Leveraging Generative AI in Enhancing Product Owner Responsibilities in the Post-Market Phase of Medical Device Software
Notice bibliographique
Résumé
Patients can die from using a medical device if it is not closely monitored after its release into the market. Post-market surveillance (PMS) was introduced by regulators as a requirement for medical device manufacturers to continuously monitor the performance and safety of their devices while they are in the market. However, medical device manufacturers face several challenges in the PMS phase of their devices. But it remained unclear what these challenges are and little research has explored the challenges that device manufacturers face in post-market surveillance. Therefore, the aim of this thesis is to identify the activities carried out by device manufacturers in the post-market surveillance phase and understand the challenges they encounter while monitoring the devices in the market. This thesis also aims to map post-market surveillance activities to scrum product owner responsibilities and suggest ways of using generative AI to simplify the post-market surveillance process. Semi-structured interviews were conducted with four industry professionals who currently have devices in post-market surveillance phase. The study was focused on the European Union with all participants from the European Union. The data collected from these interviews were thematically analysed using Claude 3.5 Sonnet with structured prompts to reveal themes. The study revealed some key challenges in post-market surveillance, including data management and feedback analysis. Currently, there are complexities in managing large amounts of data such as customer feedback, generated during post-market surveillance. These challenges hinder the efficiency of a feedback-driven decision-making process which is crucial for continuous improvement of medical devices. Another crucial challenge was with limited resources, particularly for smaller medical device manufacturers. The inability of manufacturers to sometimes conduct post-market clinical follow-up studies based on customer feedback could hinder the product’s improvement, market expansion, and affect the long-term success of the device. It was also found that generative AI can be potentially used to automate initial feedback processing which could significantly improve the efficiency of categorising and prioritizing feedback. This thesis confirms that generative AI has the potential to improve post-market surveillance of medical devices and offers insights to medical device manufacturers, product owners, project management office roles, regulatory bodies, and AI developers on the application of generative AI in post-market surveillance. Some main limitations of this study include its small sample size of four, its limited scope to the EU, and it only presents a theoretical model.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,028 | 0,072 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,008 |
| Communication savante | 0,007 | 0,010 |
| Science ouverte | 0,003 | 0,010 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».