Transforming multiple sclerosis care using AI: prospects and challenges

20 in Neurology

The effect size, expressed as mean difference in annual percentage cortical thickness change (MD-APC), is graphically displayed in different shades of blue for each of the Desikan-Killiany atlas25 regions presenting significant differences between groups after correction for multiple comparisons.

The effect size, expressed as mean difference in annual percentage cortical thickness change (MD-APC), is graphically displayed in different shades of blue for each of the Desikan-Killiany atlas25 regions presenting significant differences between groups after correction for multiple comparisons.

The effect size, expressed as mean difference in annual percentage cortical thickness change (MD-APC), is graphically displayed in different shades of blue for each of the Desikan-Killiany atlas25 regions presenting significant differences between groups after correction for multiple comparisons.

The paper is a scoping review evaluating the current landscape of randomized controlled trials (RCTs) for artificial intelligence (AI) algorithms used in clinical practice. The authors conducted a systematic search for RCTs published between January 2018 - August 2023, resulting in 84 unique trials included in the analysis. The key findings were:

• Most trials were in gastroenterology (41.7%), radiology (15.5%), surgery (6%), and cardiology (6%).
• The US (31%) and China (28.6%) had the most trials, with most being single-center studies.
• 54.8% of trials had primary endpoints related to diagnostic accuracy. Other endpoints assessed care management, patient outcomes, and clinical decision making.
• 82.1% of trials reported positive results for their primary endpoint, suggesting AI’s potential to improve healthcare. However, publication bias is likely.
• Most models were deep learning systems for medical imaging, particularly video analysis. Models for structured data used various techniques like neural networks.
• Industry developed most models (56%), followed by academia (39.3%). Models were commonly evaluated in an assistive setup with clinicians.

Page 2: Key implications and limitations:

• Need for more multi-center and international trials to ensure generalizability of models across diverse populations and settings.
• Focus has been on technical performance, but impact on patient outcomes needs more assessment through endpoints like survival, symptoms, need for treatment.
• High reported success rate indicates promise of AI but is likely inflated by publication bias and lack of comprehensive, multi-center studies.
• Homogeneity of studies/models indicates immaturity of the field. More variety needed in research groups, interventions, outcomes assessed.
• Understanding of AI’s limitations requires focus on unsuccessful trials and models.

In conclusion, the review reveals expanding interest in clinical AI trials across many fields and geographies. While most find positive results, publication bias is likely. More comprehensive research is essential to fully understand AI’s impact and limitations in healthcare. Key gaps include multi-center trials, clinically meaningful endpoints, and unsuccessful models.