Sina Bari MD

When AI models are deployed without thorough validation, the results can be unpredictable. Validation frameworks provide the structure needed to test, refine, and verify AI systems for consistency and accuracy. From model benchmarking to error analysis, these frameworks make AI more transparent and dependable. They also enhance user confidence by proving that the system can perform well under varied conditions. To explore in detail how validation frameworks boost AI performance, discover more .

  AI telemonitoring combines compassion and technology to support seniors living at home. Using connected devices and advanced algorithms, it monitors vital signs and daily activity, catching health concerns early. This helps reduce costly hospitalizations while preserving personal freedom. For families, it means less worry and more quality time. For seniors, it means independence without isolation. With transparent data practices, AI becomes a trusted companion promoting safety, dignity, and peace of mind. 👉 Read more here!

Dr. Sina Bari shares why generative AI scribes now face FDA oversight. These tools do more than transcribe—they interpret and shape clinical notes that impact patient care. Errors can lead to clinical risks, moving them into the FDA’s regulatory scope. With no LLM-based ambient scribe cleared yet, developers should now prepare for De Novo classification and gather validation data. As trust and safety become priorities, the shift toward regulated AI scribes will define how these tools can reduce burnout while protecting patient outcomes. Continue reading ...

  In this enlightening interview, Sarah Chen engages with Dr. Sina Bari, a plastic surgeon and medical AI innovator, about the hurdles hospitals face when implementing AI governance. Dr. Bari reveals that AI governance is often unclear, leading to inconsistent and sometimes ineffective approaches across healthcare institutions—from minimal oversight to overly complex committees. He emphasizes the critical role of clinician involvement early in the process and recommends starting with low-risk AI applications to build trust and ensure safety. Dr. Bari explains the importance of establishing a “ground truth” system to verify AI outputs for accuracy continuously. He discusses the regulatory landscape, acknowledging the FDA’s efforts to regulate AI in medicine but noting the challenges posed by rapid technological change. Addressing physician skepticism fueled by early AI errors, he advocates for honest communication about AI’s realistic capabilities. Looking ahead, Dr. Bari foresees i...

With the rapid advancement of technology , particularly in artificial intelligence (AI) and machine learning, there is growing speculation about the possibility of doctors being replaced by computers in the field of healthcare. While computers have demonstrated remarkable capabilities in assisting with diagnostics and treatment, the notion of complete substitution remains a topic of debate. In this article, we will explore the evolving relationship between doctors and computers, highlighting the importance of striking a balance that leverages the strengths of both to improve patient care. The Transformative Potential of Computers in Healthcare Computers, driven by AI algorithms, have made significant contributions to the field of healthcare. They excel in processing and analyzing vast amounts of medical data, enabling rapid and accurate diagnoses. Moreover, AI systems have shown promise in identifying patterns and correlations in patient data that can assist doctors in formulating pers...

  Artificial intelligence (AI) chatbots are becoming more popular and accessible as a way of communicating with various online services and platforms. However, not all chatbots are created equal, and some may pose serious risks to public health by spreading medical misinformation. A recent article by The Atlantic revealed how some AI chatbots, powered by large language models such as GPT-4, can generate misleading or inaccurate responses when asked about health-related topics. For example, one chatbot claimed that vaccines cause autism , another suggested that drinking bleach can cure COVID-19, and another advised against wearing masks to prevent infection. These chatbots are not intentionally lying or malicious, but they are simply repeating what they have learned from analyzing huge amounts of text data from the internet. The problem is that this data may contain false, outdated, biased, or incomplete information that can confuse or harm users who rely on chatbots for medical ad...