Then the AI algorithm integrates this information with other information, such as imaging information and general lab test information, and puts it all together to make a more comprehensive prediction about the condition of the patient.
Professor Lequan Yu is the director of the Medical AI Lab at HKU and his work lies at the intersection of AI and healthcare. Before joining HKU, Professor Yu was a postdoctoral research fellow at Stanford University. In this interview he explains to our science editor, Dr Pavel Toropov, how AI can revolutionise healthcare.
❓ Dr Pavel Toropov: How do you use artificial intelligence in your research?
💬 Professor Lequan Yu: We use AI technology, AI algorithms to solve problems related to healthcare and medicine. We rely on multimodal AI models, using AI to analyse and integrate different medical data, such as medical images, medical reports, lab test results and genomic data. The aim is to interpret and integrate them together to help doctors make decisions.
❓ Could you provide an example?
💬 For example, using an algorithm to see if the patient has cancer or not from computed tomography (CT) images. This can reduce the doctor’s workload. Also, we want to do precision medicine, especially for cancer patients. Currently, treatment strategies are not really tailored for individual patients. We want to use AI algorithms to integrate the diverse information about each individual patient and then let AI make recommendations for doctors.
❓ What data would AI integrate?
💬 Radiology data such as CT scans, MRI and also pathology images – microscopic images. Recently, we have been exploring how to integrate genomic data.
❓ What do you mean by “genomic data”？
💬 Broadly speaking, this refers to DNA, RNA or protein data. For example, we work on gastric cancer. We get samples of cancerous tissue, and do genetic sequencing or molecular testing to obtain molecular information, for example, about what subtype of cancer it is.
Then the AI algorithm integrates this information with other information, such as imaging information and general lab test information, and puts it all together to make a more comprehensive prediction about the condition of the patient.
For cancer, take gastric cancer for example, there are different treatment strategies, for example immunotherapy. But, we do not know whether this strategy and treatment would benefit this particular patient. Because some strategies may not. So our AI algorithm can predict treatment response in this particular patient and also provide survival analysis.
❓ In healthcare, what does using AI allow humans to do that they cannot do alone?
💬 Two examples. One is chest X-rays. A doctor can do the analysis very well, detecting pneumonia, for example. AI can also do it, and the reason to use AI is to help reduce the doctors’ effort and workload.
But for cancer image analysis, that is different. Doctors can estimate potential survival or potential treatment response from the image. But this is quite subjective, based on the doctor’s experience. AI has the potential to evaluate this more quantitatively and objectively.
❓ Can this technology be used in clinical practice now?
💬 Currently for oncology this is frontier research. There is still a way to go before putting it in clinical practice. But, after we incorporate genomic data, we think it will be more workable. Perhaps in to 10 years this will be applied in clinical practice.
❓ Other than cancer, in the treatment of what other conditions can AI help?
💬 Cardiovascular disease. Here AI can play an important role. When it comes to certain heart risk disease predictions, it is less challenging than with cancer. Moreover, AI can integrate and analyse chest X-ray images, and here the accuracy of AI is very high, over 90%.
But still we have issues regarding privacy, ethics and medical regulations before we can apply it in clinical practice.
Collaboration is very important – we must collaborate with doctors, hospitals, medical schools. It is the best way to apply AI technology to solve real-world problems, address real-world needs, and help our society, medicine and economy.
👏 Thank you, Professor Yu.