As advancements continue, AI technology is rapidly gaining popularity across various industries and sectors. While the technology has many exciting applications in the field of medicine, we are beginning to see the dangers it poses to patients. Automation bias, misinterpretation of commands, and software glitches can lead to medical misdiagnoses, preventing patients from getting the care they need. Here is what you need to know about this evolving issue.
How AI Is Used in Medicine
AI technology has a broad range of applications in the medical field. One of its primary uses is computer vision, which involves interpreting and visualizing data with special algorithms. AI can analyze medical images such as X-rays, MRIs, and CT scans to identify diseases, track disease progression, and support radiologists in making more accurate diagnoses.
Time series analysis is another major application. It examines complex datasets over time to forecast future events or trends. This can mean monitoring patient vital signs in real time to predict and prevent adverse events or analyzing the progression of diseases such as diabetes and heart conditions.
Natural Language Processing (NLP) also has several uses in AI-driven medicine. Speech recognition can be used to transcribe patient-provider conversations, allowing for more efficient documentation and freeing healthcare professionals to focus more on patient care. This technology can also facilitate voice-activated commands in surgical settings or for patients with physical impairments, enhancing accessibility and efficiency.
Information extraction, also made possible by NLP, enables the pulling of meaningful information from unstructured text data, such as clinical notes or research articles. This aids in clinical decision support, patient triage, and the personalization of care plans.
How AI Can Cause Diagnostic Errors
While AI has many potentially useful applications, its use can lead to misdiagnoses through:
- Automation Bias: Healthcare professionals might over-rely on AI-driven diagnostic tools, assuming these systems are error-free. This bias can lead to disregarding other clinical signs or symptoms and subsequent misdiagnosis. One revealing study showed that even experienced doctors assumed that the information an AI system gave about a mammogram result was accurate and would reach the same conclusion, even when the system provided inaccurate results.
- Confirmation Bias: Alternatively, physicians might underuse AI’s predictive abilities to play it safe. In a study conducted at Johns Hopkins University, researchers found that physicians primarily used AI in “low uncertainty” situations, essentially confirming what they already knew, rather than in “high uncertainty” situations, where its predictive abilities could better augment a physician’s own experience. The reason, the researchers discovered, was a fear of legal liability on the part of the studied physicians.
- Incorrect Speech Relay: Speech recognition programs might inaccurately transcribe patient histories or doctor’s notes, leading to misinformation. A speech recognition system that incorrectly relays medical terms or patient symptoms could result in improper diagnosis or treatment plans.
- Misinterpretations of Words or Images by AI: Natural Language Processing (NLP) systems might misinterpret the nuances of human language, leading to errors in processing patient information. Similarly, computer vision systems may incorrectly analyze medical images due to their inherent limitations or biases in the training data, potentially leading to incorrect diagnoses, according to the Patient Safety Network.
- Software Rot: Over time, software can degrade or become outdated – a phenomenon known as software rot. Older diagnostic software may lead to erroneous conclusions based on outdated information or methodologies if it is not regularly updated to reflect the latest medical knowledge and algorithms.
- Programming Glitches: AI systems are only as good as the code they run on. Bugs or glitches in the programming of AI diagnostic tools can introduce errors in medical data analysis, leading to incorrect diagnoses. These errors might arise from oversights during the development phase or from unexpected interactions between different parts of the AI system.
Contact an Experienced Medical Malpractice Attorney
Have you suffered an adverse medical outcome because of AI misdiagnosis? If so, you need an experienced medical malpractice lawyer to help you seek the fair compensation you deserve. Contact the team at Salvi Schostok & Pritchard P.C. today to get started with your free consultation.
