Cognitive Biases and Pattern Recognition - The human brain is wired to identify patterns for survival, but this can lead to finding patterns where they don't exist, a phenomenon called apophenia [1][2][3][4] - Cognitive biases, like availability bias, can cause inaccurate or illogical decision-making, even among skilled professionals such as doctors [5][6][7] - Emotions can impair the ability to understand probability objectively, leading to misinterpretations of data and potentially harmful consequences [9][10][11][12] Artificial Intelligence in Healthcare - AI is a fast and accurate pattern detector capable of processing vast amounts of data, but it is trained on human data and can therefore reflect human biases [7][8][13] - AI can surface potential diagnoses that human doctors may not consider, highlighting its potential as a diagnostic tool [15][16] - A partnership between AI and human intelligence is crucial for accurate diagnoses and better decision-making in high-stakes situations [17][18] Numeracy and Probability - Numeracy, the ability to understand and work with numbers, and probability are essential for objective decision-making [8][9] - Misunderstanding probability can lead to severe consequences, as illustrated by the Sally Clark case where incorrect statistical analysis resulted in a wrongful conviction [10][11][12]

Healthcare Challenges & Opportunities - AI in healthcare aims to augment human intelligence, not replace it, utilizing technologies like machine learning and natural language processing [4][5] - The healthcare industry faces challenges including information overload, clinician burnout, and wasteful spending, with 20% of costs considered wasteful and significant expenses related to billing and administration [8][10][11] - AI can improve diagnosis using image recognition, reduce documentation burden through automated note-taking, and enhance hospital flow by triaging patients and allocating resources [12][13][15] AI Adoption & Concerns - AI adoption in healthcare follows the Gartner hype cycle, with image recognition already productive but disease treatment and behavioral health still facing inflated expectations and disillusionment [6][7] - There is existing bias against AI-generated medical advice, with people finding it less reliable and empathetic compared to advice from human doctors [16][17] - Legal and ethical questions arise regarding data ownership, liability for incorrect AI advice, and potential loss of trust in manual processes due to AI involvement [18][19] - Bias can be built into AI systems through problem selection, data collection methods, and inherent assumptions, potentially leading to skewed outcomes [21] Future Vision - The future vision involves AI-powered wearable devices that can detect health issues, alert emergency services, and transmit vital information to hospitals, improving patient care and outcomes [22][23][24][25]

Healthcare Challenges & Opportunities - Healthcare data overload exceeds human capacity, hindering effective diagnosis and treatment [1] - Delayed or missed diagnoses, particularly in women's health, lead to prolonged suffering and uncertainty [2][3][4] - In women's health, for every one woman diagnosed, four remain undiagnosed or undertreated [5] - Up to 90% of patients are left untreated or undertreated despite available data [5] - Over half of patients with COPD remain undiagnosed or undertreated [10] - Significant variation in healthcare availability exists [20] Pangaea Data's Solution: PALLUX AI Platform - PALLUX mimics physician decision-making by integrating medical knowledge and patient interaction data [8][9] - PALLUX interprets patient data using clinical guidelines to provide real-time responses to clinicians [14][15] - PALLUX connects with different healthcare systems through various interfaces [15] - PALLUX, deployed with the NHS, found six times more undiagnosed cancer cachexia patients compared to traditional methods [19][20] - Early diagnosis via PALLUX can cut cachexia treatment costs by up to 50% and potentially save 1 billion pounds per year [20] - PALLUX helps find more patients who are falling in care gaps and bring them back on to the relevant care pathway [32] Technology & Collaboration - NVIDIA and Microsoft support Pangaea Data with AI technologies, including large language models and NVIDIA GPUs [16][24][25] - NVIDIA FLARE helps train centralized models while preserving patient privacy [24] - Microsoft Azure provides a compliant environment across different countries and territories [22][23] Impact & Future - Early diagnosis and guideline-concordant therapy improve patient outcomes and reduce hospital admissions [16][17] - AI-powered clinicians can provide more empathetic and personalized care [18] - Unlocking health record data can revolutionize the healthcare landscape [19] - Pangaea Data's approach can be applied globally to address challenges in identifying and treating patients [26]