Objective:To assess the value of artificial intelligence（AI）assisted system in the diagnosis of malignancy in follicular thyroid tumours，and to compare with the diagnostic results of doctors with different levels of experience.Methods:A total of 101 nodules were retrospectively collected from 86 patients with follicular thyroid tumours who underwent surgical treatment at Peking Union Medical College Hospital，Chinese Academy of Medical Sciences，Peking Union Medical College from May 2016 to January 2018.The nodules were classified into risk group（29 patients，34 nodules，including 15 follicular carcinomas and 19 follicular tumours of indeterminable malignant potential）and benign group（59 atients，67 nodules，including 15 follicular adenomas and 52 nodular goitre adenomatoid hyperplasia）. The sensitivities，specificities and accuracies of the AI system，two doctors of different seniorities（one junior A and one senior B），and guidelines of thyroid ultrasound malignancy risk stratification［including the 2015 American Thyroid Guidelines（ATA），the 2017 American College of Radiology Thyroid Imaging Reporting and Data System（ACR TI-RADS），the 2020 Chinese Society of Ultrasound，Thyroid Imaging Reporting and Data System（C-TIRADS）］（classified by a senior doctor C）for diagnosing follicular tumours in the risk group and follicular carcinomas were calculated and compared.Results:The AI system showed a sensitivity of 46.7%，specificity of 89.6% and accuracy of 81.7% for diagnosing follicular carcinoma；and a sensitivity of 32.4%，specificity of 89.6% and accuracy of 70.3% for diagnosing follicular neoplasms（risk group）. Compared with junior doctor A，the specificity of AI system in diagnosing follicular cancer and follicular neoplasms（risk group）was higher（89.6% vs. 83.6%， P=0.020；89.6% vs. 73.1%， P=0.020），and the differences of sensitivity were not significant（46.7% vs. 32.4%， P=0.181；32.4% vs. 11.8%， P=0.073）. The difference of sensitivity and specificity were not statistically significant between the AI system and senior doctor B（all P＞0.05）.The differences in area under the curve for diagnosis of follicular carcinoma and follicular tumour（risk group）were not statistically significant between the AI system compared to junior doctor A，senior doctor B，the C-TIRADS，ATA guideline，and ACR TI-RADS（all P＞0.05）. Conclusions:Ultrasound-based AI-assisted diagnostic system is similarly efficient in diagnosing follicular thyroid tumours as experienced doctors，and the AI system diagnostic specificity is superior to that of junior doctors.

Objective:To assess the value of artificial intelligence（AI）assisted system in the diagnosis of malignancy in follicular thyroid tumours，and to compare with the diagnostic results of doctors with different levels of experience.Methods:A total of 101 nodules were retrospectively collected from 86 patients with follicular thyroid tumours who underwent surgical treatment at Peking Union Medical College Hospital，Chinese Academy of Medical Sciences，Peking Union Medical College from May 2016 to January 2018.The nodules were classified into risk group（29 patients，34 nodules，including 15 follicular carcinomas and 19 follicular tumours of indeterminable malignant potential）and benign group（59 atients，67 nodules，including 15 follicular adenomas and 52 nodular goitre adenomatoid hyperplasia）. The sensitivities，specificities and accuracies of the AI system，two doctors of different seniorities（one junior A and one senior B），and guidelines of thyroid ultrasound malignancy risk stratification［including the 2015 American Thyroid Guidelines（ATA），the 2017 American College of Radiology Thyroid Imaging Reporting and Data System（ACR TI-RADS），the 2020 Chinese Society of Ultrasound，Thyroid Imaging Reporting and Data System（C-TIRADS）］（classified by a senior doctor C）for diagnosing follicular tumours in the risk group and follicular carcinomas were calculated and compared.Results:The AI system showed a sensitivity of 46.7%，specificity of 89.6% and accuracy of 81.7% for diagnosing follicular carcinoma；and a sensitivity of 32.4%，specificity of 89.6% and accuracy of 70.3% for diagnosing follicular neoplasms（risk group）. Compared with junior doctor A，the specificity of AI system in diagnosing follicular cancer and follicular neoplasms（risk group）was higher（89.6% vs. 83.6%， P=0.020；89.6% vs. 73.1%， P=0.020），and the differences of sensitivity were not significant（46.7% vs. 32.4%， P=0.181；32.4% vs. 11.8%， P=0.073）. The difference of sensitivity and specificity were not statistically significant between the AI system and senior doctor B（all P＞0.05）.The differences in area under the curve for diagnosis of follicular carcinoma and follicular tumour（risk group）were not statistically significant between the AI system compared to junior doctor A，senior doctor B，the C-TIRADS，ATA guideline，and ACR TI-RADS（all P＞0.05）. Conclusions:Ultrasound-based AI-assisted diagnostic system is similarly efficient in diagnosing follicular thyroid tumours as experienced doctors，and the AI system diagnostic specificity is superior to that of junior doctors.

DNA-encoded chemical library (DEL) links the power of amplifiable genetics and the non-self-replicating chemical phenotypes, generating a diverse chemical world. In analogy with the biological world, the DEL world can evolve by using a chemical central dogma, wherein DNA replicates using the PCR reactions to amplify the genetic codes, DNA sequencing transcripts the genetic information, and DNA-compatible synthesis translates into chemical phenotypes. Importantly, DNA-compatible synthesis is the key to expanding the DEL chemical space. Besides, the evolution-driven selection system pushes the chemicals to evolve under the selective pressure, i.e., desired selection strategies. In this perspective, we summarized recent advances in expanding DEL synthetic toolbox and panning strategies, which will shed light on the drug discovery harnessing in vitro evolution of chemicals via DEL.