AIML PRESENTATION

"A.I in Healthcare: Transforming the Future"

ARTIFICAL INTELLIGENCE AND MACHINE LEARNING FOR BUSINESS

"An Overview of Applications and Impact"

INDEX

What is AI?

Why AI in Healthcare

Recent Trends

INDEX

Applications of AI

Drawbacks of AI

Project

Conclusion

Closure

WHAT IS A.I ?

ARTIFICAL INTELLIGENCE

Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings(Humans).

INTRODUCTION TO A.I

+INFO

WHY AI IN HEALTHCARE?

ARTIFICAL INTELLIGENCE IN HEALTHCARE

Artificial Intelligence (AI) is employed in healthcare to enhance efficiency, accuracy, and overall patient outcomes. By automating administrative tasks and aiding in diagnostic processes, AI streamlines healthcare operations, allowing professionals to focus on patient care. Its ability to analyze large datasets enables early detection of diseases, contributing to preventive interventions and personalized treatment plans.

WHY AI IN HEALTHCARE?

+INFO

2018

2015-2020

FDA Approvals for AI Diagnostic Tools

Expansion of AI Applications

2016

IBM Watson for Oncology

RECENT TRENDS

2020-

COVID-19 and Accelerated Innovation

TRENDS

2020s

2019

Continued Integration and Ethical Considerations

Google's DeepMind and AI in Drug Discovery

APPLICATIONS OF AI

PREDICTIVE ANALYSIS

AI models analyze patient data to predict disease risk, patient deterioration, and hospital readmissions, enabling early intervention and personalized care plans.

APPLICATIONS

ROBTIC SURGERY

AI-powered robotic systems assist surgeons in performing complex procedures with precision, reducing invasiveness and improving patient recovery times.

+INFO

83%

40%

11.4%

AI FOR

AI FOR

AI FOR

IMPROVED HEALTHCARE EXPERIENCE

INCREASED EFFICENY

KEEPING TRACK OF PATIENTS

USES OF AI

The prevalence of diabetes in India stands at 11.4%. Patients can now use wearable and other monitoring devices that provide feedback about their glucose levels to themselves and their medical team.

According to Harvard’s School of Public Health, although it’s early days for this use, using AI to make diagnoses may reduce treatment costs by up to 50% and improve health outcomes by 40%.

A recent study found that 83% of patients report poor communication as the worst part of their experience, demonstrating a strong need for clearer communication between patients and providers.

DATA COLLECTED FROM IBM.

SOCIAL

CONFIDENCE

03

01

ETHICAL CONCERNS

USER ACCEPTANCE AND TRUST

Addressing ethical considerations such as bias in AI algorithms, ensuring fairness, transparency, and avoiding discrimination in healthcare decision-making.

Gaining acceptance and trust from healthcare professionals and patients regarding the reliability and safety of AI-driven technologies.

POTENTIAL DRAWBACKS OF AI

SAFETY

EXPENSIVE

04

02

PRIVACY AND SECURITY

COST OF IMPLEMENTATION

Safeguarding patient privacy and securing sensitive healthcare data from unauthorized access or breaches as AI systems rely heavily on large datasets.

Managing the upfront costs associated with implementing AI infrastructure and ensuring that the benefits justify the investment.

DRAWBACKS OF AI

PROJECT ON DIABETES ANALYSIS

Conclusion: Summarized the findings and their implications for improving diabetes diagnosis and care among Pima Indian females. This project aims to contribute to the field of healthcare by leveraging data-driven insights to enhance diagnostic capabilities and ultimately improve the lives of individuals in the Pima Indian community.

GITHUB REPOSITORY- https://github.com/ATHARVAKARANDE27/Prediction.git

PROJECT

DIABETES ANALYSIS

CONCLUSION

Conclusions

POINTS DISCUSSED IN THE PRESENTATION

• DRAWBACKS OF AI
• ANALYSISOF DIABETES DATASET
• CONCLUSION

• WHAT IS AI
• WHY AI IN HEALTHCARE
• RECENT TRENDS IN AI
• APPLICATIONS OF AI

AI IN HEALTHCARE

THANK YOU!

PROJECT BY - ATHARVA KARANDE SOHAM JADHAV AREEN MUKHERJEE PARUL CHOURASIA JINISHA CHOUDHARY

The use of AI in healthcare is expected to grow, with advancements in precision medicine, population health management, and AI-driven robotics. However, ethical considerations, data privacy, and regulatory frameworks will continue to be important aspects of AI in healthcare.

AI can help providers gather that information, store and analyze it, and provide data-driven insights from vast numbers of people. Leveraging this information can help healthcare professionals determine how to better treat and manage diseases.

Google's DeepMind made strides in AI applications for drug discovery, demonstrating the potential of AI in accelerating the identification of new therapeutic compounds.

AI integration in healthcare significantly enhances efficiency across various aspects of the industry. Through automation of routine administrative tasks such as appointment scheduling and billing, AI allows healthcare professionals to allocate more time to direct patient care. In diagnostics, AI-powered algorithms analyze medical images rapidly and accurately, leading to quicker and more precise diagnoses. Predictive analytics aids in resource allocation, optimizing staff and facility utilization, and reducing wait times.

IBM Watson for Oncology was introduced, using AI to assist oncologists in cancer treatment decisions by analyzing vast amounts of medical literature and patient data.

The COVID-19 pandemic highlighted the importance of AI in healthcare, with applications in epidemiology, diagnostics, drug repurposing, and vaccine development. The crisis accelerated the adoption of telehealth and remote patient monitoring, further incorporating AI technologies.

As AI becomes more integrated into healthcare workflows, nurturing a culture of collaboration, openness, and ongoing feedback helps cultivate user acceptance, laying the foundation for a seamless and trustworthy partnership between healthcare professionals, patients, and AI technologies.

AI applications expanded into various healthcare domains, including personalized medicine, drug discovery, predictive analytics, and genomics. Companies and research institutions began investing heavily in AI for healthcare solutions.

While the upfront costs of implementing AI in healthcare can be substantial, organizations often weigh these investments against potential long-term benefits, such as improved diagnostic accuracy, streamlined workflows, and enhanced patient outcomes. As AI technology continues to advance, costs may evolve, and the return on investment becomes a critical consideration in healthcare AI implementation strategies.

Key concerns include the potential for algorithmic bias, as AI systems may unintentionally reflect and perpetuate existing disparities in healthcare. Ensuring fairness and mitigating bias is crucial to prevent discrimination against certain demographic groups.

ARTIFICAL INTELLIGENCE AND ITS IMPORTANCE

IMPORTANCE OF AI

WHAT IS AI ?

• AI is important for its potential to change how we live, work and play. It has been effectively used in business to automate tasks done by humans, including customer service work, lead generation, fraud detection and quality control. In a number of areas, AI can perform tasks much better than humans. Particularly when it comes to repetitive, detail-oriented tasks, such as analyzing large numbers of legal documents to ensure relevant fields are filled in properly, AI tools often complete jobs quickly and with relatively few errors.

• As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use it. Often, what they refer to as AI is simply a component of the technology, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No single programming language is synonymous with AI, but Python, R, Java, C++ and Julia have features popular with AI developers.

CHALLENGES AND POTENTIAL SOLUTIONS

CHALLENGES IN TRADITIONAL HEALTHCARE

HOW AI CAN CHANGE THIS

• Artificial Intelligence (AI) holds significant promise in overcoming current healthcare challenges by revolutionizing diagnostics, data management, and treatment approaches. AI's ability to analyze vast datasets enhances diagnostic accuracy, optimizing patient care. It efficiently manages and interprets extensive patient information, facilitating personalized treatment plans and population health management. Predictive analytics aids in resource allocation, reducing wait times and improving healthcare efficiency.

• Without the incorporation of Artificial Intelligence (AI), healthcare encounters inefficiencies such as diagnostic delays, data overload challenges, and resource allocation inefficiencies. Manual administrative processes can be time-consuming, diverting attention from direct patient care, while the absence of advanced analytics limits the ability to implement timely preventive measures. Traditional one-size-fits-all treatment approaches may result in less personalized care, and drug discovery processes can be prolonged and expensive.

The U.S. Food and Drug Administration (FDA) started approving AI-based diagnostic tools, such as the IDx-DR, an AI system for detecting diabetic retinopathy.

AI technologies like natural language processing (NLP), predictive analytics and speech recognition could help healthcare providers have more effective communication with patients. AI could, for instance, deliver more specific information about a patient’s treatment options, allowing the healthcare provider to have more meaningful conversations with the patient for shared decision-making.

Ensuring privacy and security is a critical challenge in healthcare AI due to the sensitivity of patient data. AI systems often rely on vast datasets, including personal health information, and maintaining the confidentiality of this information is paramount.