Optical coherence tomography (OCT) is widely used for detection of ophthalmic diseases, such as glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy. Using a low-coherence-length light source, OCT is able to achieve high axial resolution in biological samples; this depth information is used by ophthalmologists to assess retinal structures and characterize disease states. However, OCT systems are often bulky and expensive, costing tens of thousands of dollars and weighing on the order of 50 pounds or more. Such constraints make it difficult for OCT to be accessible in low-resource settings. In the U.S. alone, only 15.3% of diabetic patients meet the recommendation of obtaining annual eye exams; the situation is even worse for minority/under-served populations. In this study, we focus on data acquired with a low-cost, portable OCT (p-OCT) device, characterized by lower resolution, scanning rate, and imaging depth than a commercial OCT system. We use generative adversarial networks (GANs) to enhance the quality of this p-OCT data and then assess the impact of this enhancement on downstream performance of artificial intelligence (AI) algorithms for AMD detection. Using GANs trained on simulated p-OCT data generated from paired commercial OCT data degraded with the point spread function (PSF) of the p-OCT device, we observe improved AI performance on p-OCT data after single-image super-resolution. We also achieve denoising after image-to-image translation. By exhibiting proof-of-principle AI-based AMD detection even on low-quality p-OCT data, this study stimulates future work toward low-cost, portable imaging+AI systems for eye disease detection.