #womenshealth #breastcancerresearch #fda #ama #ai #healthcareai

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

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 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

🌐 The latest AI Act highlights the importance of addressing biases in AI models. In many discussions, whether with clients or during panels, the term "bias" is often used loosely, without a clear understanding of what it truly entails, aside from representing a specific population. 🩺 To identify effective examples of bias, we can look at current practices in medicine. This is explored in Usha Lee McFarling latest article on STAT News, which is part of a series called “Embedded Bias.” The article discusses how Black patients with low T cell counts are automatically classified as being in poor health, even though this count is normal for that population. 🔍 This bias in interpretation stems from a narrow perspective where medical standards are based on a Caucasian model. Addressing such biases is crucial for AI providers as they strive to build models that are more accurate, reliable, and fair. In this way, AI has the potential to correct decades of bias in traditional medicine, benefiting both patients and healthcare providers. https://lnkd.in/duy2c_49 #bias #healthcare #AI

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

$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