AI in Healthcare has the potential to fundamentally change medical care – especially in diagnostics. However, as Dr. Tina Madoharan clearly points out in the Scavenger podcast, the true value of AI lies not in pure speed, but in better decisions, higher precision, and earlier detection of diseases.

Many AI applications start with efficiency gains: automated documentation, prioritization of cases, or administrative relief for medical staff. These use cases are important – but they fall short. The crucial point is where AI truly helps clinically: for instance, in the early detection of cancer, in analyzing tissue at the cellular and molecular level, or in supporting complex diagnostic decisions that are not possible for the human eye alone.

Dr. Madoharan cites concrete examples: AI can already quantify tumors, count mitoses, segment image data, or mark anomalies in radiology and pathology images. In patient monitoring, AI helps prioritize a multitude of vital parameters that signal an urgent need for action – such as in cases of sepsis or stroke risks. Generative AI is also utilized to summarize findings or uncover inconsistencies in reports.

At the same time, she explains why many AI projects in healthcare fail: lack of integration into clinical workflows, isolated island solutions, and insufficient explainability. Often, the AI perspective lacks a view of the human being and the patients themselves. Trust can only be established when AI is reliable, regulated, transparent, and understandable – especially in a highly sensitive and regulated environment.

Conclusion:
The future of AI in healthcare is not "AI first at any cost," but rather AI assisted with human responsibility. Not necessarily just faster – but above all, better for the patients.