Welcome to the American Society for Quality in New Hampshire AND MAINE 

Section Mission Statement

To be recognized as the primary source for information and education in the Quality Sciences in the States of New Hampshire and Maine.

Upcoming Events

Click on Meetings / Presentations for more information

Thursday May 14, 2026

Laura Halleck, Senior Consultant, Quality Management Systems, QSG will present “AI in Action: Enhancing Risk Based Audit Planning”.

Venue: Hilton Garden Inn Portland Airport in Portland, ME

Time: 5:30 PM to 8:00 PM

Registration: https://mp.gg/ficrfzavp

Description:

This presentation will explore how AI can support risk-based audit planning, delivering more efficient, targeted, and effective audits. The presenter will begin with an introduction to risk-based thinking, emphasizing its importance in focusing resources on areas of higher risk to improve audit effectiveness and ensure quality system compliance. The core of the session will outline the steps to develop a risk-based audit plan, from risk identification to prioritization, including a demonstration of how AI can optimize the audit planning process by automating risk analysis, identifying patterns, and providing data-driven insights that enhance decision-making.

Tuesday May 26, 2026

Attrayee (Atty) Chakraborty, will present “Case Study: Engineering Safety for AI-Enabled Medical Applications”

Venue: Lenzi's Catering & Function Facility, Dracut, MA 01826

Time: 5:30 pm to 8:00 pm

Registration: https://mp.gg/ssxvwgrbo

This is a joint meeting of Granite State and Merrimack Valley ASQ sections.

Description:

The FDA has introduced the Early Alerts program and issued warning letters for AI device misclassification, signaling heightened enforcement in this area. While FDA guidance focuses on risk elements to be included in a submission, this talk will emphasize the "how" to create and implement a comprehensive risk management file for an AI-enabled medical device—comprising a risk management plan, risk assessment, and risk management report.

We will demonstrate how Failure Modes and Effects Analysis (FMEA) can systematically identify, assess, and mitigate AI-specific risks such as algorithmic bias, data drift, and unintended behaviors, illustrated through a case study of an AI-enabled medical device. Attendees will learn how FMEA aligns with ISO 1497/AAMI 34971 and engineer safety through the total product lifecycle (TPLC). Drawing on FDA’s latest guidance on AI-enabled device software functions and lifecycle management, we highlight the need to integrate risk management, data quality assurance, algorithm transparency, and post-market monitoring across the total product lifecycle, enabling robust compliance and patient safety.

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