Dr Vivekananda Reddy’s Post

🔬 AI Revolution in Male Infertility Diagnosis by Toho University, Japan. Exciting developments in reproductive health! Researchers at Toho University, Tokyo, led by Associate Professor Hideyuki Kobayashi, have developed an innovative AI tool that predicts male infertility using blood tests. 1. Accuracy: 74% overall; 100% in identifying non-obstructive azoospermia. 2. Breakthrough: Analyzes hormone levels (T/E2, LH, FSH) for precise risk predictions. 3. Impact: Simplifies and makes male infertility diagnosis more accessible. This groundbreaking tool could transform how we approach male infertility screening. #AI #HealthcareInnovation #MensHealth #Infertility #JapanInnovation

One of my favorite things about fall is ASRM, learning about new initiatives, ideas, and innovations in the fertility space, and connecting with friends from all over the world! One of the big topics over the last year increasingly has been the usage of AI in the fertility clinic setting. However... I believe that not enough attention has been paid to the usage of AI BEFORE patients walk in the door...using it to educate new patients, nurture them in to becoming patients in your clinic and guiding them through the process. That's what I am focused on Cima, to help bring to together the pre clinic AI process and the clinic based AI processes. I'd love to connect with you at ASRM and talk about how this can be perfectly deployed in your clinic and practice setting! . . #asrm #fertility #asrm #marketing

Few days back, one LinkedIn post dropped a bombshell on me and I was completely shattered. The news was something like "Soon artificial womb is gonna be a new norm in the society" Yaa....the reaserch was going on. But I was amazed by knowing that it is right in front of us and knocking our doors. #ARTIFICIALWOMB / #ARTIFICIALUTERUS 👉 It's basically a device in which a foetus grows outside the body of mother. 👉 It mimics the internal environment of the uterus which helps baby to grow. 👉 We can see how the baby is growing. 👉 Certainly it has pros and cons _________________________________________ • We can save premature infants. ( Nearly half number of premature babies die in the incubator ) • People can have biological child if there are certain problems in conceiving the pregnancy. _________________________________________ BIGGEST ISSUE Is an artificial womb Ethical ? Definately it depends on person to person or we can say community to community. Especially, in traditional Indian families it's gonna be way beyond their acceptance level. Biologically it's an invention which has potential to reshape humanity. But there are high chances of misusing this technology. Secondly it'll be quite expensive initially so it would not be affordable for maximum people. It needs people with high expertise to handle. _________________________________________ Science is moving at lighting speed. What's your opinion ? Is it ethical ? Comment down 👇

Absolutely fascinating article, and one that hits close to home for me. Having spent over 20 years speaking and consulting with professionals in the death, cemetery, and crematoria sectors about the evolving future attitudes, rituals and practices towards death, burial, and memorialisation, this is a remarkable extension of those conversations. The integration of AI into end-of-life decision-making certainly showcases its potential, but is this truly what AI is meant to do? In my view, AI excels when it sticks to its lane—taking vast amounts of information and assembling it into actionable knowledge. Yet, when it comes to making one of the toughest decisions that no one wants to face, there's a crucial need to leave vast amounts of room for human wisdom, faith and compassion. The collaboration between families, caregivers, and our incredible medical teams should remain at the forefront. I’m curious to hear your thoughts on this—how do you see the role of AI in such deeply personal and impactful decisions? Check out the article here: https://lnkd.in/ggF-jR4S #AI #Healthcare #EndOfLifeCare #DeathCare #Cremation #Memorialization #TechInHealthcare #AIInMedicine #DigitalHealth #MedicalEthics #Innovation #FutureOfHealth #FamilyCaregivers #MedicalProfessionals #HealthcareInnovation

One of my favorite things about fall is ASRM, learning about new initiatives, ideas, and innovations in the fertility space, and connecting with friends from all over the world! One of the big topics over the last year increasingly has been the usage of AI in the fertility clinic setting. However... I believe that not enough attention has been paid to the usage of AI BEFORE patients walk in the door...using it to educate new patients, nurture them in to becoming patients in your clinic and guiding them through the process. That's what I am focused on Cima, to help bring to together the pre clinic AI process and the clinic based AI processes. I'd love to connect with you at ASRM and talk about how this can be perfectly deployed in your clinic and practice setting! . . #asrm #fertility #asrm #marketing

How is #artificialintelligence (AI) reshaping the landscape of medical innovation? Anubhav Prashant, COO, Apollo Cradle and Apollo Fertility, tells us about the transformative impact of #AI in healthcare, from IVF laboratories to predictive preventive care.   #HealthTech #AI #Healthcare #ISBExecutivePerspectives #ExecutiveEducation #ISBExecEd Indian School of Business Healthcare at ISB

We are delighted to announce our latest opinion article in Frontiers in Global Women’s Health titled, “The Impact of Informant-Related Characteristics Including Sex/Gender on Assessment of Alzheimer’s Disease Symptoms and Severity.”   Did you know that the Clinical Dementia Rating Scale (CDR) is pivotal in Alzheimer’s disease (AD) staging?   Have you ever wonder which is the impact of the gender of caregiver characteristics on its readout?   Here we discussed how this factor —often overlooked—play a crucial role in the accuracy of this assessment.   Key Takeaways on possible biases:   Dynamics: The informant’s relationship with the patient can significantly affect the perception of the patient’s health status, potentially influencing severity scores.   Sex/Gender Influence: The sex/gender of the informant can also influence dementia assessments in significant ways.   Cultural Lens: Cultural and socioeconomic characteristics can influence how caregivers assess AD symptoms and severity.   These factors are vital in understanding caregiver reports and ensuring precise patient diagnosis and staging, especially when used as primary endpoints in clinical trials.   A heartfelt thank you to the brilliant minds behind this work: Ella Abken, Laura Castro, Antonella, Maria Teresa Ferretti, and Carmela Tartaglia for their invaluable collaboration. 👏   #AlzheimersResearch #WomensBrainHealth #NeurodegenerativeDiseases #ClinicalDementiaRating #HealthcareInnovationbias Link to the publication: https://lnkd.in/eUteA-u4   🤝 Donate today to support the advancement of brain and mental health: https://lnkd.in/epBrMRP5

In 2020, I started exploring how Oura Ring data could be used to estimate ovulation. Fast forward to this week—Oura launched Fertile Window! This feature is powered by a deep learning model trained on physiological data from Oura Ring. It predicts fertility windows with greater accuracy than traditional methods, adapting to individual patterns in real time. I’ve been especially passionate about ensuring the model works for everyone—whether they have short, long, irregular cycles, or are in perimenopause. Navigating biological data variability and refining the model through many iterations was challenging, but seeing it released is incredibly rewarding. This feature isn’t just for fertility—tracking menstrual cycle patterns connects to all aspects of health, including sleep, activity, and stress. So if you have an Oura Ring and a menstrual cycle, or if you're interested in AI and health, check it out! https://lnkd.in/e6Tagv-6

I wonder to what extent "deep learning" was necessary here, or if that is just modern shorthand for some kind of supervised learning? I also wonder if the individual variability is so great that an n-of-1 style "trained model" might outperform a CATE-based group-level training. Perhaps this is already being used, as it says "The Fertile Window feature uses two algorithms to predict and detect your most fertile days based on your past cycles and physiological data" -- the phrase "past cycles" suggests that user-provided ovulation timing data may be incorporated. If so, this is a notable feature that is methodologically distinct from how we prediction algorithms are typically deployed in consumer contexts, where a pre-trained model is used to generate predictions. A model/algorithm trained on the user's own data is (at least in some cases, and this is likely one of them) much better! Nina Thigpen, PhD, message me if you are able to provide some more details :-)

🧠 Exploring Bias in LLM Opioid Recommendations This study investigates how large language models (LLMs) recommend pain management strategies, focusing on potential biases related to race, ethnicity, and sex. 🔎 Study Details -480 real-world patient cases were analyzed using MIMIC-IV data. -LLMs (GPT-4 and Gemini) were tasked with recommending opioids for patients of various races/ethnicities and sexes experiencing common pain complaints. 📊 Key Findings -No Racial/Ethnic or Sex Bias: LLMs did not show bias in recommending opioids based on race/ethnicity or sex. -Gemini's Opioid Recommendations: Gemini was more likely to recommend strong opioids and rate pain as severe compared to GPT-4. 💡 Implications for Healthcare LLMs may help physicians provide more equitable pain management, countering the racial and ethnic disparities often seen in opioid prescriptions. #HealthcareAI #PainManagement #HealthEquity #OpioidCrisis #ArtificialIntelligence https://lnkd.in/gGcmqzi4