Tag Archives: ArtificialIntelligence

World Thyroid Day 2025: How AI Is Revolutionizing Thyroid Health

Medical Publishing, Medical Writing, , , , , , , , , , , , ,

World Thyroid Day invites us to appreciate the tiny, butterfly-shaped gland with a big job in managing our body’s energy, growth, and mood. This year, the spotlight shifts to a very 21st-century ally, artificial intelligence (AI), and how it is reshaping the way clinicians detect, treat, and even prevent thyroid disorders such as hypothyroidism, hyperthyroidism, Graves’ disease, Hashimoto’s thyroiditis, thyroid nodules, and thyroid cancer.

 

AI in early detection: Who needs it the most

Machine-learning models trained on millions of electronic-health-record lines and lab reports can now flag people at risk of developing clinical thyroid disease months before the first symptom.1 A 2025 multicenter study built an interpretable ensemble model that accurately stratified heart-failure patients with co-existing thyroid dysfunction for one-year mortality and hospital-admission risk, outperforming conventional scores.2 By linking abnormal thyroid stimulating hormone (TSH) traces with age, comorbidities and medications, the system directs scarce clinic slots to those who need them most.

 

Diagnostic tools rebuilt around AI

Imaging: Deep-learning convolutional networks now review ultrasound videos, color-code suspicious tissue, and even render 3-D nodule maps on-the-fly. A study of 4569 cases found that the 3-Dimensional Total Nitrogen Visualization (3-D ‘TN Vis’) model, which was validated using data from seven hospitals, improved radiologists’ diagnostic accuracy by raising the area under the curve (AUC) from 0.66 to 0.79 and helped junior radiologists perform at the level of their senior colleagues.3 Another 2025 paper reported that “ThyroNet-X4 Genesis,” surpassed mainstream models in classifying sub-centimeter nodules.4

Blood-test interpretation: AI tools digest full thyroid panels – TSH, free T4/T3, antibodies – alongside “hidden” markers hidden in routine blood counts.

Pathology & molecular markers: Computer vision is also assisting cytologists reading fine needle aspiration slides and matching tumor genomes to targeted therapies. The same codebase that spots lung mutations now guide the choice of kinase inhibitors in regenerative medicine trials for recurrent thyroid cancer.

Remote monitoring: Wearables that track heart-rate variability and basal temperature can now feed AI predictors. In a 2023 cohort, resting-heart rate streams from smartwatches predicted thyrotoxic episodes with 86.14% sensitivity and 85.92% specificity, weeks before lab confirmation.5 For patients in rural areas, merging these data with tele-endocrinology portals means rapid dose tweaks without leaving home.

Limitations & Ethical Considerations of AI in Thyroid Care

While AI-driven tools promise faster detection and personalized management, several cautions remain.

  • Most commercial algorithms are trained on imaging and laboratory datasets derived from narrowly defined, often Western, populations. This can skew performance and increase the risk of missed or misclassified disease in underrepresented groups. Precise risk scores may also create false reassurance for low-risk patients or drive unnecessary procedures for those flagged as high-risk—unless clinicians interpret the outputs within the full clinical context.
  • On the privacy front, cloud-based ultrasound archives, wearable sensor feeds and integrated electronic health records aggregate highly sensitive data that must now meet India’s Digital Personal Data Protection (DPDP) Act, Health Insurance Portability and Accountability Act (HIPAA)/ General Data Protection Regulation (GDPR), and other regional regulations; security breaches in healthcare remain among the costliest of any sector.
  • Finally, many AI applications are still classed as software as a medical device and must clear Central Drugs Standard Control Organization (CDSCO) and FDA regulatory pathways before routine clinical use.

Ongoing prospective validation, algorithmic auditing, and explicit human oversight are therefore essential to translate impressive pilot results into safe, equitable real-world care.

Practical ways to observe World Thyroid Day

  1. Check your risk: Women over 30, anyone with autoimmune history, or those previously irradiated should ask for a TSH baseline
  2. Practice iodine-smart nutrition: Use iodized salt; eat seafood or dairy twice a week; discuss selenium or vitamin D status with your clinician
  3. Leverage digital tools: Connect a reputable thyroid-tracking app to your smartwatch and share the trends with your doctor for personalized dose adjustments
  4. Know the warning signs: Persistent fatigue, neck swelling or unexplained weight change warrant a thyroid panel—early self-awareness amplifies AI’s predictive power
  5. Spread the word: Share a factsheet, host a webinar, or post on social media using the hashtags #WorldThyroidDay and #ThyroidAwareness to boost public engagement

 

The road ahead

AI will not replace endocrinologists; it will empower them. Imagine chat-bots that guide newly diagnosed patients through lifestyle tweaks, or ultrasound probes that whisper malignancy scores during a scan. As datasets grow to include genomics, microbiome signals and environmental exposures, guidelines could evolve into continuously learning care paths, delivering precision medicine to the people living with thyroid disease.

On World Thyroid Day 2025, Turacoz celebrates not only the promise of silicon and code but also the power of informed citizens. By pairing innovative AI algorithms with classic public-health wisdom, we can ensure that the next decade marks a turning point in thyroid care.

References:

  1. Németh Á, Tóth G, Fülöp P, et al. Smart medical report: efficient detection of common and rare diseases on common blood tests. Frontiers in Digital Health. 2024 Dec 5;6:1505483.
  2. Iacoviello M, Santamato V, Pagano A, et al. Interpretable AI-driven multi-objective risk prediction in heart-failure patients with thyroid dysfunction. Front Digit Health. 2025;7:1583399.
  3. Zhou Y, Chen C, Yao J, et al. A deep learning-based ultrasound diagnostic tool driven by 3-D visualization of thyroid nodules. NPJ Digit Med. 2025;8:126.
  4. Santos-Silva MA, Sousa N, and Sousa JC. Artificial intelligence in routine blood tests. Front Med Eng (Lausanne). 2024;2:1369265.
  5. Shin K, Kim J, Park J, et al. A machine learning-assisted system to predict thyrotoxicosis using patients’ heart rate monitoring data: a retrospective cohort study. Sci Rep. 2023;13:21096.

Artificial Intelligence in Medical Device Industry

Medical Devices, , , , , , , , ,

What is artificial intelligence (AI)?

As per the Merriam Webster dictionary, AI is “the capability of a machine to imitate intelligent human behavior”.

The recently released proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL LAYING DOWN HARMONISED RULES ON ARTIFICIAL INTELLIGENCE (ARTIFICIAL INTELLIGENCE ACT) AND AMENDING CERTAIN UNION LEGISLATIVE ACTS defines AI system as a “software that is developed with one or more of the techniques and approaches that can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with”. According to this document, AI techniques and approaches include the following1:

(a) Machine learning approaches, including supervised, unsupervised and reinforcement learning, using a wide variety of methods including deep learning;

(b)Logic- and knowledge-based approaches, including knowledge representation, inductive (logic) programming, knowledge bases, inference and deductive engines, (symbolic) reasoning and expert systems;

(c)Statistical approaches, Bayesian estimation, search, and optimization methods;

Birth of AI into the Healthcare Field

Changes Brought by AI in the Medical Device Field

AI has brought about many revelations in healthcare field. It would be difficult to sum it all up in one blog hence we would be looking into some of the changes brought by AI into healthcare.

So, what do you think about an automatic blood pressure monitor at your homes? Well, yes, that is a change brought out by AI since it mimics the activity of a trained physician in detecting the sounds that are generated when a blood pressure cuff changes the flow of blood through the artery and in reporting the diastolic and systolic blood pressure measurements3.

Many such devices are available in the market that does not require a physician nearby, instead you can work on it by yourself.

And now companies are equipping themselves with machine learning to monitor patients using sensors and automate delivery of treatment using connected automated mobile apps. Ex: Medtronic launched the MiniMed 670G system, which is AI trained on algorithms that help to self-adjust insulin delivery once we feed the amount of insulin required for a given time 4.

So, as AI integrated medical devices are slowly becoming part of our lifestyles, shouldn’t the safety concerns around it be more stringent.

Regulations around AI integrated medical devices

An AI/ML screening tool for the eye disease occurring due to diabetic retinopathy, was cleared (in 2018) to aid in diagnostic decision by the FDA. It was cleared since it was a tool which was based on a ‘locked’ algorithm, which means that they don’t evolve over time and do not require new data to alter their performance. It is important that regulators follow stringent rules regarding software as a medical device using AI or machine learning (ML) so that they do not provide approval based on an already existing algorithm5.

As per a recent (2020) article published in the Nature, regulators must not restrict their evaluation to the AI/ML-based medical devices only but also assess the entire systems associated with it, for approval. The key things that should be done to attain a full system approach include5:

a) Collecting entire data such as current regulatory and legal mandate information, reimbursement decision of insurers, data quality of any third-party providers, any ML algorithms developed by third parties etc.

b) Issuing a limited authorization which would track factors discussed above

c) Seeking approval from a specific hospital, with specific trained and authorized users, and

d) Obtaining detailed hospital level information such as how the AI/ML-based medical device software is integrated into the workflow and staffing levels, the practice style and training of the physician, etc.

As for European Union is concerned, it is planning to tighten its regulations regarding AI by implying additional requirements on the use of AI in medtech along with heavy fines for those companies that fail to adhere to the EU requirements on AI. An official from the European Commission’s health group stated that “An AI medical device… would be now more secure, in the sense that it will also be complying with the MDR obligations and in addition those aspects of AI that could be creating some worries and some concerns would be handled by the new AI regulations. So, the two would be ensuring that the system is secure and trustworthy and so on6.”

Since AI is a vast and rapidly evolving topic, stay tuned to reading more about in our upcoming blogs/posts. Also, if you find our blogs to be interesting and you want to take the next step in advancing your knowledge on EU MDR and CER, consider our CER training class. Our experts are also available to help you with end-to-end CER development and gap analysis. Please contact us at [email protected]

References:

1) Proposal for a Regulation of the European Parliament and of the Council LAYING DOWN HARMONISED RULES ON ARTIFICIAL INTELLIGENCE (ARTIFICIAL INTELLIGENCE ACT) AND AMENDING CERTAIN UNION LEGISLATIVE ACTS. https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1623335154975&uri=CELEX%3A52021PC0206

2) Demystifying AI in Healthcare: Historical Perspectives and Current Considerations. https://www.physicianleaders.org/news/demystifying-ai-in-healthcare-historical-perspectives-and-current-considerations

3) Machine Learning AI in Medical Devices: Adapting Regulatory Frameworks and Standards to Ensure Safety and Performance. https://www.ethos.co.im/wp-content/uploads/2020/11/MACHINE-LEARNING-AI-IN-MEDICAL-DEVICES-ADAPTING-REGULATORY-FRAMEWORKS-AND-STANDARDS-TO-ENSURE-SAFETY-AND-PERFORMANCE-2020-AAMI-and-BSI.pdf

4)https://emerj.com/ai-sector-overviews/ai-medical-devices-three-emerging-industry-applications/

5)https://www.nature.com/articles/s41746-020-0262-2#Sec4

6)https://www.medtechdive.com/news/eu-plans-to-impose-additional-regulations-on-medtech-ai-products-other-hi/600022/