Artificial intelligence-enabled healthcare delivery.

In recent years, there has been massive progress in artificial intelligence (AI) with the development of deep neural networks, natural language processing, computer vision and robotics. These techniques are now actively being applied in healthcare with many of the health service activities currently being delivered by clinicians and administrators predicted to be taken over by AI in the coming years. However, there has also been exceptional hype about the abilities of AI with a mistaken notion that AI will replace human clinicians altogether. These perspectives are inaccurate, and if a balanced perspective of the limitations and promise of AI is taken, one can gauge which parts of the health system AI can be integrated to make a meaningful impact. The four main areas where AI would have the most influence would be: patient administration, clinical decision support, patient monitoring and healthcare interventions. This health system where AI plays a central role could be termed an AI-enabled or AI-augmented health system. In this article, we discuss how this system can be developed based on a realistic assessment of current AI technologies and predicted developments.

Integrated health care: it's time for it to blossom.

Considering the grim scenario of burgeoning health-care costs and cost-cutting measures by the Australian Government, there is a clear case to invest and research into disciplines that will ensure sustainability of the public health system. There is evidence that integrated health care contributes to a cost-efficient and quality health system because of potential benefits like streamlined care for patients, efficient use of resources, a better cover of patients and improved patient safety. However, integrated health care as a notion is submerged in the disciplines of public health and primary care. In reality, it is a distinct concept acting as a bridge between primary and secondary care. This article argues it is time for the discipline of integrated health care to be recognised on its own and investment be driven into the establishment of integrated care centres.

Comprehensive multiplatform biomarker analysis of 350 hepatocellular carcinomas identifies potential novel therapeutic options.

BACKGROUND AND OBJECTIVES: Effective therapies for hepatocellular carcinoma (HCC) are limited. Molecular profiling of HCC was performed to identify novel therapeutic targets. METHODS: 350 HCC samples were evaluated using a multiplatform profiling service (Caris Life Sciences, Phoenix, AZ), including gene sequencing, amplification, and protein expression. RESULTS: EGFR, TOPO1, PD-1, TOP2A, SPARC, and c-Met were overexpressed in 25-83% of samples. Decreased expression of RRM1,TS, PTEN, and MGMT occurred in 31-82% of samples. TP53 was mutated in 30%, CTNNB1 in 20%, and BRCA2 in 18%; other gene mutation rates were <5%. TP53-mutated tumors showed significantly higher TOPO2A (90% vs. 38%, P < 0.0001) and TS (56% vs. 29%, P = 0.0139) expression. CTNNB1-mutated tumors had significantly higher AR (56% vs. 21%, P = 0.0017), SPARC (61% vs. 29%, P = 0.0135), PDL1 (29% vs. 0%, P = 0.0256) expression, and BRCA2 mutations (50% vs. 6%, P = 0.0458). Metastases exhibited significantly higher infiltration by PD-1+ lymphocytes (79% vs. 50%, P = 0.047) and TS (31% vs. 14%, P < 0.0003) than primary HCC. CONCLUSIONS: Multiplatform profiling reveals molecular heterogeneity in HCC and identifies potential therapies including tyrosine kinase, PI3 kinase, or PARP inhibitors for molecular subtypes. Chemotherapy may benefit some tumors. CTNNB1-mutated tumors may respond to multi-target inhibition. These limited and preliminary data require clinical validation.