Application of a Comprehensive Evaluation Framework to COVID-19 Studies: Systematic Review of Translational Aspects of Artificial Intelligence in Health Care

Background

Despite immense progress in artificial intelligence (AI) models, there has been limited deployment in health care environments. The gap between potential and actual AI applications is likely due to the lack of translatability between controlled research environments (where these models are developed) and clinical environments for which the AI tools are ultimately intended.

Objective

We previously developed the Translational Evaluation of Healthcare AI (TEHAI) framework to assess the translational value of AI models and to support successful transition to health care environments. In this study, we applied the TEHAI framework to the COVID-19 literature in order to assess how well translational topics are covered.

Methods

A systematic literature search for COVID-19 AI studies published between December 2019 and December 2020 resulted in 3830 records. A subset of 102 (2.7%) papers that passed the inclusion criteria was sampled for full review. The papers were assessed for translational value and descriptive data collected by 9 reviewers (each study was assessed by 2 reviewers). Evaluation scores and extracted data were compared by a third reviewer for resolution of discrepancies. The review process was conducted on the Covidence software platform.

Results

We observed a significant trend for studies to attain high scores for technical capability but low scores for the areas essential for clinical translatability. Specific questions regarding external model validation, safety, nonmaleficence, and service adoption received failed scores in most studies.

Conclusions

Using TEHAI, we identified notable gaps in how well translational topics of AI models are covered in the COVID-19 clinical sphere. These gaps in areas crucial for clinical translatability could, and should, be considered already at the model development stage to increase translatability into real COVID-19 health care environments.

Evaluation framework to guide implementation of AI systems into healthcare settings

Objectives

To date, many artificial intelligence (AI) systems have been developed in healthcare, but adoption has been limited. This may be due to inappropriate or incomplete evaluation and a lack of internationally recognised AI standards on evaluation. To have confidence in the generalisability of AI systems in healthcare and to enable their integration into workflows, there is a need for a practical yet comprehensive instrument to assess the translational aspects of the available AI systems. Currently available evaluation frameworks for AI in healthcare focus on the reporting and regulatory aspects but have little guidance regarding assessment of the translational aspects of the AI systems like the functional, utility and ethical components.

Methods

To address this gap and create a framework that assesses real-world systems, an international team has developed a translationally focused evaluation framework termed ‘Translational Evaluation of Healthcare AI (TEHAI)’. A critical review of literature assessed existing evaluation and reporting frameworks and gaps. Next, using health technology evaluation and translational principles, reporting components were identified for consideration. These were independently reviewed for consensus inclusion in a final framework by an international panel of eight expert.

Results

TEHAI includes three main components: capability, utility and adoption. The emphasis on translational and ethical features of the model development and deployment distinguishes TEHAI from other evaluation instruments. In specific, the evaluation components can be applied at any stage of the development and deployment of the AI system.

Discussion

One major limitation of existing reporting or evaluation frameworks is their narrow focus. TEHAI, because of its strong foundation in translation research models and an emphasis on safety, translational value and generalisability, not only has a theoretical basis but also practical application to assessing real-world systems.

Conclusion

The translational research theoretic approach used to develop TEHAI should see it having application not just for evaluation of clinical AI in research settings, but more broadly to guide evaluation of working clinical systems.

Use and validation of text mining and cluster algorithms to derive insights from Corona Virus Disease-2019 (COVID-19) medical literature

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) late last year has not only led to the world-wide coronavirus disease 2019 (COVID-19) pandemic but also a deluge of biomedical literature. Following the release of the COVID-19 open research dataset (CORD-19) comprising over 200,000 scholarly articles, we a multi-disciplinary team of data scientists, clinicians, medical researchers and software engineers developed an innovative natural language processing (NLP) platform that combines an advanced search engine with a biomedical named entity recognition extraction package. In particular, the platform was developed to extract information relating to clinical risk factors for COVID-19 by presenting the results in a cluster format to support knowledge discovery. Here we describe the principles behind the development, the model and the results we obtained.

Use and validation of text mining and cluster algorithms to derive insights from Corona Virus Disease-2019 (COVID-19) medical literature

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) late last year has not only led to the world-wide coronavirus disease 2019 (COVID-19) pandemic but also a deluge of biomedical literature. Following the release of the COVID-19 open research dataset (CORD-19) comprising over 200,000 scholarly articles, we a multi-disciplinary team of data scientists, clinicians, medical researchers and software engineers developed an innovative natural language processing (NLP) platform that combines an advanced search engine with a biomedical named entity recognition extraction package. In particular, the platform was developed to extract information relating to clinical risk factors for COVID-19 by presenting the results in a cluster format to support knowledge discovery. Here we describe the principles behind the development, the model and the results we obtained.