🚨 Pulse of Innovation 🚨

Breaking news in the healthcare AI

Eleven Labs - Enhancing Healthcare Communication with AI Voices

You all would agree with us that “effective communication” is the cornerstone of providing quality healthcare to patients globally. This area is prime for AI to tackle, as it can incorporate Humans into AI and overcome the barrier of effective communication.

ElevenLabs uses AI-powered text-to-speech technology to improve healthcare communication. Their solutions address language barriers, resource limitations, and accessibility needs, offering:

• Efficient Information Dissemination: Delivering medical instructions and public health updates.

• Multilingual Patient Support: Breaking language barriers with AI voices in multiple languages.

• Empathetic and Clear AI Voices: Enhancing patient experience with warm, clear communication.

• Streamlined Healthcare Workflows: Automating routine tasks and integrating seamlessly into existing systems.

• Accessible Information: Providing equal access for patients with disabilities.

🩺 Start-Up Stethoscope 💵 

Trending startups and technologies impacting clinical practice

We need your help! In this section, we focus on new Healthcare AI companies and briefly summarize what they offer.

🧑🏼‍🔬 Bench to Bedside👨🏽‍🔬

Developments in healthcare AI research and innovations

Automated De-identification Tool for EHRs

The utility of AI for a de-identification system has the potential to revolutionize medical practice by enhancing the accessibility and utility of EHRs for medical research. The use of EHRs is constrained by strict privacy regulations, e.g., de-identification, a labor-intensive process.

In the study, authors identify few key factors:

• Accuracy: The ensemble de-identification tool achieved a high accuracy rate, outperforming other methods. Specifically, it showed a Precision of 98.5% and a Recall of 97.3%.

• Adaptability: The tool was successfully adapted to different types of medical records and various de-identification standards. It maintained high performance across diverse datasets, showing a less than 1% drop in accuracy when applied to different types of EHRs.

• Speed: The tool significantly reduced the time required for de-identification. It processed records at a rate of 10,000 records per hour, compared to manual de-identification, which can take several hours per record.

For this tool to be effective, it requires continuous updates to keep up with evolving medical terminology, and concerns for data privacy. Nonetheless, this brings a major advance in using Real-world data extraction using AI.

👩‍⚕️AI in the Clinic 🏥

Real-world and practical use of healthcare AI in clinic

Scite.AI helps get real references for your academic research

Scite.ai is an innovative platform designed to enhance scientific research and literature evaluation. Its key features include:

1. Smart Citations: This core feature provides context for how publications are cited, classifying citations as supporting, contrasting, or mentioning evidence.

2. Comprehensive Search: Users can search across metadata (titles, abstracts) and Citation Statements extracted from full-text articles.

3. Collection Management: Researchers can build and manage collections of articles, receiving aggregate insights and notifications.

4. AI-Powered Assistant: Scite offers an LLM-based assistant that leverages its Smart Citations database to minimize hallucinations and improve information quality.

Where Scite.ai might not fit:

1. Early-stage research: The Smart Citations feature may not be as effective for very new or niche topics with limited citations.

2. Users seeking full-text access: Scite provides citation context but doesn't replace access to full articles.

3. Budget constraints: Small research teams or individual researchers might find the cost prohibitive if their institution does not provide it.

🤖Patients First, AI Second🤖

Ethical and regulatory landscape of healthcare AI

The largest Nursing Union in the US is sounding the alarm about the use of AI in healthcare!

• In a recent survey by National Nurses United (NNU) of 2,300 registered nurses across the US, 60% of nurses don’t trust their employers to prioritize patient safety when implementing AI. This has led to issues raised by NNU:

  1. Patient Safety: Concerns that AI might not account for individual patient needs and nuances

  2. Job Security: Fears that AI could lead to staffing cuts or reduced roles for nurses

  3. Data Privacy: Worries about the security and ethical use of patient data in AI systems

  4. Clinical Judgment: Emphasis on the irreplaceable value of human expertise and intuition in patient care.

• NNU recently protested at major hospitals, including Kaiser Permanente in San Francisco, and proposed some solutions.

  • Introducing a "Nurses' and Patients' Bill of Rights" regarding AI use ensures that nurses are involved in AI development and implementation process and that human oversight is maintained.

  • Call for transparency in AI algorithms, decision-making processes, and regular audits of AI systems for bias and errors.

Here at AI Grand Rounds, we strongly advocate for Augmented AI where humans are in the loop while maintaining transparency and oversight to create a safe and ethical AI system for our patients.