Early screening for gastrointestinal diseases is of vital importance for reducing mortality and improving life expectancy. In this paper, a biomimetic artificial whisker system with artificial intelligence-enabled self-learning capability is proposed for this purpose. Unlike the traditional vision-based clinical screening routine, which is capturing data, transmitting back, processing, and diagnosing by clinicians based on their clinical knowledge, the proposed solution provides an end-to-end screening strategy based on tactile information without human intervention, so that the abominable working conditions which prevent optical sensors from working optimally, and the personal bias introduced by clinicians can be eliminated. In the benchmark experiment, the electrical characteristic and core functions, such as texture discrimination, distance estimation, and hardness evaluation, are assessed to evaluate the benchmark performance of the proposed system. In the following pilot study, a medical phantom is used for detecting some common tissue structures. After iterative training, the test accuracy can reach 100%, showing the great potential of the proposed system as a new sensing modality in improving clinical disease screening. In the future, a large-scale database can be built through massive clinical trials, so as to train and deploy artificial intelligence algorithms with enhanced disease detection ability, higher accuracy, and improved robustness. Meanwhile, this tactile-based system is a promising foundation that replicates the sense of touch in minimally invasive surgery and non-invasive surgery.