Generative Artificial Intelligence (AI) in Healthcare Technology - PowerPoint PPT Presentation

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Generative Artificial Intelligence (AI) in Healthcare Technology

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Generative AI is a type of artificial intelligence that can create new data that is comparable to existing data. This data may comprise text, images, and other types of information. The application of generative AI is widespread, encompassing the healthcare sector. Because of generative AI's innovative ideas and revolutionary possibilities, the healthcare industry has seen a noticeable growth in its application. – PowerPoint PPT presentation

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Date added: 22 November 2023
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Title: Generative Artificial Intelligence (AI) in Healthcare Technology


1
Generative Artificial Intelligence (AI) in
Healthcare Technology
  • Generative AI is a type of artificial
    intelligence that can create new data that is
    comparable to existing data. This data may
    comprise text, images, and other types of
    information. The application of generative AI is
    widespread, encompassing the healthcare sector.
    Because of generative AI's innovative ideas and
    revolutionary possibilities, the healthcare
    industry has seen a noticeable growth in its
    application.
  • Download-
  • https//www.marketsandmarkets.com/industry-practic
    e/RequestForm.asp?pageGenerative20AI
  • Important uses and advantages of Generative AI in
    Healthcare
  • Drug Discovery and Development
  • Molecule Generation Using generative AI models
    to develop and produce novel molecules with
    desired properties helps expedite the drug
    discovery process.
  • Virtual Screening The virtual screening of
    chemical compounds powered by AI can save time
    and money in the process of identifying potential
    medicine candidates.
  • Side Effect Prediction Artificial intelligence
    models can assist in safety assessments by
    predicting potential side effects of new drugs.

2
  • Medical Imaging and Analysis
  • Image Enhancement Generative artificial
    intelligence (AI) improves the quality of medical
    images, leading to more accurate diagnosis.
  • Image Segmentation Through the segmentation and
    identification of regions of interest in medical
    images, AI can assist in the diagnosis and
    planning of treatments for tumors.
  • Synthetic Data Generation To enhance the minimal
    datasets used in machine learning model training,
    artificial intelligence generates artificial
    medical images.
  • Disease Diagnosis and Prediction
  • Early Detection It is possible to examine
    patient data to identify early disease indicators
    and plan appropriate therapy by utilizing
    generative AI.
  • Diagnostic Support Diagnoses enabled by AI that
    provide suggestions and insights gleaned from
    patient data are helpful to medical
    practitioners.
  • Pharmacogenomics AI algorithms predict how
    patients would react to specific therapies, which
    helps with drug selection.
  • Health Data Generation
  • Synthetic Data for Privacy Artificial
    intelligence generates artificial patient data
    for research and analysis purposes in order to
    protect patient confidentiality.
  • Electronic Health Records (EHRs)
  • Data Extraction and Summarization AI can extract
    and summarize relevant data from electronic
    health records, which can help healthcare
    providers work more efficiently.

3
Generative AI in Healthcare
  • The use of generative AI has made it possible for
    those who live in distant areas or are unable to
    travel to conventional healthcare institutions to
    obtain medical care. The use of generative AI
    makes virtual patient consultations feasible. You
    can do this with other technologies, including
    video conferencing. Through virtual
    consultations, patients can obtain the treatment
    they need without ever having to see a doctor.
    Applications of generative AI can be used to
    provide telemedicine services. Using
    telecommunications technology, this type of
    healthcare delivery provides treatment to
    patients remotely. Telemedicine can be used to
    provide many different services, such as
    consultations, diagnostic tests, and therapy.
    Generative AI is useful for creating mobile
    health apps. People can access healthcare
    services and information, such as symptom
    monitoring, prescription reminders, and
    connections to medical specialists, by using
    these applications. Generative AI can be applied
    to the development of wearable devices. Patients
    can provide information about their health, such
    as their blood pressure, blood sugar levels, and
    heart rate, using these devices. This data can be
    used to monitor a patient's health and provide a
    warning system for potential problems.

4
Challenges of generative AI in healthcare
  • Although generative AI has many potential uses in
    the medical field, there are also several
    problems that need to be fixed. A few challenges
    with generative AI in healthcare are as follows
  • Data availability Generous AI algorithms need to
    be trained on large data sets. However,
    collecting this data may be expensive and
    time-consuming, especially in the healthcare
    sector. Because healthcare data is often
    sensitive and confidential, patients may be
    reluctant to disclose it. This makes it difficult
    to collect and combine data for training
    generative AI models. To tackle the problem of
    data availability, healthcare institutions should
    try to collect and share data more effectively.
    They can also develop entirely new methods of
    gathering data, like as crowdsourcing and
    wearable technologies. Additionally, governments
    can support and fund research into the use of
    generative AI in healthcare.
  • Bias Generative artificial intelligence
    algorithms run the risk of providing skewed data
    that isn't always representative of reality. This
    could lead to incorrect diagnosis and misdirected
    treatment plans. Bias may exist in the algorithm,
    the training set, or the algorithm's application.
    To get around the bias issue, generative
    artificial intelligence (AI) systems can be
    trained on a range of datasets. Moreover,
    adversarial training and debiasing are techniques
    to reduce bias in the algorithms. Healthcare
    organizations should also establish policies and
    procedures to ensure that generative AI
    algorithms are used equitably.

5
  1. Regulation In the healthcare industry,
    generative AI is still in its early stages of
    development and application, and its use is
    currently unregulated. This could give rise to
    safety concerns. Governments are able to enact
    rules governing the development and use of
    generative AI in the medical domain. These
    limitations can help to ensure that technology is
    used safely and morally.
  2. Security Generative AI algorithms may generate
    data that is harmful or inaccurate. Both medical
    personnel and patients may suffer as a result of
    this. To protect data from abuse or unauthorized
    access, security measures might be put in place.
    For example, encryption can protect data both at
    rest and in transit. Moreover, only authorized
    personnel may be able to access data.
  3. Interpretability It can be difficult to
    interpret the output produced by generative AI
    systems. This makes it potentially difficult to
    confirm that the algorithms are generating
    accurate data and to understand how they work.
    Enhancing the interpretability of generative AI
    systems can be accomplished through methods such
    as explanation and visualization. Users may be
    more likely to understand how the algorithms work
    and to trust the results they produce as a
    result.
  4. Accuracy Although generative AI systems are
    still in the early stages of development, their
    accuracy varies depending on the datasets they
    are trained on. This could lead to incorrect
    diagnosis and treatment. Generative AI systems
    can be made more accurate by strengthening them
    and using more and more diverse training datasets.

6
  • Acceptance Since generative AI is still a
    relatively young technology, there may be
    considerable resistance to its application in the
    medical field. This is occurring because people
    may not trust the algorithms or may be concerned
    about the potential repercussions. Healthcare
    organizations have the ability to educate
    patients and providers about generative AI and
    its potential benefits. They can also establish
    policies and procedures to guarantee that
    technology is used ethically and safely.
  • Through the resolution of these problems,
    generative AI can aid in improving the
    effectiveness and efficiency of healthcare.
  • All things considered, generative AI is an
    innovative technology that has the potential to
    drastically alter the medical industry. However,
    there are still a few problems that need to be
    fixed before the technology is generally adopted.
    By addressing these problems, generative AI has
    the ability to improve patient care quality and
    raise the efficiency and equity of healthcare.
  • Future of Generative AI in Healthcare
  • The application of generative AI in medicine
    looks very promising. As technology develops, we
    should expect dramatic changes in the way we
    diagnose, treat, and prevent illnesses.
  • Read More - https//www.marketsandmarkets.com/indu
    stry-practice/GenerativeAI/genai-healthcare
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