Cancer treatment is growing more complex, but so too are the possibilities. After all, the better a tumour’s biology and genetic features are understood, the more treatment approaches there are. To be able to offer patients personalised therapies tailored to their disease, laborious and time-consuming analysis and interpretation of various data is required. In one of many artificial intelligence (AI)projects at Charité – Universitätsmedizin Berlin and Humboldt-Universität zu Berlin, researchers studied whether generative AI tools such as ChatGPT can help with this step.
The crucial factor in the phenomenon of tumour growth is an imbalance of growth-inducing and growth-inhibiting factors, which can result, for example, from changes in oncogenes.
Precision oncology, a specialised field of personalised medicine, leverages this knowledge by using specific treatments such as low-molecular weight inhibitors and antibodies to target and disable hyperactive oncogenes.
The first step in identifying which genetic mutations are potential targets for treatment is to analyse the genetic makeup of the tumour tissue. The molecular variants of the tumour DNA that are necessary for precision diagnosis and treatment are determined. Then the doctors use this information to craft individual treatment recommendations. In especially complex cases, this requires knowledge from various fields of medicine.
At Charité, this is when the “molecular tumour board” (MTB) meets: Experts from the fields of pathology, molecular pathology, oncology, human genetics, and bioinformatics work together to analyse which treatments seem most promising based on the latest studies.
It is a very involved process, ultimately culminating in a personalised treatment recommendation.
Can artificial intelligence help with treatment decisions?
Dr Damian Rieke, a doctor at Charité, and his colleagues wondered whether AI might be able to help at this juncture.
In a study just recently published in the journal JAMA Network Open, they worked with other researchers to examine the possibilities and limitations of large language models such as ChatGPT in automatically scanning scientific literature with an eye to selecting personalised treatments.
AI ‘not even close’
“We prompted the models to identify personalised treatment options for fictitious cancer patients and then compared the results with the recommendations made by experts,” Rieke explains.
His conclusion: “AI models were able to identify personalised treatment options in principle – but they weren’t even close to the abilities of human experts.”
The team created ten molecular tumour profiles of fictitious patients for the experiment.
A human physician specialist and four large language models were then tasked with identifying a personalised treatment option.
These results were presented to the members of the MTB for assessment, without them knowing where which recommendation came from.
Improved AI models hold promise for future uses
Dr. Manuela Benary, a bioinformatics specialist reported: “There were some surprisingly good treatment options identified by AI in isolated cases. “But large language models perform much worse than human experts.”
Beyond that, data protection, privacy, and reproducibility pose particular challenges in relation to the use of artificial intelligence with real-world patients, she notes.
Still, Rieke is fundamentally optimistic about the potential uses of AI in medicine: “In the study, we also showed that the performance of AI models is continuing to improve as the models advance. This could mean that AI can provide more support for even complex diagnostic and treatment processes in the future – as long as humans are the ones to check the results generated by AI and have the final say about treatment.”
