- Tomorrow's Dose
- Posts
- Edition 11 - AI in Dentistry and Cancer Care: Breaking Barriers
Edition 11 - AI in Dentistry and Cancer Care: Breaking Barriers
Dive into AI's role in transforming dentistry, uncover key lessons in healthcare implementation, and discover how deep learning is revolutionizing cancer imaging.
AI in Dentistry: Challenges and Opportunities
Lessons on AI implementation
Deep learning-based whole-body PSMA PET/CT attenuation correction
Featured follow of the week
Top posts of the week across social
Meet the editor
Want a featured article?
Specialty: Radiology // Sub-Specialty: Dental // Body Site: All
1. AI in Dentistry: Challenges and Opportunities
A survey assessed the perspectives, attitudes and readiness of the dental professionals across India (256 responders). The main barriers to AI adoption were (in rank order): technical concerns, financial considerations, ethical and legal issues, and organisation factors. Despite this, more than 70% of responders had a positive attitude towards AI. Current usage was dominated by diagnostic support (44%) and administrative tasks (82%). However, gaps in familiarity with AI applications like robotic assistance and treatment planning remain. Recommendations include addressing technical challenges, offering targeted training, and creating ethical frameworks to advance AI integration in dental care.
Read Full Article
Paul’s Thoughts:
It is very interesting to see publications 'defending' AI and its ability to be implemented in clinical practice. Evaluating AI performance in everyday practice is highly important, and inevitably there will be some pushback from staff groups that are concerned about their positions. As Dr Bennett argues, however, utilisation of these innovations, in their current form, is for patient triaging and workflow optimisation and efforts should be directed towards understanding how such tools can aid reporting radiologists rather than replace them.
Timescale: Early | 2 Years
Specialty: All // Sub-Specialty: AI // Body Site: All
2. Lessons on AI implementation
The article examines AI adoption in UK medical imaging and radiotherapy, highlighting benefits like workflow optimization, workforce development, and improved patient care. Challenges include data governance, workforce changes, and integration issues. Recommendations are focused around tailored training, robust governance, and multidisciplinary collaboration to ensure ethical, patient-centered, and effective AI implementation.
Read Full Article
Paul’s Thoughts:
The article highlights AI’s transformative potential in medical imaging and radiotherapy. Its capacity to streamline workflows, enhance diagnostic accuracy, and support decision-making could address workforce shortages and improve patient outcomes. However, AI’s integration also risks altering traditional roles, potentially reducing human autonomy and patient interaction. A critical impact area is workforce evolution. While AI may upskill professionals, reliance on automation could lead to deskilling. Furthermore, its influence on patient care raises concerns about preserving empathy and trust. The article’s emphasis on governance and education reflects the broader necessity of preparing healthcare systems for sustainable AI adoption. By fostering interdisciplinary collaboration, continuous training, and ethical oversight, AI’s impact can be harnessed positively, ensuring it enhances, rather than disrupts, the human elements of care.
Timescale: Acute | 1 Year
Specialty: Nuclear Medicine // Sub-Specialty: AI // Body Site: All
3. Deep learning-based whole-body PSMA PET/CT attenuation correction
A generative adversarial network (GAN) architecture was developed from paired AC-PET and NAC-PET images. 18F-DCFPyL PSMA (prostate-specific membrane antigen) PET-CT studies from 302 prostate cancer patients, split into training, validation, and testing cohorts found that quantitative markers and image quality could be preserved when using the Pix-2-Pix GAN model, without the need for a CT scan for attenuation correction.
Read Full Article
Paul’s Thoughts:
AI-generated PET images without the need for CT scans for attenuation correction is a big development for the field. Being able to limit patient dose, especially for a PET scan that covers the entire body, is always desirable. It remains to be seen whether such approaches can be truly relied upon and whether any of the major vendors will implement it in their platforms.
Timescale: Early | 3 Years
A round-up of some of the best posts we found online this week.
Was this email forwarded to you?
Our weekly email brings you the latest health trends and insights, combining top news and opinions into a straightforward, digestible format.
 |  |  |
Want an article featured?
Have an insightful link or story about the future of medical health? Reach out below, and we may include it in a future release.
Reply