Top Tweets for #AddingGenes

Google just unveiled its 2024 vision for Generative AI in healthcare, and it aligns with what I have been sharing in my public talks. Essentially, there are 3 main opportunities: 1️⃣ Near-term: Reducing administrative work The immediate focus is on reducing the administrative burden for healthcare professionals. We're looking at applications that automate mundane tasks-automated coding, report generation, note summarization, and medical transcription. This will not only increase the efficiency of healthcare facilities by allowing staff to focus more on patient care rather than paperwork but also reduce the likelihood of human error and burnout. Notably, these applications could reach the market swiftly, with minimal regulatory hurdles. Examples include the Epic + Microsoft collaboration to improve clinical documentation (https://t.co/QlJi3gDw5Q), Nabla's recent $24 million Series B funding for medical transcription (https://t.co/r5buSuJVJc), and many more. 2️⃣ Short-term: Clinical care support The short-term goal would be to provide support for clinical care through technology. Applications will be created that can assist frontline workers and patients by providing accurate medical information. These may include GenAI-powered search agents, which can offer instant responses to medical queries, aid in decision-making, and further provide evidence-based recommendations. This could improve the quality of care by making reliable information more accessible and by supporting healthcare providers in their diagnostic and treatment processes. Google Cloud's collaboration with Mayo Clinic in developing enterprise search capabilities with GenAI is a good example of this progress (https://t.co/NwanA6xCZr). 3️⃣ Moonshot: Personalized care The moonshot goal would be to use foundation models to crack the holy grail in medicine, which is creating personalized care journeys for patients, including diagnostics and treatment recommendations tailored to the individual. These multimodal models would be able to analyze a patient's unique health profile and predict the most effective treatments, leading to better patient outcomes and a more personalized healthcare experience. A good example of where this future lies is the 2023 Nature paper (https://t.co/NmD1mVaOCp) that showed that LLMs can outperform traditional models in predicting hospital mortality and readmission rates by evaluating unstructured clinical records. Google’s 3 predictions for AI in healthcare in 2024: https://t.co/F5gvQmjotS