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Can Machine Learning be Used for Healthcare and Medical Diagnosis

Artificial Intelligence and Machine Learning are increasingly making our daily lives more efficient and convenient. AI is the technology driving many of these programs and services that help us do daily tasks such as connecting with friends on Social Networks, using an Email program, or using a Cab service. Most of us have been using AI daily for many years now without fully being aware of it.

AI and ML are applied in several real-life situations, with some of the common examples being: Digital Assistants, Navigation through Maps, Parking Vehicles and Self-Driving vehicles, Smart E-mail communication, Predictive Web search, AI-Enhanced Experience at Online Stores and Services, Social Media Apps, Commercial Airlines, Medical diagnoses, and health care.

Effective and good quality Healthcare services are dependent on the precise diagnosis of diseases. Artificial intelligence and Machine learning tools hold immense promise to improve the accuracy of diagnosis and thereby aid healthcare service providers.

Machine Learning (ML)

A division of artificial intelligence, Machine Learning, is based on the premise that computer systems can make decisions using algorithms and statistical models, which helps analyze and draw inferences from data and patterns with the least human intervention.

The increasing role of Machine Learning in Medical Diagnosis and Health Care:

Machine learning is observed in many healthcare applications, like case management of prevalent chronic diseases and leveraging patient health data, in conjunction with external factors like pollution exposure and weather.

Machine learning technology can help healthcare professionals generate precise medicine solutions customized to individual patient characteristics by crunching large volumes of data.

With such immense scope in medical diagnosis and health care, students and professionals interested in pursuing a career in the health sector can consider several free machine learning courses offered by well-known training/educational institutions.

Let us take a closer look at practical examples of how machine learning paradigm change ML has brought in providing smart medical diagnosis and effective health care services. With experience, ML applications can potentially grow in scope and deal with increased sophistication.

Oncology: Timely detection of a malignant tumor is important for diagnosis and clinical decision-making on the correct course of treatment.

  • Machine learning helps Oncologists detect cancer at an early stage. For example, diagnostic tools like DeepGene have enabled medical professionals to detect somatic mutations easily. Faster and accurate pinpointing of mutation markers is possible due to the application of Artificial intelligence.
  • Machine learning helps accurately determine a tumor’s malignancy in milliseconds. While it is understood that computer-based predictions aren’t error-free, it is noteworthy that tumor classification accuracy is 88%.

Pathology: Machine learning can make a crucial difference in processing large datasets in Pathology. With enhanced efficiency, pathologists can use their time smartly in clinical analysis and interpretation, alleviating the shortage of pathologists in many countries.

  • Automated tissue and cell quantification helps improve precision and reliability in blood and culture analysis.
  • Mapping of affected cells and flagging them for further research studies
  • Making profile scanning faster, thereby Improving healthcare professionals’ productivity

Dermatology: Artificial intelligence improves the accuracy of skin disease diagnoses and the quality of clinical decision-making. There are many functional machine learning implementations in Dermatology, like:

  • Machine learning can help decrease the number of avoidable biopsies that patients undergo.
  • ML algorithms have helped separate melanomas from benign skin lesions with higher precision and reliability.
  • Tools tracking skin moles changes help in the early detection of pathological conditions.

Genetics and Genome Study: Artificial intelligence has helped Geneticists make rapid progress in the transcription of human genes.

  • The Human Genome Project is a healthcare and technological collaborative research project with potentially revolutionary implications.
  • Preventive Genetics is greatly assisted by ML and AI technologies. Algorithms enable Scientists to understand how drugs, chemicals, and environmental factors influence the human genome.

Mental health: Mental health disorders can be debilitating to concerned individuals, but low productivity, increased health spending and lower overall quality of life affect families and communities. Research indicates that 20% of the adult population may have mental health-related challenges. AI and ML can profoundly impact mental health research and the efficiency of medical diagnosis.

Machine learning tools help

  • avoid the social isolation of high-risk groups, thereby aiding mental disease prevention.
  • In identifying and providing support to high-risk suicide groups.

Smart Medical Records: Machine learning is helping reduce the cost of EMR Systems (Electronic Medical Records) by optimizing and standardizing the way those systems are designed. Ultimate helps provide improved care at a lower cost.

Drug R & D and Manufacturing: Machine learning assists in the early-stage drug discovery. This also includes R & D technologies such as next-generation sequencing and precision medication, helping find alternative therapy paths for multifactorial diseases.

Clinical Trial and Research: Clinical trials, in most cases, take a longer time to complete and involve larger financial investment. Applying ML-based predictive analytics to identify potential candidates for a clinical trial can help researchers draw a much faster pool from a wide variety of data points.

Outbreak Prediction: AI and ML technologies are increasingly being used in monitoring and predicting epidemics and pandemics across the world. Scientists now have access to a large amount of data from satellites, real-time social media updates, website information. Artificial neural networks further help in collating this information and predicting outbreaks like malaria, chronic infectious diseases.

The recent C-19 pandemic has hastened interest in ML and AI in the IT and student community. As aspiring students and working IT professionals explore possibilities on what skills can impact today’s world and seek to acquire them, python for machine learning can be an area for skilling up-skilling.

Conclusion:

Machine Language (ML) and Artificial Intelligence (AI) applications are growing by the day. They have made a humongous difference in the qualitative changes these technological tools have brought in several sectors. ML and AI impact on the medical diagnosis and health care can only spur further attention, investments for enhancing applications by Governments, Policymakers, and Medical sector Leaders.

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