The podcast explores the progress of AI in medicine, the historical foundations of diagnostic thinking, the limitations of diagnostic algorithms, the Hawthorne Effect in observational studies, propensity scoring in pharmaco epidemiology, and the evolution of prediction tools and AI.
The invention of AAPHELP in the 1970s marked the beginning of clinical decision support systems and the potential for computers to outperform human clinicians in diagnostic accuracy.
Spectrum bias, influenced by disease prevalence and patient presentation, can impact the effectiveness of diagnostic algorithms, emphasizing the importance of considering regional variations and local disease prevalence in developing diagnostic tools.
Deep dives
The Development of Clinical Decision Support and the Importance of Diagnostic Artificial Intelligence
This podcast episode explores the development of clinical decision support and the role of diagnostic artificial intelligence in modern medicine. It discusses the foundational paper by Ledley and Lestad, which argued for the need to reconceive diagnosis on a probabilistic basis. The episode highlights the work of Tim D'Dambal, a surgeon and researcher, who developed a computer program called AAP Help to assist in diagnosing acute abdominal pain. The program demonstrated higher diagnostic accuracy than human clinicians, leading to improved patient outcomes. However, the limitations of the program, such as spectrum bias and the reliance on reliable data collection, were also recognized. The episode concludes by highlighting the potential of artificial intelligence in diagnostic decision-making and the ongoing challenges in building a generalized diagnostic machine.
Spectrum Bias and Geographic Portability
The episode discusses the concept of spectrum bias, wherein the prevalence of diseases and cultural differences in patient presentations can impact the effectiveness of diagnosis. This bias was observed in the performance of diagnostic algorithms, such as AAP Help, which showed decreased accuracy when applied to different patient populations. The importance of considering regional variations and local disease prevalence when developing diagnostic tools is emphasized. The episode also touches on the need for in-depth data analysis and the ability of computers to identify predictive factors that human clinicians may overlook. It suggests that artificial intelligence systems can offer valuable insights by utilizing vast amounts of data and making inferences beyond human cognitive capacity.
The Success and Limitations of AAP Help
The episode highlights the success of the AAP Help computer program created by Tim D'Dambal. AAP Help demonstrated high diagnostic accuracy, surpassing the abilities of human clinicians. It significantly improved outcomes by facilitating timely surgeries for patients who needed them and reducing unnecessary procedures. However, its effectiveness varied when applied to different geographical locations and broader diagnostic scenarios. The episode notes that while computer assistance can enhance diagnostic capabilities, the role of human clinicians in collecting accurate patient data and performing clinical examinations remains crucial. It emphasizes the need for collaboration between humans and machines for optimal diagnostic outcomes.
The Future of Artificial Intelligence in Diagnosis
The podcast episode concludes by discussing the future implications of artificial intelligence in clinical diagnosis. It acknowledges the advancements in machine learning and the ability of computers to identify predictive factors for diagnosis. The potential for AI to analyze large-scale data and update diagnostic models in real-time is highlighted. While recognizing the current limitations and challenges in creating a generalized diagnostic machine, the episode suggests that continued research and collaboration between clinicians and AI systems will lead to further improvements in diagnostic accuracy and patient care.
What does it mean when a computer can make better medical decisions than a human? The progress in large language models, and in particular the popularity of ChatGPT, has brought these questions to the forefront in 2023, but we’ve been discussing this for over 50 years. In this episode, Dr. Shani Herzig and I are going all the way back to the early 1970s with the invention of AAPHELP, the first real clinical decision support system, and the first time doctors had to contemplate working with – or competing against – computer systems.
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