Healthtech Talks with Fexingo: Digital Health, Telemedicine, and Medical Software

In this episode of Healthtech Talks with Fexingo, hosts Lucas and Luna dive into how machine learning models are now predicting 30-day hospital readmissions with over 80% accuracy — before the patient even walks out the door. They focus on Mount Sinai's SPOT Readmission Prediction Tool, which analyzes over 200 variables from electronic health records, social determinants, and real-time vitals to f...

In this episode of Healthtech Talks with Fexingo, Lucas and Luna explore how artificial intelligence is transforming lung cancer detection. They focus on a breakthrough AI system developed at the University of Cambridge that can identify malignant lung nodules from a single CT scan with greater accuracy than human radiologists alone. The conversation covers the training dataset of 10,000 scans, th...

Lucas and Luna explore how generative AI is automating the clinical note, one of the most hated tasks in medicine. They focus on the specific case of NYU Langone Health, which piloted an ambient AI scribe from Nuance's Dragon Ambient eXperience (DAX) in 2024. By mid-2026, over 200 physicians are using it, saving an average of 7 minutes per visit. Lucas breaks down the workflow: a microphone captur...

In this episode, Lucas and Luna explore a cutting-edge application of artificial intelligence in neonatology: predicting sepsis in premature babies hours before clinical signs appear. They dive into a 2025 study from three US children's hospitals that used a machine learning model trained on continuous vital-sign data from over 4,000 infants in the NICU. The model, developed by researchers at Chil...

For episode 100 of Healthtech Talks, Lucas and Luna explore how machine learning models are detecting early biomarkers of Alzheimer's disease from routine eye scans and speech patterns — years before cognitive symptoms appear. They dive into a 2025 study from the University of Edinburgh that used retinal imaging to predict disease onset with 85 percent accuracy, and discuss how the technology coul...

In this episode, Lucas and Luna explore how machine learning models are predicting surgical site infections before the patient even leaves the operating room. They dive into a specific model developed by a team at the University of Michigan Health System, which analyzes pre-op labs, patient history, and intra-operative data to assign a real-time infection risk score. The hosts discuss the 2024 stu...

In this episode of Healthtech Talks, Lucas and Luna explore how AI is turning smartphone photos into skin cancer screening tools. They focus on a specific 2025 study from Stanford where a deep learning model matched dermatologists' accuracy in detecting melanoma from patient-taken selfies. The discussion covers the model's 96% sensitivity rate, the challenge of differentiating benign moles from ma...

Sepsis kills roughly 350,000 adults in US hospitals each year — often because clinicians catch it too late. In this episode, Lucas and Luna drill into a single case: how the University of Florida Health system deployed a real-time AI sepsis alert that flags patients 4 to 6 hours before they meet standard diagnostic criteria. They discuss the model's design — it analyzes 35 vitals and lab values ev...

Episode 96 of Healthtech Talks dives into the fast-emerging field of AI-driven post-surgical monitoring. Lucas and Luna examine how hospitals are deploying machine learning models that analyze continuous vital-sign streams—heart rate, respiratory rate, oxygen saturation—from bedside monitors to detect complications like sepsis, internal bleeding, and anastomotic leaks hours before a patient feels...

Emergency rooms are chaotic by design, but AI is starting to bring order. In this episode, Lucas and Luna explore how hospitals are using machine learning to predict how long you'll wait before seeing a doctor — and why that matters for both patient satisfaction and clinical outcomes. They break down a real deployment at a major health system: how the model ingests real-time data from triage notes...

Episode 94 of Healthtech Talks with Fexingo explores how artificial intelligence is moving beyond diagnosis to actually design personalized cancer treatment plans. Lucas and Luna examine a real-world case at Memorial Sloan Kettering, where an AI system called CURATE.AI helped a patient with advanced prostate cancer find the optimal drug combination after standard therapies failed. The algorithm an...

Episode 93 of Healthtech Talks explores how hospitals are using generative AI to create personalized, plain-language discharge instructions for patients. Lucas and Luna examine a pilot at Johns Hopkins where an AI model tailors aftercare summaries to a patient's reading level, language, and condition. The system reduced 30-day readmissions by 18% in a six-month trial. They discuss the challenges o...

In this episode, Lucas and Luna explore how artificial intelligence is being used to personalize mental health therapy for millions. They focus on the case of Woebot, a chatbot therapist that uses cognitive behavioral therapy techniques and natural language processing to adapt to each user's emotional state in real time. The hosts discuss how Woebot's algorithms analyze user responses to tailor ex...

Lucas and Luna explore how artificial intelligence is being used to detect signs of anxiety and depression from voice patterns alone. They dive into a specific study from the University of Maryland where an AI model achieved 85% accuracy in identifying depression from just 30 seconds of speech. The episode covers the underlying technology—acoustic features like pitch, rhythm, and energy—as well as...

In this episode of Healthtech Talks with Fexingo, Lucas and Luna explore how artificial intelligence is transforming nursing by predicting patient deterioration hours before it becomes critical. They dive into a specific case: the AI-powered early warning system deployed at Johns Hopkins Hospital, which reduced cardiac arrests by 30% on pilot units. Lucas breaks down how the model uses vitals, lab...

Lucas and Luna explore how machine learning models are being trained on passive smartphone data — typing speed, sleep patterns, social media activity — to predict mental health crises days in advance. They walk through a specific study from Cambridge Health Alliance that achieved 72-hour warning accuracy of 87 percent, and discuss the ethical tightrope between intervention and surveillance. The ep...

In this episode of Healthtech Talks, Lucas and Luna explore how artificial intelligence is being used to detect brain aneurysms from routine CT scans before they rupture. They focus on a deep-learning algorithm developed at the University of Zurich, which was trained on over 1,500 angiograms and flagged suspicious vessels with 95 percent sensitivity in a recent study. The conversation covers how t...

Lucas and Luna explore how machine learning models are moving beyond traditional risk factors to predict heart attacks days or even weeks before they happen. They focus on a specific algorithm developed by a team at the University of Texas that analyzes wearable ECG data combined with electronic health records. The episode breaks down how the model identifies subtle electrical changes in the heart...

Episode 86 of Healthtech Talks explores how AI is helping hospitals predict supply chain disruptions before they happen. Lucas and Luna dive into the case of Intermountain Health, which uses machine learning to forecast shortages of IV fluids, surgical gloves, and other critical supplies. They discuss how the system analyzes historical usage data, supplier performance, and external signals like we...

Lucas and Luna explore how a growing number of hospitals are using AI to predict equipment failures before they disrupt patient care. They focus on the case of Baptist Health in Florida, which deployed a predictive maintenance platform on 4,500 infusion pumps and reduced unplanned downtime by 38 percent. The conversation covers how vibration sensors, temperature logs, and utilisation data feed mac...

Lucas and Luna dive into how hospitals are using AI to forecast bed demand in real time, reducing overcrowding and improving patient flow. They examine a case study from Humber River Health in Toronto, which partnered with a startup called Trella Health to build a predictive model that cut emergency department wait times by 22% and reduced ambulance offload delays by 15 minutes. The hosts discuss...

Episode 83 of Healthtech Talks with Fexingo dives into how machine learning models are now predicting which patients are likely to be readmitted within 30 days of discharge. Lucas and Luna explore a concrete case: a 450-bed community hospital in Ohio that deployed a predictive model trained on EHR data — vitals, lab results, medication changes, and social determinants like housing stability. The m...

Finding the right clinical trial is still one of the most manual, time-consuming parts of oncology care — often taking weeks and relying on harried nurses flipping through eligibility criteria. This episode looks at how a new wave of AI-powered matching platforms is changing that. Lucas and Luna focus on a specific case: how a system built by a startup called Trialpath — now used at a major U.S. c...

Lucas and Luna dive into the growing use of AI-powered computer vision and sensor systems to predict patient falls in hospitals. Over 700,000 patients fall each year in U.S. hospitals, leading to extended stays and $30 billion in annual costs. We look at one specific system deployed at a 400-bed hospital in Ohio: a combination of ceiling-mounted depth sensors and machine learning algorithms that t...

Every missed appointment costs a clinic money and wastes a slot another patient could have used. In Episode 80, Lucas and Luna explore how health systems are using machine learning models to predict which patients are most likely to miss their appointments — sometimes with 80% accuracy. They dig into a real deployment at a large urban hospital system in the Midwest that cut no-show rates by 18% in...