Transforming Rare Disease Diagnosis with AI

Year : 2026 | Volume : 15 | Issue : 01 | Page :
By

Saara Darakshan,

Riya Gupta,

Ashish Khare,

  1. Student, Department of Computer Science and Engineering, Lakshmi Narain College of Technology and Science, Bhopal, Madhya Pradesh, India
  2. Student, Department of Computer Science and Engineering, Lakshmi Narain College of Technology and Science, Bhopal, Madhya Pradesh, India
  3. Professor, Department of Computer Science and Engineering, Lakshmi Narain College of Technology and Science, Bhopal, Madhya Pradesh, India

Abstract

Artificial intelligence is changing healthcare fast. It is making diagnoses accurate, helping doctors get better results, and streamlining how care works. This paper looks at how AI shows up in healthcare right now – where it is already making a difference, what is working, and what is still tricky. The focus is on machine learning, natural language processing, and computer vision. Particular attention is given to using AI in diagnosing rare diseases by integrating genomic and phenotypic data, addressing the limitations of traditional diagnostic methods. Rare diseases are highly diverse and complex, which leads to major gaps in research, medical expertise, diagnosis, treatment options, and overall patient quality of life. Although each rare disease affects only a small fraction of the population – around 1 in 2,000 people
in Europe and 1 in 1,250 in the United States – the total number of such conditions is huge, with over 5,000 identified worldwide. As a result, rare diseases collectively impact millions of people, affecting nearly 30 million individuals in Europe and about 25 million in North America. Rare diseases affect a small number of people but cause huge health and financial stress, especially in India. Because of genetic diversity and practices like consanguineous marriages, India faces extra challenges in identifying and managing these conditions. Although the National Policy for Rare Diseases (2025) aimed to improve care, awareness among doctors and the public is still low, diagnoses are often delayed, and treatments can cost over ₹1 crore while government support is limited. Primary care lacks early screening and strong referral systems, and access to affordable orphan drugs remains difficult. However, the recent Union Budget 2026–27 brought important relief by cutting import duties on costly cancer and rare disease
medicines and proposing customs-duty exemptions for seven additional rare diseases – showing that the government is waking up to the needs of patients. To make care truly equitable, India still needs better early diagnosis, revised health technology assessment methods for rare diseases, more local drug production, and rare disease training in medical education. Advanced AI models like deep learning and clustering uncover novel disease patterns, refine diagnostics, and enable personalized medicine,
addressing challenges in data and ethics.

Keywords: Artificial intelligence (AI), deep learning, disease prediction, genotype-phenotype integration, machine learning (ML), natural language processing (NLP), rare disease diagnosis

[This article belongs to Research and Reviews : Journal of Computational Biology ]

How to cite this article: Saara Darakshan, Riya Gupta, Ashish Khare. Transforming Rare Disease Diagnosis with AI. Research and Reviews : Journal of Computational Biology. 2026; 15(01):-.
How to cite this URL: Saara Darakshan, Riya Gupta, Ashish Khare. Transforming Rare Disease Diagnosis with AI. Research and Reviews : Journal of Computational Biology. 2026; 15(01):-. Available from: https://journals.stmjournals.com/rrjocb/article=2026/view=238889

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Regular Issue Subscription Review Article
Volume 15
Issue 01
Received 28/01/2026
Accepted 12/02/2026
Published 23/03/2026
Publication Time 54 Days

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