Artificial intelligence (AI) tools can analyze data from individual cells within tumors to forecast whether a patient’s cancer will react to a particular drug. However, the researchers noted challenges in the clinical deployment of AI tools, citing issues like integrating it into existing radiology workflows and establishing regulatory approval processes:
1.Data Quality And Accessibility.
2.Implementation Cost.
3.Acceptance Of AI In Healthcare.
4.Lack Of Experience In Evaluating AI Performance.
5.Technical Complexity And Skill Gaps.
Despite challenges, the increasing adoption of AI in healthcare suggests a transformative future. Overcoming obstacles with innovation can significantly enhance healthcare quality, efficiency and accessibility, making AI a fundamental improvement rather than a fleeting trend.