“In the face of the new AI technology that is developing by leaps and bounds, as a neurosurgeon, I’m not worried about losing my job, but more concerned about how to embrace AI to better serve patients, and to allow us doctors to get off work earlier. For example, AI-assisted clinical interrogations, brain image and EEG data analysis, and surgical scheme formulation; or examining medical issues from a completely new perspective of AI, overcoming the subjective shortcomings of studying human brain, and tackling brain diseases as soon as possible,” said Professor Mao Ying, President of Huashan Hospital and Director of the Translational Center of TCCI, in his opening speech at a seminar on AI-assisted solutions to brain diseases jointly hosted by the Tianqiao and Chrissy Chen Institute (TCCI), Huashan Hospital (National Medical Center for Neurological Diseases) and Shanghai Mental Health Center (National Medical Center for Mental Diseases) on April 9.

 

At the meeting, experts from the AI and clinical fields had active exchanges. Professor Xu Yifeng, Director of the Brain Health Institute at National Center for Mental Disorder, Professor Wang Zhen, Vice President of the Shanghai Mental Health Center, and Professor Li Weidong, Executive Director of the Institute of Psychology and Behavioral Science at Shanghai Jiao Tong University, attended the meeting.

 

AI + wearable devices will help precision medicine solve the last-mile problem

 

AI scientist Dr. Hu Pengwei pointed out that the application scenarios of AI in the medical field are extremely wide, and currently it mainly serves three purposes: reducing the burden of repetitive work, identifying traces and clues that are difficult to detect by human beings, and conducting clue analysis in complex environments. AI also has great potential in precision medicine. GPT’s summarizing and inductive ability has shown great strength in early examination and diagnosis, out-of-hospital emotional support and assistance, big data analytics and pattern identification. He predicts that with the combined innovation of AI technology and wearable smart devices, precision medicine is expected to solve the last-mile problem in the next three to five years.

 

Deciphering invasive EEG to achieve infinite possibilities

 

Professor Chen Liang, Deputy Director of Neurosurgery and Leader of Functional Neurosurgery at Fudan University-affiliated Huashan Hospital, focused on the construction of invasive EEG databases and the prospects of augmented AI technology in deciphering brain functions. Invasive EEG refers to the implantation of electrodes into the brain or placing them on the surface of the brain to obtain EEG data with high signal-to-noise ratio. This type of data is of critical value in the fields of neuroscience and neurosurgery, as neuronal firing is the most basic form of nerve cell activities. By implanting high-density surface or deep electrodes, researchers expect to collect more data on EEG activities. In the case of Parkinson’s disease patients, for example, clinicians urgently need to find the best treatment options through a large number of intracranial stimulation experiments, which is burdensome for patients. He hopes that augmented AI technology will complete the time-consuming and repetitive work previously done by human beings to address unanswered scientific questions, including epilepsy traceability warning and consciousness transformation.

 

A prospective key role in treating AD

 

Professor Yu Jintai, Deputy Director of the Department of Neurology at Fudan University-affiliated Huashan Hospital and Leader of cognitive disorders research at the National Medical Center for Neurological Diseases, pointed out that to achieve early diagnosis of AD, it is necessary to set up large cohorts, especially community-based cohorts, in order to identify patients in the preclinical stage. The GPT model holds great potential in disease management which will improve medical automation such as the construction of a disease management platform to achieve individualized patient condition assessment, automated analysis reporting, intelligent follow-up Q&A, and other functions. He also mentioned that GPT faces many challenges in the field of AD diagnosis and research, such as the lack of high-quality medical data, data security issues, and the reliance of effective answers on the training data. However, he also believes that AI is expected to play a key role in the field of AD by continuously deepening research and practice.

 

Contributive to dream decoding

 

Professor Yu Huan, Executive Director of the Sleep Disorders Clinic at Fudan University-affiliated Huashan Hospital, emphasized the impact of sleep disorders on quality of life, such as cerebrovascular accidents and dementia. Currently, polysomnography (PSG) is the standard technique for sleep disorder diagnosis, but it is costly and inefficient. Therefore, researchers are looking forward to improving diagnostic methods through artificial intelligence technology. Professor Yu Huan introduced the application of dream research in the field of sleep disorders, such as improving memory by controlling dreams. There are more than 150 methods for encoding and calculating dreams, and the researchers hope to design a more practical research tool with the help of AI technology; at the same time, mobile apps are developed to encourage individuals to record and share their dreams, so as to carry out dream research more relevant to daily life.

 

Robot-assisted consultation and diagnosis for depression patients

 

According to Associate Professor Wu Mengyue of the Department of Computer Science and Engineering at Shanghai Jiao Tong University, the development of a robot based on human-computer conversations for clinical consultations on depression, as well as the use of speech and language features to build a knowledge graph of relevant symptoms and mental illnesses, is the future direction of early diagnosis and treatment of depression.

 

According to her, the diagnosis of many mental illnesses relies heavily on face-to-face clinical interviews and conversations. Theoretically, AI models should be able to learn this skill as well. Meanwhile, speech and language are already used as objective biomarkers in the DSM-5 diagnostic manual to diagnose mental illnesses including depression. The development of a robot based on human-computer conversations for depression diagnosis would allow such conversations to be as accurate as the description of symptoms obtained by doctors through in-depth communications with patients. She also introduces how to treat speech and language features as computable and transferable methods, as well as the creation of knowledge graphs of relevant symptoms and diseases through the self-expressions of patients, which will provide new methodologies for detecting a wide range of diseases.

 

New explorations of early diagnosis and treatment of depression

 

Professor Peng Daihui, Director of the Department of Mood Disorders of Shanghai Mental Health Center, introduced that the major research project he is leading — “Prospective Clinical Cohort Study of Depression”, which aims to collect nationwide data from patients with depression, and to create a large-scale, multi-center, normalized and standardized long-term case database.

 

Currently, the team has initially constructed a diagnostic and typing model of the brain function network for depression by combining brain imaging and clinical neuropsychological assessment. They propose to further apply digital phenotyping technology, including audio, video, EEG and eye movement, and other multidimensional stereoscopic big data for feature extraction, screening, and modeling. He pointed out that such multidimensional stereoscopic big data may improve the accuracy rate of depression diagnosis, optimize screening and assessment methods as well as the prediction of risk events; the combination of big data and artificial intelligence technologies has great potential in providing sensitive and specific diagnostic and treatment solutions for patients.

 

Unlocking genetic secrets and probing knowledge graphs

 

Professor Lin Guaning of the School of Biomedical Engineering at Shanghai Jiao Tong University demonstrated the results achieved by applying GPT technology in the fields of mental health and brain science research through continuous optimization of GPT training and rule setting.

 

In the area of stress, depression and suicide risk detection, the research team has improved the accuracy of GPT through the prompt engineering and has been able to initially achieve accurate classification and prediction. The team also successfully extracted structured information from unstructured text, and by providing a prescribed schema for the GPT, achieved the standardized storage of such information in the database, which can be valuable for future research and clinical practice. Despite the challenges encountered in the application process, such as API interface limitations, Professor Lin believes that large language models like GPT will play an increasingly important role in mental health and brain science research, and will soon have the ability to process data other than textual language, such as multimodal data from imaging, EEG, and bio-omics, and to reason about the intrinsic logic amongst the data. This will revolutionize the existing research paradigm and drive the rapid development of the mental health and brain science research.