{"id":18748,"date":"2024-12-08T08:16:05","date_gmt":"2024-12-08T08:16:05","guid":{"rendered":"\/?p=18748"},"modified":"2024-12-08T20:12:01","modified_gmt":"2024-12-08T20:12:01","slug":"issues-in-using-ais-for-meningitis-and-other-exact-terms-on-the-internet","status":"publish","type":"post","link":"\/?p=18748","title":{"rendered":"Issues in using AIs for “meningitis” and other exact terms on the Internet"},"content":{"rendered":"
\n
\n
\n
\n
\n
\n
\n
\n
\n
Issues in using AIs for “meningitis” and other exact terms on the Internet<\/strong><\/em><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n
<\/div>\n
Richard Collins:<\/strong><\/div>\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
<\/div>\n
What are the routes of infection for meningitis? Separate fungal, bacterial and viral. How good can diagnoses be if done by AIs with good tools and tests? The checklists with probabilities that are done by traditional databases and statistical frequencies are not substantially improved by using LLM methods – which are indirect – since the training, encoding and validation are not specific to medical diagnosis. And most of the training data is not in the public domain, rather locked in copyrights, publishers contracts, research ownership agreements, trademarks, and patents. LLM is just ONE of many data management and statistical recall tools.<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n
\n
OpenAI ChatGPT Plus:<\/strong><\/span><\/h6>\n
\n
\n
\n
\n
\n
\n
\n
\n

Routes of Infection for Meningitis<\/strong><\/h3>\n

Meningitis is the inflammation of the meninges, the protective membranes covering the brain and spinal cord. The routes of infection vary depending on the causative agent:<\/p>\n

1. Bacterial Meningitis<\/strong><\/h4>\n

Bacterial meningitis is often severe and requires immediate medical intervention. Common routes of infection include:<\/p>\n

    \n
  • Respiratory Droplets<\/strong>: Pathogens like Neisseria meningitidis<\/em> and Streptococcus pneumoniae<\/em> are transmitted via coughing, sneezing, or close contact with an infected person.<\/li>\n
  • Hematogenous Spread<\/strong>: Bacteria in the bloodstream can cross the blood-brain barrier (e.g., from otitis media or pneumonia).<\/li>\n
  • Direct Spread<\/strong>: From contiguous sites like:\n
      \n
    • Sinus infections<\/li>\n
    • Skull fractures or head trauma<\/li>\n
    • Post-surgical complications (e.g., neurosurgery)<\/li>\n<\/ul>\n<\/li>\n
    • Vertical Transmission<\/strong>: From mother to baby during childbirth (e.g., Group B Streptococcus<\/em>, Escherichia coli<\/em>).<\/li>\n<\/ul>\n

      2. Viral Meningitis<\/strong><\/h4>\n

      Viral meningitis, often less severe, is caused by viruses such as enteroviruses, herpesviruses, or arboviruses. Routes of infection include:<\/p>\n

        \n
      • Fecal-Oral Route<\/strong>: Common for enteroviruses.<\/li>\n
      • Respiratory Droplets<\/strong>: Spread of viruses like mumps<\/em> or measles<\/em>.<\/li>\n
      • Arthropod Vectors<\/strong>: Mosquitoes and ticks transmit arboviruses like West Nile virus.<\/li>\n
      • Vertical Transmission<\/strong>: From mother to baby in cases like herpes simplex virus.<\/li>\n<\/ul>\n

        3. Fungal Meningitis<\/strong><\/h4>\n

        Fungal meningitis is rare and usually affects immunocompromised individuals. Common routes include:<\/p>\n

          \n
        • Inhalation of Spores<\/strong>: Fungi like Cryptococcus neoformans<\/em> or Histoplasma capsulatum<\/em> are inhaled and disseminate through the bloodstream.<\/li>\n
        • Direct Inoculation<\/strong>: Post-surgical or traumatic introduction of fungal agents.<\/li>\n
        • Hematogenous Spread<\/strong>: Fungal infections elsewhere in the body spread to the central nervous system.<\/li>\n<\/ul>\n
          \n

          Effectiveness of AI in Diagnosing Meningitis<\/strong><\/h3>\n

          AI has the potential to improve diagnostic accuracy for meningitis, but it is constrained by the quality and specificity of available data. Key considerations include:<\/p>\n

          Strengths of AI in Diagnoses:<\/strong><\/h4>\n
            \n
          1. Integration of Multi-Modal Data<\/strong>:\n
              \n
            • AI can synthesize symptoms, imaging (CT\/MRI), and lab results (e.g., cerebrospinal fluid analysis) for rapid diagnosis.<\/li>\n<\/ul>\n<\/li>\n
            • Pattern Recognition<\/strong>:\n
                \n
              • Algorithms excel in identifying subtle patterns that may be missed by human clinicians.<\/li>\n<\/ul>\n<\/li>\n
              • Speed<\/strong>:\n
                  \n
                • Faster processing of large datasets, enabling quicker diagnostic decisions.<\/li>\n<\/ul>\n<\/li>\n
                • Custom Risk Profiling<\/strong>:\n
                    \n
                  • Personalized risk assessments based on patient history and demographics.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n

                    Limitations of Current AI Approaches:<\/strong><\/h4>\n
                      \n
                    1. Training Data Bias<\/strong>:\n
                        \n
                      • AI systems rely on historical datasets, which may not represent diverse populations or rare cases.<\/li>\n<\/ul>\n<\/li>\n
                      • Data Silos and Restrictions<\/strong>:\n
                          \n
                        • Proprietary restrictions on medical data limit comprehensive training and validation.<\/li>\n<\/ul>\n<\/li>\n
                        • Limited Understanding of Pathophysiology<\/strong>:\n
                            \n
                          • LLMs and other AIs can infer correlations but lack the ability to understand causal mechanisms in disease processes.<\/li>\n<\/ul>\n<\/li>\n
                          • Checklists and Probabilities<\/strong>:\n
                              \n
                            • Many current AI implementations rely on statistical recall and traditional algorithms rather than truly innovative diagnostic pathways.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
                              \n

                              LLM-Specific Challenges:<\/strong><\/h3>\n
                                \n
                              • Indirectness<\/strong>: LLMs are language models, not designed for direct diagnostic analysis. They excel at synthesizing textual input but cannot inherently interpret raw medical data.<\/li>\n
                              • Encoding Errors<\/strong>: The general-purpose training of LLMs means they may embed outdated or generalized medical knowledge, which may not align with clinical best practices.<\/li>\n
                              • Validation Gaps<\/strong>: Medical AI tools require rigorous validation with live data from patients\u2014a challenge given the proprietary nature of much medical information.<\/li>\n<\/ul>\n
                                \n

                                Beyond LLMs: Multi-Tool AI for Medicine<\/strong><\/h3>\n

                                Effective diagnostic systems should integrate:<\/p>\n

                                  \n
                                1. Knowledge Graphs<\/strong>: Structured repositories of medical information for dynamic querying.<\/li>\n
                                2. Expert Systems<\/strong>: Rule-based systems designed for specific medical domains.<\/li>\n
                                3. Signal Processing AIs<\/strong>: For real-time analysis of imaging, lab results, and physiological data.<\/li>\n
                                4. Machine Learning Models<\/strong>: Trained on clinical datasets with direct applications in infectious diseases.<\/li>\n<\/ol>\n

                                  LLMs are a powerful piece of the AI landscape, but their best role in medicine is likely as a supporting tool<\/strong> for synthesizing medical literature and aiding clinician decision-making, rather than serving as standalone diagnostic engines.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

                                  \n
                                  \n
                                  \n

                                   <\/p>\n

                                  <\/div>\n
                                  4o<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n
                                  <\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n
                                  \n
                                  You said:<\/h5>\n
                                  \n
                                  \n
                                  \n
                                  \n
                                  \n
                                  \n
                                  \n
                                  \n
                                  You are looking at routes to infect the individual. I want to see the sites and pathways in the body where the bacteria or virus or fungus can enter the brain or spinal chord. Are you saying that bacteria routinely cross the blood brain barrier, or eyes, ears nose and moth or injuries to the blood and brain? The fast processing of datasets is a factor for anyone using a computer algorithm. The body of knowledge is fixed, though it might vary in situations. The computers use in database statistical algorithms can as well be used for LLM algorithms and visa versa. You are not balanced in your presentation. but giving benefits without looking at alternatives. Including starting all over on a new track. In a society that spends tens of trillions on health and disease, spending even a few billion short term might well be the better choice for society.<\/div>\n