Collective Intelligence can Help Reduce Medical Misdiagnoses

An estimated 250,000 people die from preventable medical errors in the U.S. each year. Many of these errors originate during the diagnostic process. A powerful way to increase diagnostic accuracy is to combine the diagnoses of multiple diagnosticians into a collective solution. However, there has been a dearth of methods for aggregating independent diagnoses in general medical diagnostics. Researchers from the Max Planck Institute for Human Development, the Institute for Cognitive Sciences and Technologies (ISTC), and the Norwegian University of Science and Technology have therefore introduced a fully automated solution using knowledge engineering methods.

The researchers tested their solution on 1,333 medical cases provided by The Human Diagnosis Project (Human Dx), each of which was independently diagnosed by 10 diagnosticians. The collective solution substantially increased diagnostic accuracy: Single diagnosticians achieved 46% accuracy, whereas pooling the decisions of 10 diagnosticians increased accuracy to 76%. Improvements occurred across medical specialties, chief complaints, and diagnosticians’ tenure levels. "Our results show the life-saving potential of tapping into the collective intelligence," says first author Ralf Kurvers. He is a senior research scientist at the Center for Adaptive Rationality of the Max Planck Institute for Human Development and his research focuses on social and collective decision making in humans and animals.

Collective intelligence has been proven to boost decision accuracy across many domains, such as geopolitical forecasting, investment, and diagnostics in radiology and dermatology (e.g., Kurvers et al., PNAS, 2016). However, collective intelligence has been mostly applied to relatively simple decision tasks. Applications in more open-ended tasks, such as emergency management or general medical diagnostics, are largely lacking due to the challenge of integrating unstandardized inputs from different people. To overcome this hurdle, the researchers used semantic knowledge graphs, natural language processing, and the SNOMED CT medical ontology, a comprehensive multilingual clinical terminology, for standardization.

"A key contribution of our work is that, while the human-provided diagnoses maintain their primacy, our aggregation and evaluation procedures are fully automated, avoiding possible biases in the generation of the final diagnosis and allowing the process to be more time- and cost-efficient," adds co-author Vito Trianni from the Institute for Cognitive Sciences and Technologies (ISTC) in Rome.

The researchers are currently collaborating - along with other partners - within the HACID project to bring their application one step closer to the market. The EU-funded project will explore a new approach that brings together human experts and AI-supported knowledge representation and reasoning in order to create new tools for decision making in various domains. The application of the HACID technology to medical diagnostics showcases one of the many opportunities to benefit from a digitally based health system and accessible data.

Kurvers RHJM, Nuzzolese AG, Russo A, Barabucci G, Herzog SM, Trianni V.
Automating hybrid collective intelligence in open-ended medical diagnostics.
Proceedings of the National Academy of Sciences of the United States of America, 120(34), 2023. doi: 10.1073/pnas.2221473120

Most Popular Now

AI Distinguishes Glioblastoma from Look-…

A Harvard Medical School–led research team has developed an AI tool that can reliably tell apart two look-alike cancers found in the brain but with different origins, behaviors, and treatments. The...

AI Body Composition Measurements can Pre…

Adiposity - or the accumulation of excess fat in the body - is a known driver of cardiometabolic diseases such as heart disease, stroke, type 2 diabetes, and kidney disease...

AI can Strengthen Pandemic Preparedness

How to identify the next dangerous virus before it spreads among people is the central question in a new Comment in The Lancet Infectious Diseases. In it, researchers discuss how...

'Future-Guided' AI Improves Se…

In the world around us, many things exist in the context of time: a bird’s path through the sky is understood as different positions over a period of time, and...

New AI Tool Scans Social Media for Hidde…

A new artificial intelligence tool can scan social media data to discover adverse events associated with consumer health products, according to a study published September 30th in the open-access journal...

Study Finds One-Year Change on CT Scans …

Researchers at National Jewish Health have shown that subtle increases in lung scarring, detected by an artificial intelligence-based tool on CT scans taken one year apart, are associated with disease...

New AI Tools Help Scientists Track How D…

Artificial intelligence (AI) can solve problems at remarkable speed, but it’s the people developing the algorithms who are truly driving discovery. At The University of Texas at Arlington, data scientists...

Yousif's Story with Sectra and The …

Embarking on healthcare technology career after leaving his home as a refugee during his teenage years, Yousif is passionate about making a difference. He reflects on an apprenticeship in which...

AI Tool Offers Deep Insight into the Imm…

Researchers explore the human immune system by looking at the active components, namely the various genes and cells involved. But there is a broad range of these, and observations necessarily...

New Antibiotic Targets IBD - and AI Pred…

Researchers at McMaster University and the Massachusetts Institute of Technology (MIT) have made two scientific breakthroughs at once: they not only discovered a brand-new antibiotic that targets inflammatory bowel diseases...

Highland to Help Companies Seize 'N…

Health tech growth partner Highland has today revealed its new identity - reflecting a sharper focus as it helps health tech companies to find market opportunities, convince target audiences, and...