Artificial Intelligence in Cancer Care: The Role of Algorithms in Data Collection
Advancements in AI Algorithms for Cancer Care
Artificial intelligence (AI) is significantly reshaping the landscape of cancer care. The increasing number of AI algorithms, now nearing a thousand, highlights the need for robust evaluations of their performance within clinical settings. Amy Abernethy, MD, emphasized during the ESMO Congress 2024, the importance of understanding these algorithms' effectiveness not just in theory, but in real-world applications with local patient populations.
Evaluating AI Performance
The algorithms developed from complex data sets must undergo rigorous assessment. Abernethy pointed out three major trends facilitating this evaluation:
- Availability of high-quality, multimodal data sets.
- Developing reliable approaches for algorithm evaluation.
- Advancements in evidence generation methodologies.
Categories of AI Application in Oncology
There are distinct categories of AI applications in oncology, including:
- AI-based medical products that follow established regulatory standards, such as continuous glucose monitors.
- AI for discovery and automation, which may not require regulatory oversight, can be pivotal in improving administrative efficiency.
- AI algorithms supporting clinical trials, enhancing evidence generation and patient matching.
The Evolution of Regulatory Models
As the regulatory models for AI applications advance, the emphasis is progressively shifting towards ensuring continuous evaluation of these intelligent systems. Abernethy states that while some algorithms are allowed to learn over time, they must be scrutinized periodically to minimize risks associated with unintended consequences.
Future Perspectives in Oncology
As technology and data evolve, oncologists must adapt by comprehensively understanding the implications of AI algorithms in practice. This evolving integration holds great promise for enhancing patient care and outcomes, requiring an increasing familiarity with AI systems among healthcare practitioners.
This article was prepared using information from open sources in accordance with the principles of Ethical Policy. The editorial team is not responsible for absolute accuracy, as it relies on data from the sources referenced.