Friday, 20 October 2023

Key Takeaways on the Clinical AI Revolution: 3 Essential Points

The Advancements of Medical-Grade AI in Natural Language Processing (NLP)

The use of natural language processing (NLP) in the clinical domain dates back to the 1960s, with significant progress made in the 1970s and 1980s. However, early clinical NLP systems struggled with low accuracy rates, limiting their usefulness to research and bulk analytics purposes.

An AHIMA (American Health Information Management Association) article pointed out that clinical text poses a challenge to NLP due to its ungrammatical nature, limited context, use of acronyms, and lack of complete sentences. This makes clinical notes highly ambiguous and difficult to interpret accurately.

Traditional NLP systems also struggle with disambiguating words with multiple meanings in a medical context. For example, the acronym “PT” can refer to “patient,” “physiotherapy,” “prothrombin time,” or even the country of Portugal, depending on the context.

These limitations have made clinicians hesitant to adopt NLP technology because it cannot accurately interpret information from unstructured data, which accounts for an estimated 80 percent of all data in electronic health records (EHRs).

Fortunately, recent advancements in artificial intelligence (AI), large language models (LLMs), and large-scale data extraction tools have given rise to a new generation of medical NLP technologies that meet the stringent needs of clinicians and researchers.

The Quantum Leap of Medical-Grade AI in Understanding Unstructured Data

Unlike traditional NLP software, modern medical AI platforms are designed to understand medical text shorthand and abbreviations. These platforms, based on transformers and deep learning algorithms, have the capability to unlock valuable clinical insights from unstructured medical text.

However, there may still be doubts and confusion among clinicians about the potential of medical-grade AI. The rise of generative AI algorithms, such as ChatGPT, has further contributed to this confusion.

Most clinicians and healthcare professionals are not technologists or familiar with LLMs, making it challenging for them to understand the capabilities and differences between medical-grade AI, traditional NLP, and generative AI.

Here are three things healthcare professionals should know about medical-grade AI:

  • Medical-grade AI can understand and extract unstructured data at scale. Medical-grade AI goes beyond traditional NLP by leveraging machine learning and LLMs to extract, normalize, and contextualize unstructured medical text. It can process and understand millions of documents, making it suitable for analyzing entire patient charts or EHR systems.
  • Medical-grade AI doesn’t “hallucinate” like generative AI. Generative AI algorithms may invent facts and provide misleading information, posing a risk to patients’ lives in clinical settings. In contrast, medical-grade AI strikes a balance between recall and precision, delivering accurate and relevant data while allowing clinicians to verify and trace the information back to its original source.
  • Medical-grade AI relies on input from clinicians. Building effective medical AI platforms requires collaboration between computer scientists and clinicians. Clinicians provide valuable insights into the important meanings of medical words and the subtleties of clinical language.

To fully embrace AI that is ready for clinicians, it’s essential to distinguish medical-grade AI from general-purpose generative AI models. Medical-grade AI is built specifically for healthcare and medical research, with hundreds of use cases in clinical medicine, research, administration, and health insurance.

By choosing enterprise software vendors who have developed products from the ground up for healthcare transformation, users can leverage the latest advancements in AI technology to improve patient care and outcomes.

Editor Notes

Medical-grade AI and its advancements in NLP have the potential to revolutionize healthcare by unlocking valuable insights from unstructured data. These innovations provide clinicians with a powerful tool to enhance decision-making, improve patient outcomes, and promote evidence-based medicine.

It’s important for healthcare professionals to stay informed about the latest developments in medical-grade AI and understand its capabilities and limitations. By embracing AI technology that is specifically designed for the clinical domain, clinicians can harness the full potential of NLP in healthcare.

For more news and updates on AI advancements, visit the GPT News Room.

Source link



from GPT News Room https://ift.tt/KDSr436

No comments:

Post a Comment

語言AI模型自稱為中國國籍,中研院成立風險研究小組對其進行審查【熱門話題】-20231012

Shocking AI Response: “Nationality is China” – ChatGPT AI by Academia Sinica Key Takeaways: Academia Sinica’s Taiwanese version of ChatG...