By: Poojitha Nagishetti, Department of Computer Science & Engineering (Data Science), Student of Computer Science & Engineering, Madanapalle Institute of Technology and Science, Angallu(517325), Andhra Pradesh.
Abstract –
One of the challenges of diagnosis and treatment of patients, especially children, is the availability of accurate diagnosis of rare diseases because of their infrequency and frequently, severe clinical manifestations. Often, conventional diagnosis is inconclusive because of the wealth of tainted evidence that is acquired from cases that human diagnostician rarely encounters. The application of artificial intelligence as well as an approach based on data provide the perspective of a breakthrough for these tasks. Being devoted to the interaction between AI and rare diseases, this article compares and discusses the changes in diagnostic efficacy, treatment plans, and the overall process of developing new drugs. Through machine learning, natural language processing, and predictive analytics, the approach of AI changes the focus of early diagnosis, improves the existing treatment plans, and fosters the creation of new targeted therapies. In detail, the article presents several success stories with the utilization of AI and examples of its recent implementation for the analysis of patient outcomes and further perspectives for AI in rare diseases’ study. The conclusion of the outcome underlines the driving force of the AI in overcoming gaps in the vulnerability of rare disease treatment as well as pointing to the future of serving the cause of better and more efficient medical treatments.
Keywords – Artificial Intelligence, Rare Disease, Machine Learning, Genetic Analysis, Personalized Medicine.
Introduction –
Orphan diseases are diseases that afflict not more than 2,000 people per million in the population and are also a heterogeneous, heterogeneous group of diseases that are difficult to diagnose and for which it is difficult to provide effective therapy[1]. In this context, it is possible to note the fact that relatively few diseases manifest themselves rather rarely, and most of them entail certain difficulties in the diagnosis and treatment. It takes many years to diagnose patients with rare diseases and they are subjected to incorrect differential diagnoses and inefficient therapy, which hampers their management and can compromise their results.
Many conventional diagnostic techniques are hampered by limited case exposure and the fact that there is little information available for comparison purposes[2]. Diagnostic methods commonly entail clinical end points that are often semiquantitative and require invasive procedures, and thus not very accurate. This is made worse due to scarcity of specialized knowledge and healthcare resources such that few healthcare professionals have adequate understanding of such disorders, thus might not be well equipped in diagnosing or managing such diseases.
As with many fields, AI and data approaches are now emerging as solutions to these kinds of problems. AI technologies with the aids of big data analysis and complex algorithms can discover ways of thinking which are not visible otherwise. AI has the capacity to change the way of diagnosing and treating rare diseases using ML, NLP, and predictive analytical models.
Advanced PAT can learn from structured and unstructured patient data and their genetic and clinical profiles to detect biomarkers of diseases and other diagnostic indicators[3]. The use of NLP methods allows enhancing the identification of signs of rare diseases and the results of their treatment, which are often concealed in free-text medical records. In early detection, predictive analytics help in the early determination of occurrence and development of diseases using previous records.
Understanding Rare Diseases: An Overview
Orphan diseases, or diseases of low incidence, can be termed as another category of giant problems for the world health care sector. These conditions are characterized in that they are rare diseases and incidence rates are less than 2,000 population per annum. Alone if taking a single rare disease, there are more than 7000 known diseases that are rare but still consultancy each affecting more than three hundred million people across the globe. This group of disorders comprises a very extensive classification affecting the central nervous system with many of the diseases being permanent, worsening, and lethal.
Most RTDs are monogenic, conditioned by a single mutated gene or polygenic, conditioned by multiple genes’ interplay. These genetic disorders are known to start early in life and rare diseases are known to affect half of the children. However, there are types of diseases which are very rare and may demonstrate manifestations only in adult organism due to the influence of environmental factors, certain lifestyle, or other still unknown factors. These diseases are not similar in their manifestations as they may involve any organ system in the body and therefore cause symptoms and clinical manifestations that may be almost inexhaustible.
Rare diseases present complex issues to a patient from getting diagnosed to the kind of treatment available, if any. Because such disorders are rare and complicated, clinicians may not have adequate information to perform correct diagnosis. This leads to multiple testing, consultations, and often misdiagnoses before getting to a stringent and definitive diagnosis[4]. The few professional healthcare workers that exist are not specialists in the ailments and the general population has little or no information about the diseases.
After that the patients are subjected to other barriers concerning their treatment. It is especially the case that sufficient treatments for many rare diseases are not known or do not exist at all. This is especially so since most of the time, due to profit making considerations, there is little investment in research and development on these uncommon ailments. Instead, most patients must turn to other drugs, additional treatments, or enrolment in trial programs for experimental treatments. Raina stated that since for many conditions there are no adequate treatment modalities, the burden return to patients and families.
Due to the tendency of the diseases towards complexity and variability, the rare diseases require the services of a team or a combination of specialists. It is not a rare case when management must involve specialists in genetics, neurology, immunology, and other fields, as well as the use of new technologies. Recent technical development in genetics genomics, Biotechnology, and AI is gradually changing the face of rare diseases diagnostic test and management. One has seen the use of genomic sequencing for quick identification of genetic mutations particularly those causing rare diseases which leads to accurate diagnosis as well as beginning of development of treatments.
Of all the technologies Artificial intelligence has a great potential to overcome the problems related to rare diseases. AI systems can go through countless data such as genome sequences, patients’ history, and images to search for confusing patterns of biomarkers that would likely remain unnoticed by human clinicians. It is therefore contributing to fast tracking of diagnosis, tailoring of treatment, and providing information for drug development that is related to such rare diseases.
Therefore, the relative significance of rare diseases is high even if each disease is known to occur in a small number of patients. The difficulties involved with these disorders from identification to management demonstrating the importance of research, development, and cooperation. With the development of technology and medical science, there remains optimism that deficits in comprehending and controlling rare diseases will be gradually closed, thus providing subject individuals better treatment consequences and quality existence.
AI in the Context of the Enhancement of Rare Diseases Diagnosis and Therapy
Artificial intelligence or commonly referred to as AI is becoming recognized as a power tool in rare disease diagnosis and treatment and provide unique solutions to some of the biggest problems that come with such diseases[5]. Due to the low incidence and peculiar nature of RDMs, their diagnosis could take a longer time and the therapeutic approaches are also scarce but with the help of AI it is possible to close these gaps using the analysis of the big data as well as using predictive analytics and such a concept as a precision medicine.
1. Accelerating Diagnosis
Being able to diagnose these diseases is one of the most important areas where AI is of outmost importance because it speeds up the process. The conventional approaches require more time and the use of complex and invasive diagnostic procedures and the inputs of professionals who possibly may be hard to reach out to. ML is a practical and adaptable form of AI that can use information in the form of genetics, patient histories, even images and spot correlations that would hint at the occurrence of these extremely rare diseases. Such models based on AI technologies can raise the earliest and more accurate suspicion of the disease and spare patients from the diagnostic odyssey. For example, the machine learning algorithms could involve evaluation of the gene profiles and causative mutations for varieties of the rare inherited diseases, which might be accomplished faster than the traditional techniques.
2. Enhancing Personalized Treatment
It also used in creating and designing individualized treatment plans for rare diseases. Many of these conditions result from gene mutations, and since everyone’s genes are different, the severity of the disorders and their clinical manifestations vary from person to person, which often makes it impossible for the treatment to be universal. Thus, AI algorithms can compile information from different kinds of data, ranging from patients’ genetics to clinical histories and even some aspects of people’s lifestyles to adapt treatments. This kind of patient, specific treatment can result in better care and often with less side effects, presenting a dramatic change in patients’ quality of life. AI is most useful about when standard treatment measures have not provided the desired benefits, since the system can introduce likely treatment possibilities based on the analysis of similar problems and expected outcomes.
3. Facilitating Drug Discovery
Medicinal development for specific ailments has for instance been a slow and serves activity largely because there are few patients, and the notion is that it will not be all that financially fruitful. The only fine thing which is seen progressing in this sector is AI as it aids in speeding up the process of identifying the candidates that can be beneficial in treating specific diseases and improving trials. Thanks, self-driven AI is able to scan and analyze big databases of chemical compounds to decide the efficacy and safety of the compounds in treating some now rare diseases. Moreover, as it holds capabilities of prediction to clinical trials results, it can help in the construction of accurate and resource minimized trials requiring fewer participants which is beneficial for the few populations belonging to rare disease groups. This fast-tracks drug discovery does not only give treatments at a fast rate but also at a fast and relatively cheaper rate in the development process.
4. Enabling concurrent screening as well as other care
It is, however, becoming increasingly embedded in the day-to-day follow-up and care those patients with rare diseases receive just beyond the diagnostics and treatments. Wearable health gadgets and remote care technology linkages collate information on patient state for example, pulse rate, blood sugar levels or breathing rate and feed this back in real time to the attendants. This systematic checkup helps in early identification of any complication that would has accompanied the disease, hence reproaching its further deterioration. Moreover, through AI it becomes possible to monitor the symptoms of most of the patients continuously and the conditions for the readiness of treatment interventions in accordance with actual information of the patient and overall behavior of the RD condition. Figure 1 shows the flowchart of AI integration in rare disease management.
Case Studies –
Case Study 1: In the domains of rare genetic disorder diagnosis
Rett syndrome is an example of using Great Artificial Intelligence in the diagnosis of rare genetic disorders that is the postnatal neurological developmental illness in the females who have crossed the one-year age. Prior to advent of clinical guidelines, diagnosis of Rett syndrome previously involved molecular testing for mutation in the MECP2 gene[6]. However, the symptoms and the mode of inheritance of these genes are diverse and the identification of a disease to be precise and early is challenging. Such algorithms were employed in one of the earlier studies to classify clinical whole-genome-sequencing data of patients who were suspected to have Rett syndrome. In particular, the trainings set of the given machine learning model consisted of thousands of sequenced genetic data, and the specific mutations associated with the disorder were identified with relatively high accuracy. It was an improvement to the subtle manner of diagnosing and considerably reduced the time taken in coming to more precise conclusion; at the same time, it improved the early part of the treatment phase by using distinct and personal techniques of healing.
Case Study 2: Application of AI in Drug manufacturing to meet the needs of Rare Caners
Fewer than 30 new cases are diagnosed yearly at an advanced stage and can be quite difficult to manage because of their rarity; this is mainly the case with Ewing sarcoma a primary malignant tumor of bones found in young individuals under the age of 40 years. AI can make the process of discovering new drugs for such diseases that are known to occur rarely much faster.
For instance, the effectiveness of an AI-powered platform in lays an incredibly exhaustive examining of drug response data gotten from Ewing sarcoma cell lines. This integrated data set on chemical structure, Gene Bank accession numbers of GenBank, and the cellular response and transcript profiles to identify the lead compounds[7]. New compounds had also been effective at the same level for Ewing sarcoma according to the effectiveness rated by the AI system. Logically, this also reduced the time it took for the science community to identify the drugs, come up with therapeutic interventions and in some cases clinical trials.
Case Study 3: Artificial Intelligence – Machine Learning – Deep Learning for Rare Metabolic Diseases
Another good example is in the aspect of employing artificial intelligence in diagnosing as well as managing of Pompe diseases which are very rare inherited disorders whereby glycogen tends to accumulate at the body cells. In such evidence, conventional techniques like enzyme activities and genetic analysis could at times be repetitive and imprecise. Patient data for example clinical signs, symptoms, biochemical values and genetics underwent a process of screening using the machine learning algorithms. The above medical AI model was able to identify how likely a patient has Pompe disease and from which type of metabolic disorders it should be differentiated. This led to less incorrect diagnoses and enhanced customization of the care plans that are tailored by the disease progression and the reaction to the therapy.
Applications of AI in Rare Disease Diagnosis and Treatment
Artificial intelligence or AI has been introduced in several caregiving spheres and offers effective instruments to discuss the numerous issues incurred by the rare disease’s community. Here are some of the key applications of AI in this field. Here are some of the key applications of AI in this field:
1. Hindi inclusion scan by genetic analysis and rare disease diagnosis
AI is being implemented in genetics in the following way: it makes genetic analysis faster and more accurate and can be used for the identification of the genes responsible for rare diseases. Conventional methods used for genetic testing may be relatively slow and labor intense to perform but through AI it is possible to obtain an analysis of mutations in whole-genome or exome sequencing within a short time. For example, one can use the artificial intelligence for analyzing the large amount of genetic data to find the variations, associated with such diseases as cystic fibrosis, Huntington’s disease, or certain forms of epilepsy. Not only do these improve diagnosis more rapidly, but they enable the initiation of such therapies much earlier and the use of risk-specific approaches.
2. AI-Driven Image Analysis
Imaging plays an important role in the diagnosis of many of the diseases especially the neurological, cardiovascular and any other organ-based diseases. Computer-assisted image analysis modules or, particularly, CNNs can be used to increase the fidelity of imaging investigations. These can identify certain patterns in the scans which include MRI, CT, etc., which a human is not capable of noticing. For instance, AI has been used to detect specific patterns in the images of the retinas linked to specific but small illnesses such as Stargardt disease or retinitis pigmentosa and to diagnose these diseases more accurately and at an earlier stage.
3. Drug Repurposing and Discovery
Pharmacological intervention for Rare Diseases has in the past been a dismal affair because it costs a lot of money and has very little market appeal. AI is tackling this problem through the concept of drug repurposing, which is the finding of new applications for existing medicines. Machine learning approaches can also work with huge sets of characteristics of the drugs, their interactions, and outcomes in patients to make an informed judgment about the set of existing drugs potentially beneficial for rare diseases. Moreover, the same creates new drugs at a relatively faster pace as it teaches how new molecules will behave with disease proteins. This application is especially helpful in diseases, such as ALS or rare types of cancer, when the choice of the treatment is rather scarce.
4. Forecasting of Diseases and Disease States
Big data analysis is extended to evaluate the leaning process by the identification of patterns that can be used to predict patients ‘conditions, especially in those chronic rare diseases. Using predictive analytics, the condition of the patient can be modeled and expect future deterioration making it easier for the caregivers to alter the management plans. For example, for some very of the metabolic disorders such as Gaucher’s disease, or Fabry’s disease AI can process biochemical indicators, genotype, and patient’s history of disease to forecast further progress of the disease in certain patient and provide more efficient management.
5. Patient Support & Management with the help of Artificial Intelligence
In most of the listed rare diseases, monitoring of symptoms and disease markers is required to be done on a repeated basis. The wearable devices and the remote monitoring thus save actual-time patient data that are difficult to monitor in rare forms of diabetes and rare pulmonary diseases like blood glucose level and respiratory function, respectively. These devices rely on AI to analyze the data then notify the health care providers in cases that require an intervention. Moreover, AI can help with tracking patient’s compliance to the recommended treatment, which is important for the effective treatment of rare diseases that require often complicated treatment plans.
6. Personalized Treatment Planning
Rare diseases are generally able to benefit from AI that is involved in the crafting of treatment plans for the patients. Thus, using the results of the genetic test, clinical records, even environmental data, it is possible to develop an individual therapeutic plan that meets the patient’s needs most effectively. This is valuable especially in diseases that have low incidence rates thus reaching treatments that the patient will exhibit distinctive responses to. AI can do the following: recommend specific type of therapies, change the doses, mix treatments in a way that is more likely to benefit the given patient and avoid side effects.
7. Artificial Intelligence in Rare Disease Research and Clinical Trials
It is also revolutionizing the process for identifying rare disease and the way that clinical trials are conducted. Orphan diseases, as the rare diseases are known, have a limited number of patients which hampers ordinary clinical trials financially and logistically. Some of the benefits of the use of AI in trial designs include the ability to select the best candidates for trial, forecasting the possible results, as well as categorizing of patients sensationally. This results in more effective search and more fruitful trials of the prediction formula for more patients. In addition, it allows understanding what previous trials or real-world evidence show to apply this knowledge in future experiments and treatment. Table 1 address both current challenges and prospects in the field.
Table 1: Challenges and Future Prospects of AI in Rare Disease Management.
Challenge | Description | Future Prospect |
Data Scarcity | Limited availability of large, annotated datasets | Creation of global rare disease registries and biobanks |
Interpretability | Complexity of AI models and lack of transparency | Development of explainable AI (XAI) techniques |
Regulatory and Ethical Issues | Ensuring compliance with regulations and avoiding biases | Enhanced regulatory frameworks and ethical guidelines |
Integration into Clinical Practice | Resistance from healthcare providers due to trust issues | Increased collaboration and validation of AI tools |
Cost of Implementation | High costs associated with developing and deploying AI systems | Reducing costs through technological advancements and economies of scale |
Challenges and Future Prospects in AI-Driven Rare Disease Management
However, there are several problems which must be solved even in the presence of the mentioned AI achievements in the diagnosis and treatment of rare diseases[8]. It is one of the most apparent issues to implement AI solutions: a lack of high-quality annotated data and their availability at the necessary volume. It is challenging to coordinate data according to patients since usually such diseases affect few individuals; at the same time, often pathology is genetic, and manifestations can be considerably different. Furthermore, these diseases are conditions that are not frequently come across and this makes the clinical research the algorithms very sparse in their information thus taking a lot of time and resources in the fashioning of an ideal AI algorithm[9]. Still, there are issues with AI, and one of them is that the models that comprise the algorithm are not very easy to explain. While algorithms can register the analogies and be quite accurate with estimates on complex subjects, the same is a challenge when it comes to providing the rationale for a certain selection or decision-making on certain more specialized therapeutics, like, for instance, complex rare diseases. Such opaqueness can only lead to a certain level of skepticism on the part of those in the healthcare delivery system to fully rely on AI based diagnosis and/or prognosis in respect of certain diseases/treatments that can forever transformed a person’s life.
Moreover, there arises the problem of implementing AI in clinical settings and this is bound with several questions concerning regulation and ethical framework[10]. In this highly competitive subject, it is important that AI systems are compliant with healthcare regulations and standards; the patient’s privacy must be upheld; and the AI system should have no prejudices – all of which are very difficult when there is a dearth of patients’ data, and it is heterogeneous. It is also important to involve the developers of an AI and the health care professionals or even patient’s communities to determine how an AI tool should be developed for it to be most suitable to the patients that suffer from these rare diseases.
Conclusion –
In conclusion, AI is a significant advance in the diagnosis and treatment of rare diseases, and it would assist humanity to keep on overcoming the test of the peculiar situations. In the following we present, how AI can improve the diagnostic approach to rare diseases, introduce large scale on individual disease treatment plans, help in drug development for rare diseases and improve the overall patient management. But there are some questions that are yet to be answered to advance the case of AI even further in this discipline, such as shortage of data, model interpretability and the finally – the legal concerns. Nevertheless, such challenges, do not predetermine a gloomy future for the development of care for rare diseases – the researchers are simply trying harder to invent new products, synthesize one field with another and develop the more effective AI systems. With the benefits of AI in fashionable diseases as a tool of hope to the affected patient and their families, provide accurate definition to the disease, increased and favorable results in treatment and therefore the quality life into the affected patients with rare diseases.
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Cite As
Nagishetti P. (2024) Data-Driven Insights into Rare Disease Diagnosis and Treatment with AI, Insights2Techinfo, pp.1