## 1 Introduction  
Lung cancer is a highly prevalent cancer type and a major cause of cancer-related deaths, accounting for 18.4% of such deaths [1]. It is also among the most commonly diagnosed cancers, accounting for 11.4% of new cancer cases [2, 3]. Low-dose computed tomography (LDCT) has been shown to be an effective tool for early lung cancer screening, resulting in a 20% reduction in mortality rates [4]. One of the major challenges of LDCT is the high false-positive rate (23%) [4], leading to unnecessary tests, patient anxiety, and invasive procedures. Currently, all low-dose CT scans (LDCTs) for screening are adjiociated for risk by radiologists using the Lung-screening Reporting and Data System (Lung-RADS) [5], which assigns a score (1, 2, 3, 4A, 4B, or 4X) based on the most worrisome nodule in an LDCT. An algorithm then suggests the next step in the workup of these findings for the clinician. Balancing the high probability of malignancy with the significant difficulty associated with investigating the nodule's etiology leaves the clinicians with a management conundrum of intervening versus watchful waiting. Biopsy strategies for these nodules are either remarkably invasive (e.g., surgical resection) or fraught with increased risk of complications and poor yields. Waiting for the nodule to grow to a size that is more amenable to biopsy also allows the potential to metastas whilst waiting. AI strategies demonstrate excellent performance in predicting the malignancy potential of a nodule within a year [6, 7]. Computer-aided diagnostic (CAD) tools based on AI have demonstrated high efficacy in low-dose CT lung cancer screening, with the performance levels approaching those of double readings by radiologists. These tools have been recommended for use as second reader [7]. A review of AI-based CAD research revealed that deep learning networks had a pooled sensitivity and specificity of 93% and 68%, respectively, [8]. Advancements in deep learning have led to significant improvements in medical image analysis [9], the accuracy of CAD-based cancer screening in particular [10, 11, 12, 13, 14, 15, 16], and the use of CAD tools to support clinical findings [9]. Most fully supervised deep learning algorithms are often limited by scarcity of annotations for medical image analysis. The cost of annotating 3D medical images at the voxel level is high, since it is labor-intensive, requires proficient clinical experts, and could be inaccurate [17, 18, 19, 20, 21]. Therefore, algorithms that can learn and generalize from limited, sparse annotations with limited supervision are essential for deep learning models in medical image analysis [22]. Given the rapid expansion of AI applications in healthcare, it is crucial to establish standardized AI software specifications and implement a safe process for continuous evaluation, updating, and evolution that can be used in clinical practice [7, 23, 24]. In real-world clinical operation, obtaining ground truth data for evaluation and monitoring of AI models require clinicians and experts to provide feedback and annotation, which is impractical for real-time or near-real-time practice. In many cases, the ground truth labels are not available until further procedural tests are performed and confirmed. Transfer learning [9, 25, 26], self-supervised learning [19, 20, 27, 28], semi-supervised learning [29, 30], uncertainty estimations [31], and active learning [32, 33] are some approaches to overcome the need for human annotations. Our study employs both active learning and semisupervised learning techniques to train and evaluate AI models to classify lung cancer malignancy from CT scans using a smaller subset of labeled images. We demonstrate an active, semisupervised expectation maximization for CAD tasks (ASEM-CAD) using pulmonary CT scans. Semisupervised learning is performed using an expectation maximization (EM) algorithm to train CNN-based CAD algorithms with observed and latent image labels (pseudolabels).