AIMC Topic: Pathology, Clinical

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Subtype classification of gastric spindle cell tumors in whole slide images.

Computers in biology and medicine
AIMS: Accurate cancer subtype classification is critical due to variations in tumor progression and prognosis. Traditionally, pathologists classified subtypes manually by examining pathological slides under the microscope. To address increasing workl...

Exploring the risks of over-reliance on AI in diagnostic pathology. What lessons can be learned to support the training of young pathologists?

PloS one
The integration of Artificial Intelligence (AI) algorithms into pathology practice presents both opportunities and challenges. Although it can improve accuracy and inter-rater reliability, it is not infallible and can produce erroneous diagnoses, hen...

The case for homebrew AI in diagnostic pathology.

The Journal of pathology
Artificial intelligence (AI) methods for digital pathology have tremendous potential to improve cancer diagnostics, biomarkers, and ultimately patient care. These AI methods, if marketed and sold, require authorisation or clearance as in vitro diagno...

Practical implementation of AI in a non-academic, non-commercial Pathology laboratory: Real world experience and lessons learned.

Histopathology
AIMS: As pathology departments transition towards digital workflows, the integration of artificial intelligence (AI) is anticipated to become increasingly common. This study aimed to describe the real-world implementation and impact of AI integration...

Reshaping Organizational Culture in Pathology.

Clinics in laboratory medicine
"Reshaping Pathology Culture" explores the transformation needed in pathology departments to meet the demands of modern health care. It advocates a shift from traditional hierarchical models to collaborative leadership, uniting cross-generational pat...

Digital pathology in veterinary clinical pathology: A review.

Veterinary pathology
Digital pathology has rapidly evolved in the field of veterinary medicine. Although digital histology advancements are widely discussed, clinical pathology specimens are also being digitized for a variety of purposes. These digital images can be used...

Artificial intelligence in digital pathology - time for a reality check.

Nature reviews. Clinical oncology
The past decade has seen the introduction of artificial intelligence (AI)-based approaches aimed at optimizing several workflows across many medical specialties. In clinical oncology, the most promising applications include those involving image anal...

Annotation Practices in Computational Pathology: A European Society of Digital and Integrative Pathology (ESDIP) Survey Study.

Laboratory investigation; a journal of technical methods and pathology
Integrating digital pathology and artificial intelligence (AI) algorithms can potentially improve diagnostic practice and precision medicine. Developing reliable, generalizable, and comparable AI algorithms depends on access to meticulously annotated...

Whole Slide Imaging, Artificial Intelligence, and Machine Learning in Pediatric and Perinatal Pathology: Current Status and Future Directions.

Pediatric and developmental pathology : the official journal of the Society for Pediatric Pathology and the Paediatric Pathology Society
The integration of artificial intelligence (AI) into healthcare is becoming increasingly mainstream. Leveraging digital technologies, such as AI and deep learning, impacts researchers, clinicians, and industry due to promising performance and clinica...

Generating and evaluating synthetic data in digital pathology through diffusion models.

Scientific reports
Synthetic data is becoming a valuable tool for computational pathologists, aiding in tasks like data augmentation and addressing data scarcity and privacy. However, its use necessitates careful planning and evaluation to prevent the creation of clini...