Pith. sign in

REVIEW 6 minor 101 references

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

T0 review · 0 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper argues that genetic professionals want a generative AI assistant that flags cases for reanalysis and synthesizes gene and variant evidence, with humans verifying the AI's output.

desk verdict Solid qualitative study of AI support for WGS analysis; worth refereeing, but fix the participant-count inconsistency before publication. read the letter →

arxiv 2412.15444 v1 pith:TNJFNNSL submitted 2024-12-19 cs.HC cs.AI

Angela Mastrianni , Hope Twede , Aleksandra Sarcevic , Jeremiah Wander , Christina Austin-Tse , Scott Saponas , Heidi Rehm , Ashley Mae Conard
show 1 more author
Amanda K. Hall
This is my paper · ORCID
classification cs.HCcs.AI
keywords wholegenomesequencinggenerativeAIlargelanguagemodelsknowledgeworksensemakingco-designrarediseasehuman-AIinteraction
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that the hard part of whole-genome sequencing (WGS) for rare disease diagnosis is sensemaking, and that genetic professionals want a generative AI assistant aimed at that bottleneck rather than at full automation. Through interviews with 17 genetics professionals and co-design sessions with six, the study identifies two AI tasks that every co-design participant prioritized: flagging unsolved cases for reanalysis when new scientific findings appear, and aggregating and synthesizing key information about genes and variants from scientific publications. The paper further claims that the assistant should be human-in-the-loop: AI drafts tables, summaries, notes, and presentations, while analysts edit, verify, tag, and share those artifacts. From this, the authors derive three design considerations for AI-enhanced sensemaking: facilitate distributed sensemaking, support both initial sensemaking and re-sensemaking, and combine multiple modalities of evidence. If the paper is right, these results give concrete targets for generative AI in clinical genomics and a template for designing such tools in other knowledge-work domains.

What carries the argument

The carrying mechanism is the co-design loop: interviews surface current challenges, a group workshop generates candidate AI tasks and interaction sketches, those sketches are turned into a clickable prototype of a generative-AI assistant embedded in the web-based genome analysis platform used at the study's partner institution, and individual design walk-throughs refine the resulting design considerations. Conceptually, the argument is organized around the named sensemaking model that distinguishes foraging (searching, filtering, and synthesizing information) from sensemaking proper (building, refining, and presenting models of information). The prototype's three features—reanalysis flagging, an AI-generated evidence table for gene and variant interpretation, and AI-drafted presentation slides—are the concrete objects through which the paper maps AI tasks onto that model.

What would settle it

Deploy a working version of the assistant with a larger set of genetic professionals across multiple institutions and measure whether the two prioritized tasks actually change practice: if analysts outside the study site do not rank reanalysis flagging and gene-and-variant evidence synthesis as top AI tasks, or if task-based testing shows no reduction in per-case analysis time or no increase in diagnostic yield, the paper's central claim about the high-value AI sensemaking tasks would be undermined.

Watch

Extended reading notes

Core claim

The paper's central claim is that sensemaking—the foraging and model-building work of finding, synthesizing, and interpreting information—is both the main bottleneck in WGS analysis and the process generative AI can most usefully support. Analysts struggle to aggregate information about genes and variants scattered across publications and databases, to share findings with colleagues, and to decide which unsolved cases deserve reanalysis as new papers appear. Asked to design an assistant, all six co-design participants prioritized the same two capabilities: flagging cases for reanalysis based on new scientific findings, and aggregating and synthesizing key gene-and-variant information from publications. The paper does not conclude that AI should replace the analyst; instead, participants envisioned a human-in-the-loop assistant that produces evidence tables, summaries, notes, and presentation drafts that analysts then edit, verify, and share, turning individual sensemaking into collaborative and distributed sensemaking. Three design considerations follow: facilitate distributed sensemaking, support initial sensemaking and re-sensemaking, and combine evidence from multiple modalities.

Load-bearing premise

The design considerations rest on the assumption that six self-selected genetic professionals from a single institution, mostly variant analysts, are representative enough of the profession that their prioritized AI tasks and interaction preferences can support general design guidance.

Editorial extensions

If this is right

  • A generative AI assistant that flags cases for reanalysis based on new scientific findings could turn reanalysis from a periodic, manually triggered event into a more continuous process driven by new evidence.
  • If analysts adopt the AI-generated evidence table, the time spent foraging across databases and publications per case could drop, shifting effort from searching to verifying and editing AI output.
  • Sharing verified, editable AI-generated evidence tables and notes across analysts could reduce duplicated sensemaking work and support distributed sensemaking within and between institutions.
  • Systems should show verification status, edits, and note authorship so readers can calibrate trust in AI-generated artifacts without hiding the model's raw inaccuracies.
  • Designers need to balance comprehensive evidence for gene and variant review against selective alerts for reanalysis flags, because analysts reject both over-filtering and too much noise.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension of the paper's design considerations is that shared, verified AI evidence tables will measurably reduce duplicate literature searching in a laboratory, shrinking per-case interpretation time for genes already curated by colleagues.
  • The same human-in-the-loop artifact model likely transfers to other knowledge-work domains where professionals track a growing literature and revisit prior cases, such as diagnostic radiology, pathology, or legal research; the paper only gestures at this generality.
  • If continuous reanalysis becomes practical, the reimbursement models and patient- or clinician-initiated reanalysis triggers the paper mentions may become binding constraints, so real deployments would need policy-aware designs rather than pure automation.
  • Because the co-design participants all came from one institution and were mostly variant analysts, the two prioritized tasks are best treated as hypotheses about the broader profession until a multi-site participatory study confirms them.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

0 major / 6 minor

Summary. This paper presents a two-phase qualitative study with genetic professionals involved in whole-genome sequencing (WGS) analysis for rare disease diagnosis. Phase I comprised semi-structured interviews with 17 professionals and identified three challenges: aggregating and synthesizing gene/variant information, sharing findings with colleagues, and prioritizing cases for reanalysis. Phase II comprised co-design sessions, including a group workshop and individual design walk-throughs with six professionals from the Broad Institute, which led to a prototype of an AI assistant embedded in the seqr platform. Participants prioritized two AI tasks—flagging cases for reanalysis based on new scientific findings, and aggregating/synthesizing key gene/variant information from publications—and their feedback produced two design themes: balancing comprehensive and selective evidence, and collaboratively interpreting/verifying AI-generated information. The paper frames these findings through sensemaking theory and proposes three design considerations for generative AI support of sensemaking in knowledge work.

Significance. If the findings hold, the paper makes a useful empirical contribution to HCI research on human-centered generative AI. It documents how one group of domain experts envisions delegating sensemaking tasks and grounds design considerations in participant-generated prototype feedback. The study is methodologically transparent about recruitment, data analysis, and limitations; it includes participant quotes and figures showing the prototype and workflow; and it explicitly acknowledges the single-institution, voluntary-participation limitations of Phase II. The central claims are descriptive and appropriately qualified, with no overreach to population-level generalizations. The paper does not claim that the prototype was evaluated for effectiveness; it is clearly framed as a design probe. These strengths make the work appropriately scoped for publication.

minor comments (6)
  1. [Section 4.2 / Table 1] The role counts in the text do not match Table 1: the text reports 17 interviewees as seven variant analysts, two laboratory directors, two clinicians, two methods developers, and two program managers, which sums to 15, whereas Table 1 lists eight variant analysts and four clinicians in the combined unique sample of 18. Please reconcile the Phase I role breakdown and clarify the unique participant count.
  2. [Section 4.4.3] The subsection numbering is duplicated: both 'Individual Design Walk-Through Sessions: Protocol' and 'Individual Design Walk-Through Sessions: Data Analysis' are labeled 4.4.3. The latter should be renumbered.
  3. [Figure 3 caption] The caption says 'WGS (whole gene sequencing)' but WGS stands for whole genome sequencing; please correct this.
  4. [Section 7.2.2] There is a typo: 'analysists' should be 'analysts', and 't o' should be 'to'.
  5. [Section 6.2.1] Please clarify whether the prototype content (summaries, tables, chat responses) was produced by an actual LLM or hand-crafted for the design probe; this affects how readers interpret the walk-through feedback on 'AI-generated' artifacts.
  6. [Section 7.4] Consider adding to the limitations that Phase II participants were all familiar with seqr by design, which may make their design ideas incremental relative to that tool; the current limitation paragraph covers institution and voluntariness but not tool familiarity.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claims are empirical findings from interviews and co-design sessions, not derived from the authors' prior work or from fitted inputs.

full rationale

This paper reports a qualitative, empirical study rather than a formal derivation or predictive model. The central claim—that genetic professionals face sensemaking challenges and prioritize an AI assistant for flagging reanalysis cases and synthesizing gene/variant information—is supported by interview data (Section 5) and by participant voting in the Phase II walk-through sessions (Section 6.1, Figure 4). No fitted parameter is later renamed as a prediction, and no equation or formal model reduces to its own inputs. The Phase I findings were used to focus the Phase II workshop, but that is a deliberate study design choice, not a definitional or statistical forcing of the outcomes. Citations to prior work that shares authors or institutional context, such as [2] and [61], are used for background workflow information and platform description; they are not load-bearing for the paper's empirical conclusions about participant preferences. The limitation that Phase II participants came from a single institution is explicitly acknowledged in Section 7.4, which further supports the honest, non-circular treatment of the findings. The minor participant-count inconsistency between Section 4.2 and Table 1 is a reporting error, not evidence of circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no mathematical parameters or hypothetical entities. Its evidence is qualitative, with the stated assumptions above.

assumptions (3)
  • domain assumption Participants' self-reports in interviews and co-design sessions accurately reflect their real work practices and needs.
    The study relies on interview and co-design data as evidence about challenges and desired AI support; this is standard for qualitative HCI but is an unverified assumption about self-report validity.
  • domain assumption The six Phase II participants, all from one institution, can generate design considerations that are useful beyond that institution.
    The paper generalizes from a small, single-site sample; the authors acknowledge this in Section 7.4.
  • ad hoc to paper The prototype based on ACTN2 publications represents a plausible generative AI assistant for WGS analysis.
    The prototype was created by the research team to elicit feedback; its representativeness of actual LLM outputs is not evaluated.

how reviews work

0 comments
Cite this review

Pith. "Pith review of AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionals." pith.science (2026). https://pith.science/paper/TNJFNNSL

@misc{pith2026241215444,
  author       = {Pith},
  title        = {Pith review of: AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionals},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TNJFNNSL}},
  note         = {Machine review of arXiv:2412.15444}
}
read the original abstract

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

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

101 extracted references · 66 canonical work pages

  1. [1]

    and Sandström, K

    Aranda-Muñoz, Á., Florin, U., Yamamoto, Y., Eriksson, Y. and Sandström, K. 2022. Co -Designing with AI in Sight. Proceedings of the Design Society. 2, (May 2022), 101–110. DOI:https://doi.org/10.1017/pds.2022.11

    work page doi:10.1017/pds.2022.11 2022

  2. [2]

    Austin-Tse, C.A. et al. 2022. Best practices for the interpretation and reporting of clinical whole genome sequencing. npj Genomic Medicine. 7, 1 (Apr. 2022), 1–13. DOI:https://doi.org/10.1038/s41525-022-00295- z

    work page doi:10.1038/s41525-022-00295- 2022

  3. [3]

    Designing Interactions

    Bill Moggridge 2007. Designing Interactions. The MIT Press

    2007

  4. [4]

    Birgmeier, J. et al. 2021. AMELIE 3: Fully Automated Mendelian Patient Reanalysis at Under 1 Alert per Patient per Year. medRxiv

    2021

  5. [5]

    and Churchill, E.F

    Bly, S. and Churchill, E.F. 1999. Design through matchmaking: technology in search of users. Interactions. 6, 2 (Mar. 1999), 23–31. DOI:https://doi.org/10.1145/296165.296174

    work page arXiv 1999

  6. [6]

    and Wang, H

    Bond, R.R., Mulvenna, M. and Wang, H. 2019. Human Centered Artificial Intelligence: Weaving UX into Algorithmic Decision Making. (2019)

    2019

  7. [7]

    Brown, T. et al. 2020. Language Models are Few -Shot Learners. Advances in Neural Information Processing Systems. 33, (2020), 1877–1901

    2020

  8. [8]

    and Zhang, Y

    Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y.T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M.T. and Zhang, Y. 2023. Sparks of Artificial General Intelligence: Early experiments with GPT-4. arXiv

    2023

Show all 101 references
  1. [9]

    and Hussmann, H

    Buschek, D., Eiband, M. and Hussmann, H. 2022. How to Support Users in Understanding Intelligent Systems? An Analysis and Conceptual Framework of User Questions Considering User Mindsets, Involvement, and Knowledge Outcomes. ACM Transactions on Interactive Intelligent Systems ...

    2022 doi

  2. [10]

    Buxton, B. 2010. Sketching User Experiences: Getting the Design Right and the Right Design. Morgan Kaufmann

    2010

  3. [11]

    and Terry, M

    Cai, C.J., Reif, E., Hegde, N., Hipp, J., Kim, B., Smilkov, D., Wattenberg, M., Viegas, F., Corrado, G.S., Stumpe, M.C. and Terry, M. 2019. Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision - Making. Proceedings of the 2019 CHI Conference on Hum...

    2019

  4. [12]

    and Brereton, M

    Capel, T. and Brereton, M. 2023. What is Human-Centered about Human-Centered AI? A Map of the Research Landscape. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg Germany, Apr. 2023), 1–23

    2023

  5. [13]

    and Weld, D.S

    Chang, J.C., Zhang, A.X., Bragg, J., Head, A., Lo, K., Downey, D. and Weld, D.S. 2023. CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (New York...

    2023

  6. [14]

    and Wu, Z.-Y

    Chen, L., Chen, D.-F., Dong, H.-L., Liu, G.-L. and Wu, Z.-Y. 2021. A novel frameshift ACTN2 variant causes a rare adult-onset distal myopathy with multi-minicores. CNS neuroscience & therapeutics. 27, 10 (Oct. 2021), 1198–

    2021

  7. [15]

    It would work for me too

    Cheng, R., Wang, R., Zimmermann, T. and Ford, D. 2024. “It would work for me too”: How Online Communities Shape Software Developers’ Trust in AI -Powered Code Generation Tools. ACM Transactions on Interactive Intelligent Systems. (Mar. 2024). DOI:https://doi.org/10.1145/3651990

    2024 doi

  8. [16]

    and Lee, E

    Cho, J. and Lee, E. -H. 2014. Reducing Confusion about Grounded Theory and Qualitative Content Analysis: Similarities and Differences. The Qualitative Report . 19, 32 (Aug. 2014), 1 –20. DOI:https://doi.org/10.46743/2160-3715/2014.1028

    2014

  9. [17]

    and Wilcox, L

    Cooper, N., Horne, T., Hayes, G., Heldreth, C., Lahav, M., Holbrook, J.S. and Wilcox, L. 2022. A Systematic Review and Thematic Analysis of Community -Collaborative Approaches to Computing Research. CHI Conference on Human Factors in Computing Systems (Apr. 2022), 1–18

    2022

  10. [18]

    and Phan, T.G

    Dai, P., Honda, A., Ewans, L., McGaughran, J., Burnett, L., Law, M. and Phan, T.G. 2022. Recommendations for next generation sequencing data reanalysis of unsolved cases with suspected Mendelian disorders: A systematic review and meta -analysis. Genetics in Medicine . 24, 8 (A...

    2022 doi

  11. [19]

    De La Vega, F.M. et al. 2021. Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases. Genome Medicine . 13, 1 (Oct. 2021), 153. DOI:https://doi.org/10.1186/s13073-021-00965-0

    2021 doi

  12. [20]

    Di Sera, T. et al. 2021. Gene.iobio: an interactive web tool for versatile, clinically-driven variant interrogation and prioritization. Scientific Reports . 11, 1 (Oct. 2021), 20307. DOI:https://doi.org/10.1038/s41598 -021- 99752-5

    2021 doi

  13. [21]

    DiStefano, M.T. et al. 2022. The Gene Curation Coalition: A global effort to harmonize gene–disease evidence resources. Genetics in Medicine . 24, 8 (Aug. 2022), 1732 –1742. DOI:https://doi.org/10.1016/j.gim.2022.04.017

    2022 doi

  14. [22]

    and Evelo, C.T

    Ehrhart, F., Willighagen, E.L., Kutmon, M., van Hoften, M., Curfs, L.M.G. and Evelo, C.T. 2021. A resource to explore the discovery of rare diseases and their causative genes. Scientific Data . 8, 1 (May 2021), 124. DOI:https://doi.org/10.1038/s41597-021-00905-y

    2021 doi

  15. [23]

    Endsley, M. 2016. From Here to Autonomy: Lessons Learned From Human –Automation Research. Human Factors. 59, (Dec. 2016), 001872081668135. DOI:https://doi.org/10.1177/0018720816681350

    2016 doi

  16. [24]

    Fischer, J.E. 2023. Generative AI Considered Harmful. Proceedings of the 5th International Conference on Conversational User Interfaces (Eindhoven Netherlands, Jul. 2023), 1–5

    2023

  17. [25]

    and Kittur, A

    Fisher, K., Counts, S. and Kittur, A. 2012. Distributed sensemaking: improving sensemaking by leveraging the efforts of previous users. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Austin Texas USA, May 2012), 247–256

    2012

  18. [26]

    and Siu, A.F

    Fok, R., Lipka, N., Sun, T. and Siu, A.F. 2024. Marco: Supporting Business Document Workflows via Collection- Centric Information Foraging with Large Language Models. Proceedings of the CHI Conference on Human Factors in Computing Systems (Honolulu HI USA, May 2024), 1–20

    2024

  19. [27]

    and Firth, H.V

    Foreman, J., Perrett, D., Mazaika, E., Hunt, S.E., Ware, J.S. and Firth, H.V. 2023. DECIPHER: Improving Genetic Diagnosis Through Dynamic Integration of Genomic and Clinical Data. Annual Review of Genomics and Human Genetics. 24, Volume 24, 2023 (Aug. 2023), 151–176. DOI:https...

    2023 doi

  20. [28]

    and Fussell, S.R

    Goyal, N. and Fussell, S.R. 2016. Effects of Sensemaking Translucence on Distributed Collaborative Analysis. Proceedings of the 19th ACM Conference on Computer -Supported Cooperative Work & Social Computing (New York, NY, USA, Feb. 2016), 288–302

    2016

  21. [29]

    and Wang, Y

    Guo, M., Zhou, Z., Gotz, D. and Wang, Y. 2023. GRAFS: Graphical Faceted Search System to Support Conceptual Understanding in Exploratory Search. ACM Transactions on Interactive Intelligent Systems. 13, 2 (Jun. 2023), 1–36. DOI:https://doi.org/10.1145/3588319

    2023 doi

  22. [30]

    Hamosh, A. 2004. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Research . 33, Database issue (Dec. 2004), D514 –D517. DOI:https://doi.org/10.1093/nar/gki033

    2004 doi

  23. [31]

    and Beaudouin -Lafon, M

    Han, H.L., Yu, J., Bournet, R., Ciorascu, A., Mackay, W.E. and Beaudouin -Lafon, M. 2022. Passages: Interacting with Text Across Documents. CHI Conference on Human Factors in Computing Systems (New Orleans LA USA, Apr. 2022), 1–17

    2022

  24. [32]

    Hearst, M.A. 1992. Automatic acquisition of hyponyms from large text corpora. Proceedings of the 14th conference on Computational linguistics - Volume 2 (USA, Aug. 1992), 539–545

    1992

  25. [33]

    and Obstfeld, D

    Hernes, T. and Obstfeld, D. 2022. A Temporal Narrative View of Sensemaking. Organization Theory. 3, 4 (Oct. 2022), 26317877221131585. DOI:https://doi.org/10.1177/26317877221131585

    2022 doi

  26. [34]

    and Nishino, I

    Inoue, M., Noguchi, S., Sonehara, K., Nakamura-Shindo, K., Taniguchi, A., Kajikawa, H., Nakamura, H., Ishikawa, K., Ogawa, M., Hayashi, S., Okada, Y., Kuru, S., Iida, A. and Nishino, I. 2021. A recurrent homozygous ACTN2 variant associated with core m yopathy. Acta Neuropathol...

    2021 doi

  27. [35]

    and Paris, C

    Irons, J., Mason, C., Cooper, P., Sidra, S., Reeson, A. and Paris, C. 2023. Exploring the Impacts of ChatGPT on Future Scientific Work. OSF

    2023

  28. [36]

    and Fourney, A

    Jahanbakhsh, F., Nouri, E., Sim, R., White, R.W. and Fourney, A. 2022. Understanding Questions that Arise When Working with Business Documents. Proceedings of the ACM on Human-Computer Interaction. 6, CSCW2 (Nov. 2022), 1–24. DOI:https://doi.org/10.1145/3555761

    2022 doi

  29. [37]

    and Xia, H

    Jiang, P., Rayan, J., Dow, S.P. and Xia, H. 2023. Graphologue: Exploring Large Language Model Responses with Interactive Diagrams. Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (Oct. 2023), 1–20

    2023

  30. [38]

    and Neustaedter, C

    Jones, B., Tang, A. and Neustaedter, C. 2020. Remote Communication in Wilderness Search and Rescue: Implications for the Design of Emergency Distributed -Collaboration Tools for Network -Sparse Environments. Proceedings of the ACM on Human -Computer Interaction . 4, GROUP (Jan...

    2020 doi

  31. [39]

    and Kittur, A

    Kang, H., Chang, J.C., Kim, Y. and Kittur, A. 2022. Threddy: An Interactive System for Personalized Thread - based Exploration and Organization of Scientific Literature. Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology (Bend OR USA, Oct. 2...

    2022

  32. [40]

    and Kittur, A

    Kang, H.B., Wu, S.T., Chang, J.C. and Kittur, A. 2023. Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking. Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (Oct. 2023), 1–19

    2023

  33. [41]

    and Barling, J

    Kelloway, E.K. and Barling, J. 2000. Knowledge work as organizational behavior. International Journal of Management Reviews. 2, 3 (2000), 287–304. DOI:https://doi.org/10.1111/1468-2370.00042

    2000

  34. [42]

    Kidd, A. 1994. The marks are on the knowledge worker. Proceedings of the SIGCHI conference on Human factors in computing systems celebrating interdependence - CHI ’94 (Boston, Massachusetts, United States, 1994), 186–191

    1994

  35. [43]

    and Bove, M.R

    Kittur, A., Peters, A.M., Diriye, A., Telang, T. and Bove, M.R. 2013. Costs and benefits of structured information foraging. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (New York, NY, USA, Apr. 2013), 2989–2998

    2013

  36. [44]

    Landrum, M.J. et al. 2018. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Research . 46, Database issue (Jan. 2018), D1062 –D1067. DOI:https://doi.org/10.1093/nar/gkx1153

    2018 doi

  37. [45]

    Lassmann, T. et al. 2020. A flexible computational pipeline for research analyses of unsolved clinical exome cases. npj Genomic Medicine. 5, 1 (Dec. 2020), 1–11. DOI:https://doi.org/10.1038/s41525-020-00161-w. 25

    2020 doi

  38. [46]

    and Myers, B.A

    Liu, M.X., Hsieh, J., Hahn, N., Zhou, A., Deng, E., Burley, S., Taylor, C., Kittur, A. and Myers, B.A. 2019. Unakite: Scaffolding Developers’ Decision-Making Using the Web. Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology (New Orleans LA U...

    2019

  39. [47]

    and Myers, B.A

    Liu, M.X., Kittur, A. and Myers, B.A. 2022. Crystalline: Lowering the Cost for Developers to Collect and Organize Information for Decision Making. CHI Conference on Human Factors in Computing Systems (New Orleans LA USA, Apr. 2022), 1–16

    2022

  40. [48]

    and Myers, B.A

    Liu, M.X., Kittur, A. and Myers, B.A. 2021. To Reuse or Not To Reuse?: A Framework and System for Evaluating Summarized Knowledge. Proceedings of the ACM on Human-Computer Interaction. 5, CSCW1 (Apr. 2021), 1–

    2021

  41. [49]

    and Myers, B.A

    Liu, M.X., Wu, T., Chen, T., Li, F.M., Kittur, A. and Myers, B.A. 2024. Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Models. Proceedings of the CHI Conference on Human Factors in Computing Systems (Honolulu HI USA, May 2024), 1–26

    2024

  42. [50]

    DOI:https://doi.org/10.1145/3449240

    doi

  43. [51]

    Multiple structured Core Disease

    Lornage, X. et al. 2019. ACTN2 mutations cause “Multiple structured Core Disease” (MsCD). Acta Neuropathologica. 137, 3 (Mar. 2019), 501–519. DOI:https://doi.org/10.1007/s00401-019-01963-8

    2019 doi

  44. [52]

    Liu, P. et al. 2019. Reanalysis of Clinical Exome Sequencing Data. New England Journal of Medicine . 380, 25 (Jun. 2019), 2478–2480. DOI:https://doi.org/10.1056/NEJMc1812033

    2019 doi

  45. [53]

    Lucero, A. 2015. Using Affinity Diagrams to Evaluate Interactive Prototypes. Human-Computer Interaction – INTERACT 2015 (Cham, 2015), 231–248

    2015

  46. [54]

    and Tan, C

    Lubars, B. and Tan, C. 2019. Ask not what AI can do, but what AI should do: towards a framework of task delegability. Proceedings of the 33rd International Conference on Neural Information Processing Systems . Curran Associates Inc. 57–67

    2019

  47. [55]

    and Siek, K

    MacLeod, H., Oakes, K., Geisler, D., Connelly, K. and Siek, K. 2015. Rare World: Towards Technology for Rare Diseases. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (New York, NY, USA, Apr. 2015), 1145–1154

    2015

  48. [56]

    Be Grateful You Don’t Have a Real Disease

    MacLeod, H., Bastin, G., Liu, L.S., Siek, K. and Connelly, K. 2017. “Be Grateful You Don’t Have a Real Disease”: Understanding Rare Disease Relationships. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (New York, NY, USA, May 2017), 1660–1673

    2017

  49. [57]

    Matalonga, L. et al. 2021. Solving patients with rare diseases through programmatic reanalysis of genome - phenome data. European journal of human genetics: EJHG . 29, 9 (Sep. 2021), 1337 –1347. DOI:https://doi.org/10.1038/s41431-021-00852-7

    2021 doi

  50. [58]

    Marshall, C.R. et al. 2020. Best practices for the analytical validation of clinical whole -genome sequencing intended for the diagnosis of germline disease. npj Genomic Medicine . 5, 1 (Oct. 2020), 1 –12. DOI:https://doi.org/10.1038/s41525-020-00154-9

    2020 doi

  51. [59]

    and Rath, A

    Nguengang Wakap, S., Lambert, D.M., Olry, A., Rodwell, C., Gueydan, C., Lanneau, V., Murphy, D., Le Cam, Y. and Rath, A. 2020. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. European Journal of Human Genetics . 28, 2 (Feb. 2020), 16...

    2020 doi

  52. [60]

    and Cunningham, F

    McLaren, W., Gil, L., Hunt, S.E., Riat, H.S., Ritchie, G.R.S., Thormann, A., Flicek, P. and Cunningham, F. 2016. The Ensembl Variant Effect Predictor. Genome Biology . 17, 1 (Jun. 2016), 122. DOI:https://doi.org/10.1186/s13059-016-0974-4

    2016 doi

  53. [61]

    Pais, L.S. et al. 2022. seqr: A web -based analysis and collaboration tool for rare disease genomics. Human Mutation. 43, 6 (2022), 698–707. DOI:https://doi.org/10.1002/humu.24366

    2022 doi

  54. [62]

    and Dix, A

    Nielsen, E.E., Owen, T., Roach, M. and Dix, A. 2023. A Patient Centred Approach to Rare Disease Technology. Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg Germany, Apr. 2023), 1–7

    2023

  55. [63]

    and Kim, J

    Park, J., Min, B., Ma, X. and Kim, J. 2023. ChoiceMates: Supporting Unfamiliar Online Decision -Making with Multi-Agent Conversational Interactions. arXiv

    2023

  56. [64]

    and Chang, J.C

    Palani, S., Naik, A., Downey, D., Zhang, A.X., Bragg, J. and Chang, J.C. 2023. Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (New York, NY, USA, Apr. 2023), 1–20

    2023

  57. [65]

    Philippakis, A.A. et al. 2015. The Matchmaker Exchange: A Platform for Rare Disease Gene Discovery. Human Mutation. 36, 10 (2015), 915–921. DOI:https://doi.org/10.1002/humu.22858

    2015 doi

  58. [66]

    and Reddy, M.C

    Paul, S.A. and Reddy, M.C. 2010. Understanding together: sensemaking in collaborative information seeking. Proceedings of the 2010 ACM conference on Computer supported cooperative work (New York, NY, USA, Feb. 2010), 321–330. 26

    2010

  59. [67]

    and Luker, P

    Popolov, D., Callaghan, M. and Luker, P. 2000. Conversation space: visualising multi -threaded conversation. Proceedings of the working conference on Advanced visual interfaces (New York, NY, USA, May 2000), 246 – 249

    2000

  60. [68]

    and Card, S

    Pirolli, P. and Card, S. 2005. The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. Proceedings of International Conference on Intelligence Analysis (Jan. 2005)

    2005

  61. [69]

    and Rehm, H.L

    Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier -Foster, J., Grody, W.W., Hegde, M., Lyon, E., Spector, E., Voelkerding, K. and Rehm, H.L. 2015. Standards and guidelines for the interpretation of sequence variants: a joint consensus recomm endation of the American...

    2015 doi

  62. [70]

    and Meek, C

    Rachatasumrit, N., Ramos, G., Suh, J., Ng, R. and Meek, C. 2021. ForSense: Accelerating Online Research Through Sensemaking Integration and Machine Research Support. 26th International Conference on Intelligent User Interfaces (New York, NY, USA, Apr. 2021), 608–618

    2021

  63. [71]

    and Card, S.K

    Russell, D.M., Stefik, M.J., Pirolli, P. and Card, S.K. 1993. The cost structure of sensemaking. Proceedings of the INTERACT ’93 and CHI ’93 Conference on Human Factors in Computing Systems (New York, NY, USA, May 1993), 269–276

    1993

  64. [72]

    Riedl, M.O. 2019. Human -centered artificial intelligence and machine learning. Human Behavior and Emerging Technologies. 1, 1 (2019), 33–36. DOI:https://doi.org/10.1002/hbe2.117

    2019 doi

  65. [73]

    and Stappers, P.J

    Sanders, E.B.-N. and Stappers, P.J. 2008. Co -creation and the new landscapes of design. CoDesign. 4, 1 (Mar. 2008), 5–18. DOI:https://doi.org/10.1080/15710880701875068

    2008 doi

  66. [74]

    Sako, M. 2024. How Generative AI Fits into Knowledge Work. Communications of the ACM. 67, 4 (Apr. 2024), 20–22. DOI:https://doi.org/10.1145/3638567

    2024 doi

  67. [75]

    and Udd, B

    Savarese, M., Palmio, J., Poza, J.J., Weinberg, J., Olive, M., Cobo, A.M., Vihola, A., Jonson, P.H., Sarparanta, J., García-Bragado, F., Urtizberea, J.A., Hackman, P. and Udd, B. 2019. Actininopathy: A new muscular dystrophy caused by ACTN2 dominant m utations. Annals of Neuro...

    2019 doi

  68. [76]

    Savarese, M. et al. 2021. Out -of-Frame Mutations in ACTN2 Last Exon Cause a Dominant Distal Myopathy With Facial Weakness. Neurology. Genetics . 7, 5 (Oct. 2021), e619. DOI:https://doi.org/10.1212/NXG.0000000000000619

    2021 doi

  69. [77]

    Schellaert, W., Martínez -Plumed, F., Vold, K., Burden, J., A. M. Casares, P., Sheng Loe, B., Reichart, R., Ó hÉigeartaigh, S., Korhonen, A. and Hernández-Orallo, J. 2023. Your Prompt is My Command: On Assessing the Human-Centred Generality of Multimodal Models. Journal of Art...

    2023 doi

  70. [78]

    Sawyer, S. l. et al. 2016. Utility of whole -exome sequencing for those near the end of the diagnostic odyssey: time to address gaps in care. Clinical Genetics . 89, 3 (2016), 275 –284. DOI:https://doi.org/10.1111/cge.12654

    2016 doi

  71. [79]

    and Cooper, D.N

    Stenson, P.D., Mort, M., Ball, E.V., Chapman, M., Evans, K., Azevedo, L., Hayden, M., Heywood, S., Millar, D.S., Phillips, A.D. and Cooper, D.N. 2020. The Human Gene Mutation Database (HGMD®): optimizing its use in a clinical diagnostic or research se tting. Human Genetics . 1...

    2020 doi

  72. [80]

    and Ball, M

    Shaer, O., Nov, O., Okerlund, J., Balestra, M., Stowell, E., Westendorf, L., Pollalis, C., Davis, J., Westort, L. and Ball, M. 2016. GenomiX: A Novel Interaction Tool for Self-Exploration of Personal Genomic Data. Proceedings of the 2016 CHI Conference on Human Factors in Comp...

    2016

  73. [81]

    and Xia, H

    Suh, S., Min, B., Palani, S. and Xia, H. 2023. Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language Models. To appear in Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST) (Oct. 2023). 27

    2023

  74. [82]

    and Kientz, J.A

    Suh, H., Dangol, A., Meadan, H., Miller, C.A. and Kientz, J.A. 2024. Opportunities and Challenges for AI -Based Support for Speech -Language Pathologists. Proceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work (Newcastle upon Tyne United ...

    2024

  75. [83]

    and Lindstrand, A

    Tesi, B., Boileau, C., Boycott, K.M., Canaud, G., Caulfield, M., Choukair, D., Hill, S., Spielmann, M., Wedell, A., Wirta, V., Nordgren, A. and Lindstrand, A. Precision medicine in rare diseases: What is next? Journal of Internal Medicine. n/a, n/a. DOI:https://doi.org/10.1111...

    doi

  76. [84]

    and Miles, J.N

    Svikhnushina, E., Schellenberg, M., Niedbala, A.K., Barisic, I. and Miles, J.N. 2023. Expectation vs Reality in Users’ Willingness to Delegate to Digital Assistants. Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg Germany, Apr. 2023), 1–7

    2023

  77. [85]

    Vakkari, P. 2016. Searching as learning: A systematization based on literature. Journal of Information Science. 42, 1 (Feb. 2016), 7–18. DOI:https://doi.org/10.1177/0165551515615833

    2016 doi

  78. [86]

    -A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E

    Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M. -A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E. and Lample, G. 2023. LLaMA: Open and Efficient Foundation Language Models. arXiv

    2023

  79. [87]

    and Obstfeld, D

    Weick, K.E., Sutcliffe, K.M. and Obstfeld, D. 2005. Organizing and the process of sensemaking. Organization Science. 16, 4 (Jul. 2005), 409–422

    2005

  80. [88]

    and Chilana, P.K

    Vermette, L., Dembla, S., Wang, A.Y., McGrenere, J. and Chilana, P.K. 2017. Social CheatSheet: An Interactive Community-Curated Information Overlay for Web Applications. Proceedings of the ACM on Human-Computer Interaction. 1, CSCW (Dec. 2017), 1–19. DOI:https://doi.org/10.114...

    2017 doi

  81. [89]

    and Poon, H

    Wong, C., Zhang, S., Gu, Y., Moung, C., Abel, J., Usuyama, N., Weerasinghe, R., Piening, B., Naumann, T., Bifulco, C. and Poon, H. 2023. Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology. arXiv

    2023

  82. [90]

    Weiner, J. 2021. Project Phases and Common Project Pitfalls. Why AI/Data Science Projects Fail: How to Avoid Project Pitfalls. J. Weiner, ed. Springer International Publishing. 5–11

    2021

  83. [91]

    Xu, W. 2019. Toward human-centered AI: a perspective from human-computer interaction. Interactions. 26, 4 (Jun. 2019), 42–46. DOI:https://doi.org/10.1145/3328485

    2019 doi

  84. [92]

    and Wilcox, L

    Woodruff, A., Shelby, R., Kelley, P.G., Rousso-Schindler, S., Smith-Loud, J. and Wilcox, L. 2024. How Knowledge Workers Think Generative AI Will (Not) Transform Their Industries. Proceedings of the CHI Conference on Human Factors in Computing Systems (Honolulu HI USA, May 2024), 1–26

    2024

  85. [93]

    and Zimmerman, J

    Yang, Q., Steinfeld, A., Rosé, C. and Zimmerman, J. 2020. Re -examining Whether, Why, and How Human -AI Interaction Is Uniquely Difficult to Design. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu HI USA, Apr. 2020), 1–13

    2020

  86. [94]

    and Wang, F

    Yang, Q., Hao, Y., Quan, K., Yang, S., Zhao, Y., Kuleshov, V. and Wang, F. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (New York, NY, USA, Apr...

    2023

  87. [95]

    Yildirim, N. et al. 2024. Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit. Proceedings of the CHI Conference on Human Factors in Computing Systems (Honolulu HI USA, May 2024), 1–18

    2024

  88. [96]

    Yildirim, N. et al. 2024. Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision - Language Applications for Radiology. Proceedings of the CHI Conference on Human Factors in Computing Systems (Honolulu HI USA, May 2024), 1–22

    2024

  89. [97]

    It depends

    Zając, H.D., Ribeiro, J.M.N., Ingala, S., Gentile, S., Wanjohi, R., Gitau, S.N., Carlsen, J.F., Nielsen, M.B. and Andersen, T.O. 2024. “It depends”: Configuring AI to Improve Clinical Usefulness Across Contexts. Designing Interactive Systems Conference (IT University of Copenh...

    2024

  90. [98]

    and Zimmerman, J

    Yildirim, N., Oh, C., Sayar, D., Brand, K., Challa, S., Turri, V., Crosby Walton, N., Wong, A.E., Forlizzi, J., McCann, J. and Zimmerman, J. 2023. Creating Design Resources to Scaffold the Ideation of AI Concepts. Proceedings of the 2023 ACM Designing Interactive Systems Confe...

    2023

  91. [99]

    and Song, P

    Zhang, X., Qu, Y., Giles, C.L. and Song, P. 2008. CiteSense: supporting sensemaking of research literature. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (New York, NY, USA, Apr. 2008), 677–680. 28 A APPENDICES A.1 Participant sketch of an AI assis...

    2008

  92. [100]

    and Karger, D

    Zhang, A.X., Verou, L. and Karger, D. 2017. Wikum: Bridging Discussion Forums and Wikis Using Recursive Summarization. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (New York, NY, USA, Feb. 2017), 2082–2096

    2017

  93. [1205]

    DOI:https://doi.org/10.1111/cns.13697

    doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.