INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025
www.ijltemas.in Page 535
Artificial Intelligence and Its Impact on Enhancing Women’s
Health and Medical Condition Monitoring.
Tejaswini Reddy Beerapu
Masters in Computer Science
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1410000068
Abstract: Artificial intelligence (AI) is revolutionizing healthcare, particularly in the field of disease detection. AI-powered
algorithms can analyze vast datasets of medical images, patient records, and genetic information to identify patterns and predict
disease risks with unprecedented accuracy. In women's health, AI applications are proving particularly promising in areas such as
breast cancer detection, early diagnosis of ovarian cancer, and prenatal risk assessment. AI-powered imaging analysis can
significantly improve the accuracy and efficiency of mammograms and ultrasounds, leading to earlier detection and improved
treatment outcomes. AI algorithms can also analyze a woman's medical history, lifestyle factors, and genetic predisposition to
predict her risk of developing certain diseases and personalize preventive care strategies. As AI continues to evolve, it holds
immense potential to transform women's healthcare by enabling earlier detection, more accurate diagnoses, and more personalized
treatment plans, ultimately improving women's health outcomes and longevity.
Keywords: Artificial Intelligence, Disease Detection, Healthcare, Medical Imaging, Predictive Analysis, Personalized Medicine
I. Introduction
Women’s health and medical conditions are often complex and, at times, underestimated. Conditions such as breast cancer, multiple
sclerosis, ovarian health issues and cancer, ectopic pregnancies, and thyroid disorders primarily affect women and their overall
well-being. Many of these conditions can significantly impact the quality of life once diagnosed, with some being particularly
debilitating.
This paper explores how AI can play a crucial role in raising patients’ awareness and enabling them to monitor their health more
frequently and effectively. By doing so, AI can help empower women to take proactive measures to avoid more serious and life-
threatening complications.
1. Breast Cancer:
Incidence and Mortality: Breast cancer is the most common cancer among Indian women, accounting for 28.2% of all
female cancers, with an estimated 216,108 cases by 2022.
Age-standardized Incidence Rate: The age-standardized incidence rate of female breast cancer has increased by 39.1%
from 1990 to 2016, with this trend observed in every state of India over the past 26 years.
Survival Rates: Patients with stage I disease had a better survival rate of 93.3%, while those with stage IV disease had a
survival rate of 24.5%, with an overall survival rate of 73.8%.
2. Multiple Sclerosis (MS):
Prevalence: In the 1980s, the prevalence of MS in India was estimated to be nearly 1/100,000. Recent evidence suggests
that the number of MS patients diagnosed annually has almost doubled.
Hospital Admissions: A hospital-based study from northwestern India observed that MS constituted 1.58% of the total
neurology admissions from 1968 to 1977. This increased to 2.54% of neurology admissions between the period 1993-
1997.
3. Ovarian Health and Cancer:
Incidence: Ovarian cancer is the seventh most common cancer among women in India, with an age-standardized incidence
rate of 3.6 per 100,000 women.
Mortality: The mortality rate for ovarian cancer in India is 2.5 per 100,000 women.
4. Ectopic Pregnancies:
Incidence: Ectopic pregnancies account for approximately 1-2% of all pregnancies in India. The incidence has been rising
due to factors such as pelvic inflammatory disease and increased use of assisted reproductive technologies.
Mortality: Ectopic pregnancies are a leading cause of maternal mortality in the first trimester in India. Early diagnosis
and treatment are crucial to reduce mortality rates.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025
www.ijltemas.in Page 536
5. Thyroid Disorders:
Prevalence: Thyroid disorders impact roughly 42 million people in India, with hypothyroidism being the most prevalent.
The prevalence of hypothyroidism is approximately 10%, while hyperthyroidism affects about 1-2% of the population.
Gender Disparity: Women are more commonly affected by thyroid disorders than men, with a female-to-male ratio of
4:1.
Awareness: Despite the high prevalence, awareness about thyroid disorders remains low, leading to delayed diagnosis
and treatment.
These statistics highlight the significant burden of these medical conditions on women's health in India. AI tools have the potential
to significantly improve the diagnosis, management, and monitoring of various women's health issues. Here’s how AI can be applied
to each of these conditions:
1. Breast Cancer
AI for Early Detection: AI can analyse mammograms and other imaging techniques more accurately and quickly than
human radiologists. Tools like Deep Learning Algorithms are used to identify abnormal patterns, detect tumours, and
assess the risk of breast cancer before it is clinically obvious.
Predictive Analytics: AI models can analyse patient data, including family history, lifestyle factors, and genetic markers,
to predict the likelihood of developing breast cancer in the future, helping in preventive measures.
Treatment Monitoring: AI-powered systems can track a patient's treatment progress (e.g., chemotherapy), monitor side
effects, and suggest adjustments based on real-time data.
Personalized Medicine: Machine learning algorithms can analyse genetic information from patients to recommend the
most effective treatment options tailored to the individual, enhancing the chances of success.
2. Multiple Sclerosis (MS)
AI for Diagnosis: AI can help identify MS in its early stages using brain scans (MRI) by detecting subtle lesions or
changes in brain tissue that may indicate MS. Deep learning models can be trained on large datasets to differentiate between
MS and other neurological conditions.
Tracking Disease Progression: AI tools can analyse patient data to assess the progression of MS, helping doctors
determine the optimal treatment plan for each patient. This could include monitoring motor function, cognitive abilities,
and other symptoms.
Predictive Models: AI can predict disease flare-ups or relapses based on the patient’s historical data, lifestyle habits, and
clinical information, allowing for pre-emptive treatment adjustments to minimize relapse risk.
3. Ovarian Health and Cancer
AI in Early Detection: AI tools can analyse imaging results (e.g., ultrasound, CT scans) to detect ovarian cysts or masses
that could lead to ovarian cancer. These tools can provide automated assessments that help radiologists prioritize cases for
further testing.
Genetic Data Analysis: AI can analyse genetic data from patients to identify those at higher risk for ovarian cancer due
to inherited mutations (e.g., BRCA1, BRCA2). AI can then recommend screening schedules or preventive measures.
Personalized Treatment Plans: AI models can help oncologists design personalized treatment strategies based on a
patient's genetic profile, tumour characteristics, and responses to previous therapies, improving outcomes.
4. Ectopic Pregnancies
AI for Early Diagnosis: AI can assist in diagnosing ectopic pregnancies early by analysing ultrasound images, detecting
subtle signs such as abnormal embryo location, and helping clinicians intervene before the condition worsens.
Predictive Analytics: AI models can assess the risk of ectopic pregnancy based on patient data like previous pregnancies,
medical history, and risk factors (e.g., pelvic inflammatory disease or use of fertility treatments). This can guide preventive
care and early interventions.
Clinical Decision Support: AI tools can help clinicians determine the best course of action when an ectopic pregnancy is
suspected, such as recommending medical treatment or surgical options.
Data Collection and Continuous Learning: As more data is collected from patients diagnosed with ectopic pregnancies,
AI systems can continuously improve their accuracy. Using reinforcement learning techniques, AI can learn from mistakes
or missed diagnoses and provide more accurate predictions over time.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025
www.ijltemas.in Page 537
5. Thyroid Disorders
AI for Diagnosis: AI algorithms can be used to analyse blood test results (e.g., TSH, T3, T4) and other relevant data to
provide a quicker and more accurate diagnosis of thyroid disorders such as hypothyroidism or hyperthyroidism.
Predictive Monitoring: AI can track a patient's thyroid hormone levels over time and predict potential imbalances before
they become severe, allowing for timely intervention and more consistent treatment.
Personalized Treatment Plans: AI can analyse treatment responses and adjust medication dosages based on individual
patient data (e.g., genetics, lifestyle), leading to more effective management of thyroid conditions.
Early Detection of Complications: AI tools can identify early signs of complications associated with thyroid disorders
(e.g., cardiovascular issues, osteoporosis) through pattern recognition in patient data, allowing for proactive management.
Algorithm: RecommendTreatment(patient: PatientData)
1. Initialize:
- risk_score = 0
- recommendations = empty list
- available_treatments = GetAvailableTreatments(patient.condition)
2. Calculate Patient Risk Score:
risk_score = AssessRisk(patient)
WHERE AssessRisk:
- Evaluate demographic risk factors
- Analyze biomarker levels
- Consider genetic markers
- Account for comorbidities
- Return normalized risk score [0-1]
3. For each treatment in available_treatments:
a. Skip if treatment in patient.medical_history.previous_treatments
b. Calculate Base Effectiveness: effectiveness = GetBaseEffectiveness(treatment, patient.condition)
c. Adjust for Patient Factors: adjusted_effectiveness = effectiveness * (1 - risk_score)
d. Check Contraindications: contraindications = GetContraindications(patient, treatment)
IF Contraindications Not Empty: Continue to next treatment
e. Get Supporting Evidence: evidence = QueryEvidenceDatabase(treatment, patient. clinical_ data. condition,
patient.clinical_data.disease_stage )
f. Calculate Confidence Level: confidence = DetermineConfidence (adjusted_effectiveness, evidence.strength,
evidence.relevance )
g. IF adjusted_effectiveness > MINIMUM_THRESHOLD: Add to recommendations: { treatment_name: treatment,
effectiveness_score: adjusted_effectiveness, confidence_level: confidence, supporting_evidence: evidence, contraindications:
contraindications }
4. Sort recommendations by:
- Primary: effectiveness_score (descending)
- Secondary: confidence_level (descending)
5. Return top N recommendations
AI Tools to Support All These Conditions:
Wearable Devices & Sensors: Wearable devices and sensors are transforming the way healthcare is managed. These devices, such
as smartwatches, fitness trackers, and specialized health-monitoring tools like glucose monitors or ECG bands, track a wide range
of vital signs, symptoms, or treatment side effects. The data collected by these wearables is transmitted to AI-powered platforms,
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025
www.ijltemas.in Page 538
which analyse it in real time and provide predictions or alerts. The AI models involved in this process typically utilize predictive
modelling, where machine learning algorithms analyse sensor data to predict changes in health status or early symptoms of disease.
For instance, an increased heart rate could indicate the potential for a relapse in conditions such as Multiple Sclerosis (MS) or
indicate thyroid imbalances. Additionally, anomaly detection algorithms, such as Isolation Forests or Autoencoders, are employed
to detect abnormal patterns in the data, which could signal health complications such as irregular blood sugar levels or abnormal
respiratory patterns. Wearables like the Apple Watch and Fitbit are prominent in this field, as they track heart rate and activity,
which can be useful for monitoring MS, thyroid disorders, or even early signs of ovarian health issues.
Natural Language Processing (NLP): Natural Language Processing (NLP) plays a crucial role in processing unstructured medical
data, such as doctor’s notes, clinical records, or research papers. By utilizing NLP algorithms, healthcare professionals can access
information more quickly and efficiently, enhancing diagnosis and treatment decisions. NLP enables machines to understand and
interpret human language, which is particularly useful in the medical field for extracting relevant data from large volumes of text.
Named Entity Recognition (NER) is one such NLP technique that identifies key medical entities like diseases, symptoms, and
medications. Text classification models, using algorithms such as Support Vector Machines (SVM) or deep learning models like
LSTM or BERT, categorize documents into meaningful groups like diagnosis or treatment plans. Clinical Decision Support Systems
(CDSS) powered by NLP provide actionable recommendations based on medical literature and patient histories. For instance, IBM
Watson uses NLP and machine learning to provide evidence-based treatment options for breast cancer patients, assisting doctors in
making well-informed decisions. Similarly, the MedeAnalytics platform utilizes NLP to analyse patient records, offering insights
for improving outcomes in conditions like MS, ovarian cancer, and thyroid disorders.
Chatbots & Virtual Assistants: AI-powered chatbots and virtual assistants are increasingly utilized in healthcare to provide
patients with 24/7 support. These chatbots can answer questions related to medical conditions, remind patients about medications,
and even guide them to seek immediate medical help when necessary. By utilizing Natural Language Processing (NLP) and machine
learning, these chatbots understand and respond to user inputs, offering personalized advice and responses based on the patient's
data. AI systems can manage conversations using Dialog Management algorithms, such as Recurrent Neural Networks (RNN) or
generative pre-trained transformers like GPT-4. These models help ensure that the chatbot’s responses remain coherent and
contextually relevant throughout the conversation. AI-powered chatbots can also provide personalized recommendation engines
based on patient symptoms, suggesting specific actions like scheduling an appointment or taking medication. Health apps like
Babylon Health and Ada Health are excellent examples of AI-powered virtual assistants that utilize chatbots to help users with
conditions like MS, thyroid disorders, and breast cancer by providing symptom checks and directing them to medical professionals
when necessary.
AI-Powered Imaging: AI-powered imaging technologies are revolutionizing diagnostic imaging by enhancing the analysis of
medical scans such as MRIs, CT scans, and mammograms. Convolutional Neural Networks (CNNs), a deep learning model, are
particularly effective at analysing medical images to detect conditions like tumours, cysts, or lesions. CNNs learn to recognize
specific patterns within images, helping healthcare providers identify potential health issues before they become more severe. In
addition to detection, segmentation algorithms are used to separate regions of interest within the images, such as tumour boundaries
or lesion areas. AI systems are capable of performing these tasks more accurately and faster than traditional methods. For instance,
Google Health's AI model for breast cancer detection has demonstrated superior performance compared to human radiologists in
analysing mammograms. Similarly, Zebra Medical Vision offers AI-driven solutions for detecting various conditions, such as
ovarian cancer, from imaging scans.
Telemedicine and AI-Enhanced Diagnostics: Telemedicine, combined with AI-enhanced diagnostics, is providing new
opportunities for remote consultations and diagnoses. AI tools assist healthcare professionals by analysing diagnostic data such as
X-rays, lab results, and imaging files. These systems offer preliminary diagnoses or suggestions for further tests, allowing clinicians
to make more informed decisions during telemedicine consultations. AI in telemedicine leverages computer vision techniques to
analyse images for abnormalities like lung nodules in chest X-rays or lesions in brain MRIs. Data fusion algorithms, such as Random
Forest or Gradient Boosting Machines, are used to integrate different types of data (such as lab results and patient history) to form
a comprehensive diagnostic picture. Platforms like Arterys use computer vision to assist in analysing medical images during
telemedicine consultations, providing accurate readings of images for conditions like breast cancer. In addition, Babylon Health’s
AI can analyse patient data, including symptoms and lab results, to help make initial diagnoses or recommend further testing during
virtual healthcare visits. By leveraging AI in these ways, the healthcare system can greatly enhance early detection, personalized
treatment, and overall monitoring, improving outcomes and quality of life for individuals affected by these conditions.
Challenges to Implementation:
Data Quality & Privacy: High-quality, comprehensive datasets are essential for training AI models, and patient data
privacy must be maintained.
Integration with Clinical Workflow: AI systems need to integrate seamlessly into existing clinical workflows and
medical record systems to be effective.
Access to Technology: AI-based tools might not be accessible in rural or underserved areas, limiting their potential to
help all women.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025
www.ijltemas.in Page 539
Despite these challenges, AI-powered predictive analytics offers tremendous potential for improving outcomes through earlier
detection, more accurate diagnosis, and personalized treatment.
References:
1. Liu, Y., et al. (2020). “Artificial Intelligence in Ovarian Cancer Detection: A Systematic Review.” Journal of Ovarian
Research, 13(1), 45-60.
2. Shbaih, M., et al. (2019). “Artificial Intelligence in Breast Cancer Detection and Diagnosis: A Comprehensive
Review.” Journal of Medical Imaging and Health Informatics, 9(5), 1236-1247.
3. Sabaté, M., et al. (2020). “Artificial Intelligence in Multiple Sclerosis: Applications and Challenges.” Journal of
Neuroinflammation, 17(1), 243.
4. Singh, H., et al. (2018). “Artificial Intelligence in Thyroid Disorders: From Diagnosis to Management.” Endocrine
Reviews, 39(6), 931-948.
5. Gosling, A., et al. (2021). “Artificial Intelligence in Ectopic Pregnancy Diagnosis and Monitoring.” Journal of Obstetrics
and Gynaecology Research, 47(1), 22-30.