Table 1 An overview of the challenges and roadblocks encountered during various steps of using artificial intelligence (AI) tools in pathology workflow
Process involved in integration of AI tools in pathology | Challenges and roadblocks |
|---|---|
Identification of needs | • Incorrection assessment of end-user and demands • Small market size of AI usage • Lack of awareness of possibilities of use |
Collaborative inter-disciplinary efforts | • Lack of coordination between different players • Discordance in goals of participants |
Study concept, design | • Scientific background/rationale • Funding • Ethical approval |
Development of algorithmic models | • Pre-analytical and analytical factors • Lack of objective ground truth |
Optimization, validation, and standardization | • Lack of appropriate validation dataset • Overfitting |
Interpretability | • Lack of interpretability and generalizability • Black-box issue |
Data curation | • Difficulty in obtaining well-curated, annotated data |
Regulation/approval | • Lack of clear-cut regulatory guidelines |
Installation | • Pathologists’ resistance to changes in old workflow • IT infrastructure investment and overhead costs |
Accreditation | • No external quality assurance scheme • Unestablished audit cycles |
Reimbursement | • Lack of dedicated procedure codes |
Clinical adoption | • Lack of FDA approval for use of AI • Skepticism among pathologists and oncologists |
Computation system and data storage | • Need for powerful, high specification hardware • Cost-benefit ratio considerations |