The hottest Medical Technology Substack posts right now

A systematic review showed A.I. significantly boosts detection of precancerous polyps in colonoscopies.
Integration of A.I. in medical procedures like colonoscopies requires caution to avoid automation bias.
Physicians must serve as clinical champions to educate and integrate A.I. technology in hospitals.

DrDoctor is transforming hospital appointments by making them digital through smartphones.
Digitizing appointments reduces costs, inefficiencies, and improves the overall experience for patients.
The shift towards digital appointments has the potential to lead to more innovative healthcare services and patient engagement.

Measuring glucose alone is not rational; it should also be measured along with lactate and ketones for better understanding of metabolic changes.
BCAAs can impact glucose levels through multiple pathways, highlighting the importance of context in interpreting metabolic markers.
Having a home test for pyruvate could provide more valuable insights into metabolic health, along with glucose and lactate measurements.

Phage therapy, using bacteriophages to combat drug-resistant infections, has seen renewed interest but faces challenges in scaling up due to the specificity of phages and the complexity of creating effective treatments.
High-tech advancements like AI and synthetic biology offer potential solutions by predicting phage-host relationships and synthesizing phages for personalized treatments, aiming to overcome logistical and regulatory hurdles.
The burden of antimicrobial resistance is predicted to impact low-income countries the most, highlighting the need for affordable, accessible phage therapies that can be used to prevent the spread of bacterial diseases in global health crises.

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Vial is working to reduce the cost of clinical trials by innovating new technology.
Vial's Act I focuses on structuring, digitizing, and automating the clinical trial process to drive efficiency.
Vial's Act II, Battery Bio, aims to revolutionize drug discovery by integrating software and advanced technologies in a vertically integrated approach.

AI-powered digital health products can revolutionize healthcare by improving patient care and reducing costs.
Key trends in the future of digital health products include personalizing healthcare with AI and automating tasks to free up healthcare professionals.
Challenges in developing AI-powered digital health products include the lack of data and regulatory hurdles, despite opportunities for AI to enhance patient care, reduce costs, and improve healthcare delivery.

Alzheimer's clinical trials still use outdated paper-based tools like the CDR scale, hindering progress in drug development
Industry experts push for novel digital tools to improve clinical trials, aiming for more efficient, reliable, and cost-effective drug development
The shift towards neurotech and digital tools in Alzheimer's research shows promise for advancing diagnostics and treatments, challenging the status quo of reliance on paper-based measures

Machine learning is becoming an important tool in developmental biology, helping to analyze large datasets efficiently. It can aid in tasks like image analysis and cell grouping.
There is a growing need for data engineers, with many more job openings in this area compared to data science roles. Training and skills in data engineering are becoming more valuable.
The FDA has released its first action plan for using AI and machine learning in medical software. This shows a commitment to improving healthcare with technology.

Observing someone's suffering increases when we disconnect the process of eating from its natural form.
Historically, advances in clinical nutrition mirror advancements in surgical procedures.
Feeding tube technology, while important for sustenance, can also present a challenge due to its disconnect from the human experience of dining.

AI systems can sometimes appear successful based on unintended factors, such as background images, rather than the desired data.
AI reproducibility issues can arise when original research findings cannot be accurately replicated or verified.
The validity and reliability of AI-based techniques require thorough evaluation and validation procedures.