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1. Stefan Živković
Project Coordinator, NORBS
Data Science Conference
23th November 2023
Unlocking the Potential of
Artificial Intelligence (AI)
for Rare Diseases

2. National Organisation for Rare Diseases of Serbia is a non-profit umbrella organisation
bringing together associations of people with specific rare diseases, as well as individuals with
ultra rare diseases
NORBS was formed in 2010, and currently counts 33 member patient associations
The mission of NORBS is to improve the position of persons living with rare diseases and
persons with disabilities caused by a rare disease on the territory of the Republic of Serbia.
NORBS Activities:
1. Awareness raising activities for the general public
2. Rare diseases advocates trainings and educations
3. Educating healthcare professionals on rare diseases
4. Advocacy and policy making - communicating with stakeholders
5. Regular participation in conferences and forums on rare diseases
6. Help Line – direct support and assistance to people living with rare diseases
A b o u t N O R B S
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3. About Rare Diseases
NORBS.RS
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72% 70% 95%
of rare
diseases are
of a genetic
origin
of rare
diseases start
in childhood
have no
available
treatment
A disease is classified as “rare” if it affects
less than 1 in 2000 people. While some rare
diseases are quite close to a prevalence of 1
in 2000, most of them are actually much
rarer, affecting only 1 in 100000, 1 in 1 million
or even 1 in 1 billion people.
Over 6000 different rare diseases have been
identified. Each month there are about 5
new rare diseases documented in medical
journals.
About 5% of the global population live with
a rare disease.1
Nguengang Wakap, S., Lambert, D.M., Olry, A. et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet
database. Eur J Hum Genet 28, 165–173 (2020).
1.
2.Rare Barometer Survery - Journey to diagnosis for PLWRD - eurordis.org
It is estimated that in Europe around 30
million people live with a rare diseases,
while in Serbia we are speaking about
350,000 people.
5 years is the time it takes on average for
rare disease patient to get a diagnosis.2

4. Challanges of Rare
Diseases
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Many patients experience diagnostic odyssey (extensive and
prolonged series of test and clinical visits), sometimes for many year
Delayed diagnosis is frequent because of the lack of knowledge of
clinicians and small number of expert centers
Most RD still lack of approved treatments (orphan drugs)
Over 6000 rare diseases, expensive, not respond to available
therapies
Improvements needed to achieve patient-centricity for the
management of patients with RD (PRO, RWD, RWE)
Failure to diagnosis, late diagnosis and misdiagnosis
Lack of treatment, lack of satisfactory response to therapies
Lack of proper monitoring tools - prognosis
3. Hurvitz N, Azmanov H, Kesler A, Ilan Y. Establishing a second-generation artificial intelligence-based system for improving diagnosis,
treatment, and monitoring of patients with rare diseases. Eur J Hum Genet. 2021 Oct;29(10):1485-1490. doi: 10.1038/s41431-021-00928-4.
Epub 2021 Jul 19. PMID: 34276056; PMCID: PMC8484657.

5. Artificial Intelligence
and RD Diagnosis
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Accurate diagnosis of rare diseases is an important task in patient
triage, risk stratification and targeted therapies
Typical approach for diagnosis of rare diseases includes medical
history, physical examination, genetic testing. Additionally X-rays,
MRI, CT scans.
AI development of algorithms that can analyze large amounts of
date to identify patterns and markers that are characteristic of
specific rare disease
AI tools can help reduce time and costs - identifying potential
diagnosis more quickly and accurately
Examples

6. Artificial Intelligence
and RD Prognosis
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The prognosis includes information about the likely or expected
evolution, duration and outcome of the condition
AI helps to fill in the gaps in data and experience
Analyzing - EHR, genomic data, imaging studies
Commonly used AI approaches: Supervised learning with EL, SVM
Examples: ACC, AKU, ALS

7. Artificial Intelligence
and RD Treatment
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Urgent need to identify novel treatment options for rare diseases
(challenges)
Biomedical discoveries generate big amounts of data, opportunity
for AI
Currently - most AI methods for RD treatment belong to supervised
learning, which uses labeled datasets to train algorithms able to
classify or predict outcomes accurately
Examples for ALS, Gaucher diseases, DMD

8. Challanges and future
directions
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Integration of AI into healthcare domain presents a myriad of
challenges that encompass ethical, legal, technical and human
dimensions.
Prospective role of AI and ML in RDs holds substantial potential
AI systems function as intermediaries that brige diagnostic,
prognostic and therapeutic gaps.
Success of AI depends on the quality and quantity of available data
Transparency, privacy safeguards, stakeholders interested,
cybersecurity risks
“Beacon of hope” - offering advancements in diagnosis, treatment
and patient care.

9. The use of AI in RD
diagnostics in Serbia
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More information: https://www.ivi.ac.rs/
Project: Use of artificial intelligence in the early diagnosis of rare diseases
Who is invovled (medical institutions): University Clinical Center of
Serbia, the University Children's Clinic in Tirsova, and Zemun Hospital
Who is invovled (research institutions): Institute for Artificial Intelligence
Research and Development of Serbia
Method: Natural Language Understanding (NLU) tools designed to help
analyse free form medical texts in Serbian

10. Hurvitz N, Azmanov H, Kesler
A, Ilan Y. Establishing a
second-generation artificial
intelligence-based system for
improving diagnosis,
treatment, and monitoring of
patients with rare diseases.
Eur J Hum Genet. 2021
Oct;29(10):1485-1490. doi:
10.1038/s41431-021-00928-4.
Epub 2021 Jul 19. PMID:
34276056; PMCID:
PMC8484657.
Abdallah S, Sharifa M, I Kh
Almadhoun MK, Khawar MM Sr,
Shaikh U, Balabel KM, Saleh I,
Manzoor A, Mandal AK,
Ekomwereren O, Khine WM,
Oyelaja OT. The Impact of
Artificial Intelligence on
Optimizing Diagnosis and
Treatment Plans for Rare Genetic
Disorders. Cureus. 2023 Oct
11;15(10):e46860. doi:
10.7759/cureus.46860. PMID:
37954711; PMCID:
PMC10636514.
Visibelli A, Roncaglia B,
Spiga O, Santucci A. The
Impact of Artificial
Intelligence in the
Odyssey of Rare
Diseases. Biomedicines.
2023; 11(3):887.
https://doi.org/10.3390/
biomedicines11030887
Suggested reading
01 02 03
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Schaefer, J., Lehne, M.,
Schepers, J. et al. The
use of machine
learning in rare
diseases: a scoping
review. Orphanet J
Rare Dis 15, 145 (2020).
https://doi.org/10.1186
/s13023-020-01424-6
04

11. Reach out.
DO YOU HAVE
QUESTIONS?
stefan.zivkovic@norbs.rs