Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine

Artificial intelligence (AI) applied to medicine offers immense promise, in addition to safety and regulatory concerns. Traditional AI produces a core algorithm result, typically without a measure of statistical confidence or an explanation of its biological-theoretical basis. Efforts are underway to develop explainable AI (XAI) algorithms that not only produce a result but also an explanation to support that result. Here we present a framework for classifying XAI algorithms applied to clinical medicine: An algorithm’s clinical scope is defined by whether the core algorithm output leads to observations (eg, tests, imaging, clinical evaluation), interventions (eg, procedures, medications), diagnoses, and prognostication. Explanations are classified by whether they provide empiric statistical information, association with a historical population or populations, or association with an established disease mechanism or mechanisms. XAI implementations can be classified based on whether algorithm training and validation took into account the actions of health care providers in response to the insights and explanations provided or whether training was performed using only the core algorithm output as the end point. Finally, communication modalities used to convey an XAI explanation can be used to classify algorithms and may affect clinical outcomes. This framework can be used when designing, evaluating, and comparing XAI algorithms applied to medicine

A New Era in Pathology Consultation

Pathologists and laboratory scientists provide valuable guidance on laboratory utilization, test ordering, interpretation, and quality control provided that clinical staff can easily access the laboratory team. To encourage consultation between clinicians with laboratory scientists and pathologists, we developed an easily accessible electronic tool termed “MyPathologist,” placed on the homepage of our electronic health record system. Over its 2-year pilot, utilization of this consultation tool climbed as we continued to publicize it and incorporated education into housestaff onboarding and electronic health record training. Physician satisfaction with the tool was high. Additionally, this became the primary source of consults to our residency call service. Evaluation of MyPathologist questions received during its pilot period showed that more than half the questions were of significant educational value to the residents, often focusing on results interpretation, appropriate test ordering, and quality control. MyPathologist is a novel electronic tool for pathology consultation within our electronic health record and also represents an avenue for educating residents, improving utilization of the laboratory, and improving patient care