In recent years, access to patient medical information, coupled with rapid advancements in data analytics tools and technologies, has significantly altered many areas of healthcare, from early-stage discovery and research to patient treatment. The healthcare AI market is expected to grow from US$2 billion in 2018 to US$36 billion by 2025.
The breadth of AI’s application in healthcare is impressive, ranging from diagnostic chat bots to AI robot-assisted surgery. Other examples include AI enhanced microscopes that can more efficiently scan for harmful bacteria in blood samples; efficient and enhanced scanning for abnormalities in radiographic images; and AI algorithm analysis of tone, language and facial expressions to detect mental illness.
Baricitinib is an oral Janus kinase (JAK)1/JAK2 inhibitor approved for the treatment of rheumatoid arthritis (RA) that was independently predicted, using artificial intelligence (AI) algorithms, to be useful for COVID-19 infection via proposed anti-cytokine effects and as an inhibitor of host cell viral propagation. Baricitinib has thereafter been linked to no less than 71% reduction in COVID-19 mortality.
AI in Clinical Research
The lengthy tried and tested process of discrete and fi xed phases of randomised controlled trials (RCTs) was designed principally for testing mass-market drugs and has changed little in recent decades and appears stuck in the 20th century.
Artificial intelligence (AI) can reduce clinical trial cycle times while improving the costs of productivity and outcomes of clinical development.
The CONSORT 2010 (Consolidated Standards of Reporting Trials) Statement and SPIRIT 2013 (Standard Protocol Items: Recommendations for Interventional Trials) Statement extented their existing guidelines for clinical trials with AI recommendations: 14 new checklist items should be added to the CONSORT 2010 Statement and 15 new checklist items should be added to the SPIRIT 2013 Statement.
The new checklists recommend that AI researchers to the following:
- explain the intended use for the AI intervention in the context of the clinical pathway, including its purpose and its intended users (such as healthcare professionals, patients, public);
- state which version of the AI algorithm was used;
- describe how the input data was acquired and selected for the AI intervention and how poor quality or unavailable input data was assessed and handled;
- specify whether there was human-AI interaction in the handling of the input data, and what level of expertise was required of users;
- specify the output of the AI intervention;
- explain how the AI intervention’s outputs contributed to decision-making or other elements of clinical practice; and
- describe results of any analysis of performance errors and how errors were identified.
Deloitte has issued a report, Intelligent clinical trials: Transforming through AI-enabled engagement, issued in February 2020, just before the COVID-19 pandemic. The report mentioned that ‘future’ biopharma companies will capitalise on the digitalisation of health care to manage clinical trials remotely. The Future has arrived to clinical resarch and is knocking fiercely on the front door.