AI in Healthcare Crossword

3. When AI is too simple and misses important patterns in medical data.
4. A measurable sign in the body that can indicate disease or health risk.
7. The study of proteins in the body, which can help AI understand disease activity.
8. Testing an AI system to make sure it works safely and accurately.
9. When AI memorizes training data too closely and performs poorly on new cases.
10. Sorting patients by how urgently they need medical care.
12. Dividing patients into risk groups based on their likelihood of disease or complications.
13. The study of small molecules in the body that show changes in health or disease.
15. AI analysis of tissue or cell images to detect disease patterns.
16. Removing personal details from patient data to protect privacy.
21. Using AI to support doctors rather than fully replace them.
22. Adjusting an AI model so its predictions match real-world outcomes more accurately.
23. How well an AI system works on new patients outside its training data.

1. The study of a person’s genes, often used by AI to predict disease risk.
2. How easily a doctor can understand the logic behind an AI result.
5. The study of RNA activity, helping AI understand how genes are being expressed.
6. Changing data so a patient’s identity cannot easily be traced.
7. Classifying patients based on symptoms, genes, behaviors, or medical history.
11. The ability of different healthcare systems to share and understand data.
14. AI analysis of hidden patterns in medical images like X-rays, CT scans, or MRIs.
17. Replacing private patient information with coded values to protect identity.
18. How clearly humans can understand why an AI made a decision.
19. Using technology to complete tasks with little or no human effort.
20. Technology that allows a doctor to appear or operate from a distant location.