  Introduction: Cases of throat cancer are rising worldwide. With survival
decreasing significantly at later stages, early detection is vital. Artificial
intelligence (AI) and machine learning (ML) have the potential to detect throat
cancer from patient speech, facilitating earlier diagnosis and reducing the
burden on overstretched healthcare systems. However, no comprehensive review
has explored the use of AI and ML for detecting throat cancer from speech. This
review aims to fill this gap by evaluating how these technologies perform and
identifying issues that need to be addressed in future research. Materials and
Methods: We conducted a scoping literature review across three databases:
Scopus,Web of Science, and PubMed. We included articles that classified speech
using machine learning and specified the inclusion of throat cancer patients in
their data. Articles were categorized based on whether they performed binary or
multi-class classification. Results: We found 27 articles fitting our inclusion
criteria, 12 performing binary classification, 13 performing multi-class
classification, and two that do both binary and multiclass classification. The
most common classification method used was neural networks, and the most
frequently extracted feature was mel-spectrograms. We also documented
pre-processing methods and classifier performance. We compared each article
against the TRIPOD-AI checklist, which showed a significant lack of open
science, with only one article sharing code and only three using open-access
data. Conclusion: Open-source code is essential for external validation and
further development in this field. Our review indicates that no single method
or specific feature consistently outperforms others in detecting throat cancer
from speech. Future research should focus on standardizing methodologies and
improving the reproducibility of results.
