Emotional blind spots of gen AI
Artificial intelligence in healthcare, while increasingly sophisticated, often struggles with emotions because it doesn’t experience them as humans do. Over the past year, the explosion of generative AI platforms has introduced a new wave of prompts and results with serious emotional implications, especially in mental health applications.
Key areas where gen AI may mishandle emotions in healthcare
Lack of Genuine Understanding
AI can analyze and mimic emotional expressions, but it doesn’t truly “feel” emotions. Its responses are based on patterns and data rather than genuine emotional experiences, which can make them seem insincere or robotic.
Contextual Misinterpretation
AI may struggle to grasp the full context of a situation, leading to inappropriate or misplaced emotional responses. For example, an AI might offer a cheerful response in a situation that requires empathy or solemnity.
- Nuance and subtlety: Emotions are complex and often involve subtle nuances. AI may miss these subtleties, leading to oversimplified or inaccurate interpretations. Detecting sarcasm, irony, or mixed emotions can be particularly challenging.
- Cultural differences: Emotional expressions and interpretations vary across cultures. AI trained on data from one culture might misinterpret emotions in another cultural context.
- Consistency and predictability: Human emotions are dynamic and can change rapidly. AI, however, may offer responses that seem overly consistent or predictable, lacking the fluidity and spontaneity of real human emotion.
- Overreliance on text or data: Many AI models rely heavily on text or structured data to interpret emotions. This is limiting because emotions are often conveyed through tone, body language, and other non-verbal cues that AI may not fully comprehend.
- Ethical and privacy concerns: Attempts to quantify and respond to emotions can raise ethical issues. For instance, using AI to analyze emotional states can spark privacy concerns, particularly if done without transparency or consent.
What Can Healthcare Leaders Do?
- Healthcare leaders must create safe environments for generative AI experimentation. Testing these platforms in sandbox environments, where clinicians and doctors intentionally challenge the algorithms, is crucial. This quality control is no different from that applied to other healthcare software.
- Ensure GenAI development teams are multifunctional, extending beyond just technical expertise.
- Recognize that prompt engineering and optimization are becoming critical to applying GenAI in patient-facing healthcare settings. Teaching users how to ask the right questions and follow-ups will be key to improving algorithm outputs.
Even some of the original skeptics like myself are now realizing that the technology is becoming more reliable with every update. Most feel that unemotional rudimentary and repetitive tasks are the best starting point to assure the “do no harm” pledge.
However, despite the massive amounts of AI-washing on healthcare vendor sell sheets great caution will be needed in deployments that touch mental health and clinical decision support where sentient aspects are a matter of life and death.