In the world of clinical research, software plays a pivotal role in controlling and managing a vast array of intricate tasks. From data collection and analysis to project management and regulatory compliance, the footprint of software solutions in this field is enormous. As we peer into the crystal ball to discern the future of clinical research software, it becomes evident that the trends are aligning towards increased efficiency, profound intelligence, and heightened compliance.

One key trend that is increasingly gaining momentum is the deployment of Artificial Intelligence (AI) in clinical research software. AI, a multidisciplinary field which involves the simulation of human intelligence processes by machines, is transforming the landscape of clinical research. AI-powered software can effectively manage and analyze massive volumes of clinical data, which is instrumental in accelerating drug discovery and development. Machine learning algorithms, a subset of AI, can identify patterns and trends in data, enabling researchers to make insightful predictions about disease progression and treatment efficacy.

Beyond AI, another trend reshaping clinical research software is the integration of blockchain technology. Blockchain, a distributed ledger technology, provides a secure, immutable record of transactions, making it nearly impossible to manipulate data. In the context of clinical trials, blockchain can enhance transparency, security, and traceability, thereby ensuring data integrity and fostering trust among stakeholders.

Yet, while AI and blockchain offer immense potential, there are trade-offs. AI algorithms are typically "black boxes", meaning they don't readily reveal how they reached a decision, which could raise ethical and legal issues. Similarly, while blockchain provides enhanced security, its implementation could be complex, expensive and might face regulatory challenges.

Another forward-thinking trend is the adoption of cloud-based solutions. Cloud computing offers flexible, scalable, and cost-effective solutions for storing and managing clinical data. It facilitates real-time data sharing, collaboration, and remote access, thus enabling decentralized clinical trials. However, cloud solutions are not without their pitfalls. They might pose security risks and require robust mechanisms to ensure data privacy and compliance with regulations such as the General Data Protection Regulation (GDPR).

Taking a quantum leap, Quantum Computing (QC) is poised to revolutionize clinical research software in the future. Quantum computers, which leverage the principles of quantum mechanics, can process data exponentially faster than traditional computers. This could expedite computational tasks in clinical research, such as genomic sequencing or molecular modeling. However, QC is still in its nascent stage, and it might be years before practical, commercial quantum computers become a reality.

To mitigate some of the risks associated with these emerging trends, regulatory bodies such as the Food and Drug Administration (FDA) are introducing guidelines on software validation and data integrity for clinical research software. These regulations aim to ensure the safety, reliability, and performance of software used in clinical research.

In sum, the future of clinical research software is shaping up to be a fascinating amalgamation of technological advancements aimed at streamlining and enhancing clinical research processes. While these trends promise a slew of benefits, they also underscore the need for robust risk management strategies. Organizations must strike a delicate balance between embracing innovation and maintaining regulatory compliance. A deep understanding of the technology and its implications, coupled with a proactive approach towards regulatory developments, will be instrumental in navigating the future of clinical research software.