Elicit Launches Systematic Review Automation for Researchers
Elicit has launched systematic review automation that handles literature searching, paper screening, data extraction, and initial drafting, reducing the time to conduct systematic literature reviews from months to days.
Elicit, the AI research assistant, has launched a systematic review automation feature that dramatically accelerates one of the most time-consuming processes in academic research. Systematic reviews, which typically take 6-18 months, can now be completed in days with AI handling the most labor-intensive steps.
The system automates four key phases of systematic reviews: comprehensive literature searching across multiple databases, screening papers against inclusion and exclusion criteria, extracting data points from included studies, and drafting review sections including risk-of-bias assessments and evidence synthesis.
Elicit's screening capability processes thousands of papers against user-defined criteria, achieving 98% agreement with expert human screeners in validation studies. The system explains its screening decisions, allowing researchers to quickly review and override any questionable classifications.
Data extraction uses multimodal AI to pull information from text, tables, figures, and supplementary materials. Extracted data is organized into standardized evidence tables that follow PRISMA and Cochrane guidelines, with each data point linked to its source for verification.
The systematic review feature is available on Elicit's Plus plan at $10/month and Pro plan at $42/month. The tool has been validated in collaboration with the Campbell Collaboration and several Cochrane review groups. Elicit emphasizes that the tool is designed to assist rather than replace human judgment, with researchers maintaining final authority over all review decisions.
More News
NVIDIA Launches NIM Microservices for Enterprise AI Deployment
NVIDIA has launched NIM (NVIDIA Inference Microservices), a suite of containerized AI model serving packages that reduce enterprise AI deployment time from weeks to hours with optimized inference performance.
AI Agents Market Reaches $15 Billion as Enterprise Adoption Surges
The global market for AI agents — autonomous AI systems that can plan, execute, and iterate on complex multi-step tasks — has reached $15 billion in annual spending, according to a new report from McKinsey. This represents a 200% increase from 2025, driven by enterprise adoption of agentic AI for customer service, software development, data analysis, and business process automation. The report identifies three tiers of AI agent adoption: basic agents that handle single-step tasks like email responses and appointment scheduling (adopted by 65% of enterprises), intermediate agents that manage multi-step workflows like report generation and data pipeline management (35% adoption), and advanced agents that autonomously execute complex processes like code deployment and financial analysis (8% adoption). The largest spending categories are customer service agents ($4.2B), coding agents ($3.8B), and data analysis agents ($2.5B). McKinsey projects the market will reach $45 billion by 2028 as agent reliability improves and enterprises become more comfortable delegating complex decisions to AI. Key enabling platforms include OpenAI's Agents SDK, Anthropic's Claude computer-use capabilities, and LangChain's agent framework. The report warns that agent governance and monitoring remain underdeveloped, with most enterprises lacking adequate oversight mechanisms for autonomous AI actions.
Microsoft 365 Copilot Gets Custom AI Agents and Actions
Microsoft has updated 365 Copilot with custom AI agent creation, allowing organizations to build agents that automate complex workflows spanning Word, Excel, Outlook, Teams, and SharePoint without code.
GPT-5.2's Agentic Mode Transforms Enterprise Workflows
OpenAI's GPT-5.2 introduced a fundamentally new approach to agentic task completion that is already transforming enterprise workflows. The model can now maintain coherent plans across 50+ sequential tool calls with parallel execution, reducing latency in complex automation pipelines by up to 60%. Early enterprise adopters report that GPT-5.2's agentic mode handles tasks like multi-step data analysis, cross-platform content publishing, and automated code review workflows that previously required custom orchestration code. The key innovation is what OpenAI calls deliberative alignment — a training approach that lets the model dynamically allocate compute to harder sub-tasks while breezing through simpler ones. This means a single agentic session can handle both quick lookups and deep reasoning without manual configuration. Several Fortune 500 companies have reported 40-70% time savings on analyst workflows by deploying GPT-5.2 agents through the API. However, reliability remains a concern — OpenAI acknowledges a 3-5% failure rate on chains exceeding 30 steps, and enterprise deployments require human-in-the-loop checkpoints for critical decisions.