Opportunities and Challenges with Artificial Intelligence in Genomics.

The development of artificial intelligence and machine learning algorithms may allow for advances in patient care. There are existing and potential applications in cancer diagnosis and monitoring, identification of at-risk groups of individuals, classification of genetic variants, and even prediction of patient ancestry. This article provides an overview of some current and future applications of artificial intelligence in genomic medicine, in addition to discussing challenges and considerations when bringing these tools into clinical practice.

Shell-Free Copper Indium Sulfide Quantum Dots Induce Toxicity in Vitro and in Vivo.

Semiconductor quantum dots (QDs) are attractive fluorescent contrast agents for in vivo imaging due to their superior photophysical properties, but traditional QDs comprise toxic materials such as cadmium or lead. Copper indium sulfide (CuInS2, CIS) QDs have been posited as a nontoxic and potentially clinically translatable alternative; however, previous in vivo studies utilized particles with a passivating zinc sulfide (ZnS) shell, limiting direct evidence of the biocompatibility of the underlying CIS. For the first time, we assess the biodistribution and toxicity of unshelled CIS and partially zinc-alloyed CISZ QDs in a murine model. We show that bare CIS QDs breakdown quickly, inducing significant toxicity as seen in organ weight, blood chemistry, and histology. CISZ demonstrates significant, but lower, toxicity compared to bare CIS, while our measurements of core/shell CIS/ZnS are consistent with literature reports of general biocompatibility. In vitro cytotoxicity is dose-dependent on the amount of metal released due to particle degradation, linking degradation to toxicity. These results challenge the assumption that removing heavy metals necessarily reduces toxicity: indeed, we find comparable in vitro cytotoxicity between CIS and CdSe QDs, while CIS caused severe toxicity in vivo compared to CdSe. In addition to highlighting the complexity of nanotoxicity and the differences between the in vitro and in vivo outcomes, these unexpected results serve as a reminder of the importance of assessing the biocompatibility of core QDs absent the protective ZnS shell when making specific claims of compositional biocompatibility.

Machine Learning and Other Emerging Decision Support Tools.

Emerging applications of machine learning and artificial intelligence offer the opportunity to discover new clinical knowledge through secondary exploration of existing patient medical records. This new knowledge may in turn offer a foundation to build new types of clinical decision support (CDS) that provide patient-specific insights and guidance across a wide range of clinical questions and settings. This article will provide an overview of these emerging approaches to CDS, discussing both existing technologies as well as challenges that health systems and informaticists will need to address to allow these emerging approaches to reach their full potential.

Creation and Use of an Electronic Health Record Reporting Database to Improve a Laboratory Test Utilization Program.

OBJECTIVES: Laboratory-based utilization management programs typically rely primarily on data derived from the laboratory information system to analyze testing volumes for trends and utilization concerns. We wished to examine the ability of an electronic health record (EHR) laboratory orders database to improve a laboratory utilization program. METHODS: We obtained a daily file from our EHR containing data related to laboratory test ordering. We then used an automated process to import this file into a database to facilitate self-service queries and analysis. RESULTS: The EHR laboratory orders database has proven to be an important addition to our utilization management program. We provide three representative examples of how the EHR laboratory orders database has been used to address common utilization issues. We demonstrate that analysis of EHR laboratory orders data has been able to provide unique insights that cannot be obtained by review of laboratory information system data alone. Further, we provide recommendations on key EHR data fields of importance to laboratory utilization efforts. CONCLUSION: We demonstrate that an EHR laboratory orders database may be a useful tool in the monitoring and optimization of laboratory testing. We recommend that health care systems develop and maintain a database of EHR laboratory orders data and integrate this data with their laboratory utilization programs.