The development of advanced, scalable silicon (Si) photodetector arrays addresses critical challenges in both biomedical imaging and machine vision. Traditional fluorescence imaging methods in biomedical applications struggle with miniaturization, which is essential for ultimately implantable neural imaging and for point-of-care (POC) settings. Concurrently, machine vision systems face inefficiencies due to the physical separation of sensing and computing units, leading to high power consumption, data transfer issues, and escalating data storage challenges. In response to these challenges, this dissertation explores innovative solutions in two primary areas: fluorescence detection for biomedical applications and advanced in-sensor computing for machine vision applications. Firstly, in the realm of cell imaging, we focused on developing a spectrally filtered passive Si photodiode (PD) array for on-chip fluorescence imaging of intracellular calcium (Ca2+) dynamics. This device integrates a high-extinction-ratio spectral filter that effectively filters out strong excitation light, making the detection of weak fluorescence emission light possible. It captures both static and dynamic Ca2+ changes in C2C12 cells, demonstrating significant potential for pharmaceutical screening, cellular network studies, and potentially implantable neural interfaces. Additionally, we introduced an on-chip ratiometric aptasensing device to monitor cytokine dynamics. Utilizing pairs of sites-selectively spectrally filtered Si PDs functionalized with DNA aptamer probes, this device enables rapid detection of fluorescence changes due to aptamer-cytokine binding events at high sensitivity and specificity. Furthermore, the consistent and reliable performance across multiple runs makes it highly suitable for long-term POC diagnostics and therapeutic screening. Transitioning to the field of machine vision, the dissertation advances the concept of in-sensor visual processing by developing dual-gate amorphous-silicon photodiode arrays. These arrays are reconfigurable and can be gated to output either positive or negative photocurrent with zero power operation, thus enabling in-sensor computing in analog domain. By mimicking human retinal pathways, these bio-inspired arrays handle both static and dynamic visual information, allowing for multiplexed event sensing with sub-millisecond precision and edge detection of multiple objects. This innovative approach eliminates the need for extensive circuitry and the physical separation of sensing and computing units, significantly reducing power consumption and data transfer bottlenecks. The integration of these optoelectronics across different applications not only demonstrates their versatility but also underscores their potential to significantly impact areas ranging from pharmaceutical screenings to the development of autonomous machines. Each chapter builds upon the last, illustrating a clear trajectory towards achieving large-scale, efficient, and integrated systems that can adapt to both biomedical and intelligent environments. The potential for these advanced devices to revolutionize field-specific hardware and contribute to the advancement of intelligent systems is immense, paving the way for future innovations in these fields.