A new study has found that AI-assisted interviews can significantly improve the accuracy of diagnosing mental health conditions compared to traditional clinical rating scales. In the research, 303 participants engaged in conversations with Alba, an AI-powered assistant designed to conduct structured psychiatric assessments. After the interviews, Alba analyzed the responses and generated preliminary diagnostic suggestions. The results showed that the AI’s diagnostic recommendations aligned more closely with clinical diagnoses made by trained psychiatrists than those derived from standard rating scales commonly used in healthcare settings. These scales, while widely applied, often rely on self-reported symptoms and can be subjective or inconsistent across clinicians. Alba was developed to simulate empathetic, open-ended conversations, using natural language processing to detect patterns in speech, emotional cues, and symptom expression. The AI was trained on extensive psychiatric data and clinical guidelines to ensure its assessments were both thorough and clinically relevant. Researchers noted that the AI’s ability to maintain consistent questioning, avoid bias, and analyze vast amounts of verbal data in real time contributed to its superior performance. The system also flagged subtle indicators—such as changes in speech rhythm or word choice—that might be missed during a standard human-led interview. Experts say the findings suggest AI assistants could serve as valuable tools in mental health care, particularly in settings where access to trained psychiatrists is limited. While the AI does not replace human clinicians, it can support them by providing more accurate initial assessments, reducing diagnostic errors, and helping prioritize patients who need urgent care. The study underscores the growing potential of AI to enhance clinical decision-making in mental health, offering a scalable and objective complement to traditional evaluation methods.