From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer.
Satvik TripathiAzadeh TabariArian MansurHarika DabbaraChristopher P BridgeDania DayePublished in: Diagnostics (Basel, Switzerland) (2024)
Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.
Keyphrases
- artificial intelligence
- machine learning
- big data
- deep learning
- healthcare
- poor prognosis
- electronic health record
- long non coding rna
- convolutional neural network
- case report
- mass spectrometry
- single cell
- optical coherence tomography
- squamous cell carcinoma
- papillary thyroid
- decision making
- health information
- risk assessment
- photodynamic therapy
- young adults
- quantum dots
- health insurance
- fluorescence imaging