# AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionals

arXiv: https://arxiv.org/abs/2412.15444 · HTML: https://mlsanity.com/paper/2412.15444/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 105 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 24 of 24)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making](https://mlsanity.com/paper/1902.02960/) (2019)
- [Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering](https://mlsanity.com/paper/2303.05352/) (2023)
- [Graphologue: Exploring Large Language Model Responses with Interactive Diagrams](https://mlsanity.com/paper/2305.11473/) (2023)
- [Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology](https://mlsanity.com/paper/2402.14252/) (2024)
- [Human-Centered Artificial Intelligence and Machine Learning](https://mlsanity.com/paper/1901.11184/) (2019)
- [Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegability](https://mlsanity.com/paper/1902.03245/) (2019)
- [How Knowledge Workers Think Generative AI Will (Not) Transform Their Industries](https://mlsanity.com/paper/2310.06778/) (2023)
- [Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking](https://mlsanity.com/paper/2308.07517/) (2023)
- [Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology](https://mlsanity.com/paper/2308.02180/) (2023)
- [Threddy: An Interactive System for Personalized Thread-based Exploration and Organization of Scientific Literature](https://mlsanity.com/paper/2208.03455/) (2022)
- [CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context](https://mlsanity.com/paper/2302.07302/) (2023)
- [ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions](https://mlsanity.com/paper/2310.01331/) (2023)
- [Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections](https://mlsanity.com/paper/2302.06754/) (2023)
- [A Systematic Review and Thematic Analysis of Community-Collaborative Approaches to Computing Research](https://mlsanity.com/paper/2207.04171/) (2022)
- ["It would work for me too": How Online Communities Shape Software Developers' Trust in AI-Powered Code Generation Tools](https://mlsanity.com/paper/2212.03491/) (2022)
- [Understanding Questions that Arise When Working with Business Documents](https://mlsanity.com/paper/2203.15073/) (2022)
- [To Reuse or Not To Reuse? A Framework and System for Evaluating Summarized Knowledge](https://mlsanity.com/paper/2102.06231/) (2021)
- [Effects of Sensemaking Translucence on Distributed Collaborative Analysis](https://mlsanity.com/paper/1603.04931/) (2016)
- [GRAFS: Graphical Faceted Search System to Support Conceptual Understanding in Exploratory Search](https://mlsanity.com/paper/2302.09448/) (2023)
- ["It depends": Configuring AI to Improve Clinical Usefulness Across Contexts](https://mlsanity.com/paper/2407.11978/) (2024)

## Built on by (most cited first, 3 of 3)

- [More Than "Means to an End": Supporting Reasoning with Transparently Designed AI Data Science Processes](https://mlsanity.com/paper/2603.24877/) (2026)
- [What happens when reviewers receive AI feedback in their reviews?](https://mlsanity.com/paper/2602.13817/) (2026)
- [A Systematic Review on the Generative AI Applications in Human Medical Genomics](https://mlsanity.com/paper/2508.20275/) (2025)

## Abstract (arXiv)

Generative AI has the potential to transform knowledge work, but further research is needed to understand how knowledge workers envision using and interacting with generative AI. We investigate the development of generative AI tools to support domain experts in knowledge work, examining task delegation and the design of human-AI interactions. Our research focused on designing a generative AI assistant to aid genetic professionals in analyzing whole genome sequences (WGS) and other clinical data for rare disease diagnosis. Through interviews with 17 genetics professionals, we identified current challenges in WGS analysis. We then conducted co-design sessions with six genetics professionals to determine tasks that could be supported by an AI assistant and considerations for designing interactions with the AI assistant. From our findings, we identified sensemaking as both a current challenge in WGS analysis and a process that could be supported by AI. We contribute an understanding of how domain experts envision interacting with generative AI in their knowledge work, a detailed empirical study of WGS analysis, and three design considerations for using generative AI to support domain experts in sensemaking during knowledge work. CCS CONCEPTS: Human-centered computing, Human-computer interaction, Empirical studies in HCI Additional Keywords and Phrases: whole genome sequencing, generative AI, large language models, knowledge work, sensemaking, co-design, rare disease Contact Author: Angela Mastrianni (This work was done during the author's internship at Microsoft Research) Ashley Mae Conard and Amanda K. Hall contributed equally