Recent developments in AI have provided assisting tools to support pathologists’ diagnoses. However, it remains challenging to incorporate such tools into pathologists’ practice; one main concern is AI’s insufficient workflow integration with medical decisions. We observed pathologists’ examination and discovered that the main hindering factor to integrate AI is its incompatibility with pathologists’ workflow. To bridge the gap between pathologists and AI, we developed a human-AI collaborative diagnosis tool— xPath —that shares a similar examination process to that of pathologists, which can improve AI’s integration into their routine examination. The viability of xPath is confirmed by a technical evaluation and work sessions with 12 medical professionals in pathology. This work identifies and addresses the challenge of incorporating AI models into pathology, which can offer first-hand knowledge about how HCI researchers can work with medical professionals side-by-side to bring technological advances to medical tasks towards practical applications.

Artificial Intelligence (AI) brings advancements to support pathologists in navigating high-resolution tumor images to search for pathology patterns of interest. However, existing AI-assisted tools have not realized this promised potential due to a lack of insight into pathology and HCI considerations for pathologists’ navigation workflows in practice. We first conducted a formative study with six medical professionals in pathology to capture their navigation strategies. By incorporating our observations along with the pathologists’ domain knowledge, we designed NaviPath — a human-AI collaborative navigation system. An evaluation study with 15 medical professionals in pathology indicated that: (i) compared to the manual navigation, participants saw more than twice the number of pathological patterns in unit time with NaviPath, and (ii) participants achieved higher precision and recall against the AI and the manual navigation on average. Further qualitative analysis revealed that navigation was more consistent with NaviPath, which can improve the overall examination quality.

Neurotransmitters (NTs) mediate trans-synaptic signaling, and disturbances in their levels are linked to aging and brain disorders. Here, we ascribe an additional function for NTs in mediating intracellular protein aggregation by interaction with cytosolic protein fibrils. Cell-based seeding experiments revealed monoaminergic NTs as inhibitors of tau. Seeding is a disease-relevant mechanism involving catalysis by fibrils, leading to the aggregation of proteins in Alzheimer’s disease and other neurodegenerative diseases. Chemotyping small molecules with varied backbone structures revealed determinants of aggregation inhibitors and catalysts. Among those identified were monoaminergic NTs. Dose titrations revealed bimodal effects indicative of fibril disaggregation, with aggregation catalysis occurring at low ratios of NTs and inhibited seeding ensuing at higher concentrations. Bimodal effects by NTs extend from in vitro systems to dopaminergic neurons, suggesting that pharmacotherapies that modify intracellular NT levels could shape the neuronal protein aggregation environment.

Recent developments in AI have provided assisting tools to support pathologists' diagnoses. However, it remains challenging to incorporate such tools into pathologists' practice; one main concern is AI's insufficient workflow integration with medical decisions. We observed pathologists' examination and discovered that the main hindering factor to integrate AI is its incompatibility with pathologists' workflow. To bridge the gap between pathologists and AI, we developed a human-AI collaborative diagnosis tool -- xPath -- that shares a similar examination process to that of pathologists, which can improve AI's integration into their routine examination. The viability of xPath is confirmed by a technical evaluation and work sessions with twelve medical professionals in pathology. This work identifies and addresses the challenge of incorporating AI models into pathology, which can offer first-hand knowledge about how HCI researchers can work with medical professionals side-by-side to bring technological advances to medical tasks towards practical applications.