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# Effects of AI on Radiological Reading Activity

Olivier Ocquidant, Christian Licoppe, Gérald Gaglio

► To cite this version:

Olivier Ocquidant, Christian Licoppe, Gérald Gaglio. Effects of AI on Radiological Reading Activity. Engineering For Health Annual Forum, Nov 2025, Paris Saclay, France. hal-05413740

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https://hal.science/hal-05413740v1

Submitted on 16 Dec 2025

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Distributed under a Creative Commons Attribution 4.0 International License
## Authors

**Olivier Ocquidant**, Post Doctoral Researcher in Sociology at Telecom IP Paris.

**Christian Licoppe**, Sociology Professor at Telecom Paris and Director of the Department of Social Sciences and Management at IP-Paris.

**Gérald Gaglio**, Sociology Professor at University Côte d'Azur, Co-director of the INNOS team at GREDEG.

## Methodology

### THEORETICAL FRAMEWORK AND METHOD

**A Qualitative Micro Analysis of How AI is actually used in mammography**

Ethnomethodological investigation, **video analysis of local epistemic ecologies** (Goodwin 2016)

**A socio-technical and organizational comprehension of AI in Mammography** (Akrich, 2003; Gaglio, forthcoming).

### DATA

Radiological Cases, n = 84 in 5 Different Contexts.

**Video captures** of reading mammograms activity (radiological interpretation) – 17h.

**Voicing aloud interviews** about working – ongoing activity explained and voiced – 24h.

**Integral interviews transcripts** – 592p

2 Cases reviews transcripts from trading prospects. Technical and trade documents, Fieldwork notes, photos.

### TOWARD A PRAXEOLOGICAL INQUIRY

Sequencing radiologists' activity into episodes, procedures, pragmatic and interactional achievements.

Classification of findings by variables (expertise level, type of organization, habituation to AI, types of reading, types of integration of AI, types of interaction with tools and screens, types of gazes, gestures).

<figure><img src="image_3.png" /><figcaption>1. Radiologist dictating a report</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>2. Radiologist reading mammograms</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>3. AI Advice at Elevated Risk</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. I: AI acknowledgment moments</figcaption></figure>

## Findings to date

### 3 TYPES OF READING MAMMOGRAMS WITH AI

#### 1 - Separated Reading

AI Immediately put to the test of Professional Vision (Goodwin, 1994).

Expert knowledge and high confidence, but low reflexivity induced by AI (Abdel Karim et al, 2023)

#### 2 - Intertwined Reading

Enaction of 'AI interrogation practices' (Lebovitz et al, 2022), AI relevancy deferred until the end of examination.

Expert knowledge and high reflexivity induced by AI, but complexity of the decision making process 'weight the pro and cons'.

#### 3 - Merged Reading

Early AI acknowledgment in the reading process

High confidence in AI, moderate radiologists' self confidence (Jussupow et al, 2022).

AI delegation accelerating and alleviating reading process, but high dependency on AI accuracy. AI as diagnosis co-author.

### WHEN AI IS ACKNOWLEDGED IN THE RADIOLOGICAL EXAMINATION PROCESS – Figure I

**A** - AI Acknowledged after Radiologist's Reading Achievement – type of reading **1 & 2**
Normal and tacit order of AI acknowledgment

**B** - AI Acknowledged at the beginning of Radiologist's Reading – type of reading **3**
Unmentioned but indeed practiced way of reading, by Generalist Radiologists.

**C** - Late AI Acknowledgment mostly for Technical Reasons (loading time) - incidental
AI put at the edge of the reading process, expediting its Checking Procedures.

## Perspectives in Development

<figure><img src="image_7.png" /><figcaption>Fig. II: Forms of Reading</figcaption></figure>

### PRAXEOLOGICAL MODALITIES OF HUMAN-AI INTERACTION (Goodwin, 2016) – Figure II

- Language – in voicing aloud interviews (inquiring, explaining, arguing)

- Types of Gazes implied (scanning gaze, focus gaze, perlustrating gaze)

- Gestures (pointing, contouring, arguing)

### AI INTEGRATION MAIN PROCEDURE – Figure III

- Type I, Putting to the test AI advice by Pattern Recognition as far as possible – a longer detection, a shorter characterization

- Type II, Constructing Case's Relevance as a "diagnosis intrigue" (Saunders, 2008) including AI in it – a longer characterization

- Type III, Co-constructing Relevance with AI and repairing Radiologist's accountability if necessary – Recursion and fusion of Detection and Characterization.

### MAPPING THE ORGANIZATION OF SOCIO-TECHNICAL NETWORKS AROUND AI IN MAMMOGRAPHY – Figure IV

<figure><img src="image_8.png" /><figcaption>Fig. IV: Configuration Work Around AI in Mammography</figcaption></figure>

<figure><img src="image_9.png" /></figure>

### References

Abdel-Karim, Benjamin, Nicolas Pfeuffer, K. Valerie Carl, Oliver Hinz. 2023. « How AI-Based Systems Can Induce Reflections: The Case of AI-Augmented Diagnostic Work ». *MIS Quarterly* 47(4): 1395-1424.

Akrich, Madeleine. 2003. « La construction d'un système socio-technique. Esquisse pour une anthropologie des techniques ». *Anthropologie et Sociétés* 13(2): 31-54.

Gaglio, Gérald. Forthcoming. « Remplacer-déclasser-déplacer: trois polarités pour comprendre la dynamique d'adoption des IA en radiologie ». *Goodwin, Charles*. 1994. « Professional Vision ». *American Anthropologist* 96(3): 606-33.

Goodwin, Charles. 2016. « L'organisation co-opérative et transformative de l'action et du savoir humains ». *Tracés* 16: 19-46.

Jussupow, Ekaterina, Kai Spohrer, et Armin Heinzl. 2022. « Radiologists' Usage of Diagnostic AI Systems: The Role of Diagnostic Self-Efficacy for Sensemaking from Confirmation and Disconfirmation ». *Business & Information Systems Engineering* 64(3): 293-309.

Lebovitz, Sarah, Hila Lifshitz-Assaf, et Natalia Levina. 2022. « To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis ». *Organization Science* 33(1): 126-48.

Saunders, Barry. 2008. *CT Suite: The work of diagnosis in the age of non invasive cutting*. Durham, London. Duke university Press.

Thomassin Naggara, Isabelle, Luc Ceugnart, Marie-France Bellin, Alain Luciani. 2024. « En mammographie, l'IA doit trouver sa place ». *Tribune*, Le Monde, Sciences & Médecine 3 avril 2024: 7.