Nanomedicine & Artificial Intelligence: Their Convergent roles in advancing healthcare - PART II

00:32:23
https://www.youtube.com/watch?v=0rZXRqnZ_mo

Summary

TLDRThis video, a continuation of a series on healthcare advancements, focuses on the role of artificial intelligence (AI) in revolutionizing healthcare systems. While the initial part of the series discussed nanoparticles and nanomedicine, this segment emphasizes AI's transformative capacity in diagnostics, treatment, and patient care. AI's applications are highlighted through innovations like smart wearable technology, AI-driven imaging for early disease detection, and personalized medicine, which all contribute to enhancing healthcare delivery and patient outcomes. Furthermore, AI's integration with nanotherapeutics is underscored to illustrate its potential in advancing medical research and treatment efficacies, particularly for complex diseases such as cancer. The video also addresses the exponential growth expected in the AI health market while acknowledging challenges like data privacy, regulatory hurdles, algorithm transparency, and ethical considerations. Overall, the video showcases AI's pivotal role in augmenting medical capabilities and its synergy with other technologies for improved healthcare systems.

Takeaways

  • 🤖 AI is revolutionizing healthcare with advanced technologies.
  • 🔬 Initial video covered nanoparticles and nanomedicine.
  • 📈 AI healthcare market is projected to grow significantly by 2026.
  • 🩺 AI applications enhance diagnostics, treatments, and patient outcomes.
  • 💊 Personalized medicine is optimized through AI analysis.
  • 📸 AI improves medical imaging and disease detection accuracy.
  • 🧠 AI plays a role in mental health treatment plans.
  • 🚀 AI assists in rapid drug discovery and development.
  • 💡 AI-driven innovations include smart health gadgets and diagnostic apps.
  • 📊 Challenges include data privacy, regulatory issues, and algorithm transparency.

Timeline

  • 00:00:00 - 00:05:00

    The video begins with an introduction to the topic of nanoparticles, nanomedicine, and artificial intelligence (AI) in advancing healthcare systems. The speaker recaps the first part of the series which covered Nobel Prize-winning breakthroughs in nanotechnology, differences between traditional and Nano-enabled platforms, and examples of nano-medicines improving healthcare. This part will focus specifically on AI’s role, emphasizing AI's ability to enhance diagnostics, treatments, and patient care simultaneously to improve patient outcomes and reduce the global disease burden.

  • 00:05:00 - 00:10:00

    The speaker explains AI’s diverse applications in healthcare, such as medical imaging for personalized medicine, leading to improved accuracy and efficiency. Detailed market advancements are shared, noting the significant growth in the AI healthcare market from 2020 to 2026. As AI matures, its synergy with nanotherapeutics is crucial, evidenced by recent Nobel Prizes for AI-related research in computational modeling. AI's application in areas like liver cancer involves virtual assistance technologies and diagnostic tools enriched by AI to aid healthcare professionals.

  • 00:10:00 - 00:15:00

    Examples of AI applications in healthcare include improved diagnostics through medical imaging, aiding follow-up therapies, predicting treatment responses, and enhancing drug development. Tools like Google's DeepMind system can detect multiple eye diseases from medical scans, and AI performs image segmentation distinguishing between healthy and diseased tissues, thereby aiding surgical planning. The speaker argues AI is integral to improving diagnostic speed, accuracy, and patient outcomes, especially in conditions like cancer where early diagnosis is pivotal.

  • 00:15:00 - 00:20:00

    AI's capability extends to mental health by analyzing mood and behavior data to tailor treatments, thus helping overcome social stigma associated with mental disorders. AI further assists in personalized medicine by analyzing patient genetics for optimized therapies with minimal side effects. It also aids drug discovery, radiology, and surgery, with innovations like nano-robots delivering drugs to specific cells. The role of AI is to enhance existing systems through solutions that improve treatment plans and patient healthcare outcomes, demonstrating its transformative impact.

  • 00:20:00 - 00:25:00

    AI is explained as mimicking human cognitive functions to solve problems, with significant potential in synergy with nanotherapeutics to revolutionize healthcare. Applications discussed include drug potency predictions, microscopy for nanomaterial characterization, smart wearables for seizures prediction, and AI-driven stroke detection. The speaker highlights AI's impact, from vocal biomarkers identifying neurological diseases to smart applications notifying medical conditions before professional consultation, emphasizing advancements in clinical use enhancing healthcare delivery.

  • 00:25:00 - 00:32:23

    Challenges such as data privacy, regulatory hurdles, transparency, and ethical concerns are outlined as barriers to AI and nanotherapeutic integration. Despite these, technologies promise significant healthcare advancements. The future of nanomedicine and healthcare will involve AI-guided formulations, scalable production standards, and improved treatments for diseases like cancer. The speaker concludes that AI and nanomedicine must collaborate to enhance patient care, ensuring accessibility and affordability to maximize benefits and improve health outcomes globally.

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Mind Map

Mind Map

Faqs

  • What was covered in the first part of the video series?

    The first part covered nanoparticles and nanomedicine, including Nobel prize-winning research, types of nanomaterials, drug delivery differences between traditional and nano-enabled platforms, and examples of nanomedicine.

  • How is AI revolutionizing healthcare?

    AI introduces advanced technologies that enhance diagnostics, treatments, and patient care by improving their accuracy, efficiency, and outcomes simultaneously.

  • What are some AI applications in healthcare mentioned in the video?

    AI is used in identifying diseases through medical imaging, personalized medicine, risk screening, and follow-up therapies. It also aids drug development by predicting drug efficacy, and in robotic surgeries.

  • What is the expected growth in the AI healthcare market by 2026?

    The AI healthcare market is expected to grow to 150 billion USD by 2026.

  • How does AI contribute to personalized medicine?

    AI analyzes patient history and genetic profiling to customize treatment plans, aiming for optimized therapy with minimal side effects.

  • What role does AI play in mental health?

    AI uses data analytics to identify mood patterns and behaviors, facilitating a tailored treatment plan. It also provides anonymous assistance via chatbots.

  • What are some challenges in integrating AI with healthcare?

    Challenges include data privacy and security, regulatory hurdles, the complexity of AI algorithms raising transparency and explainability issues, and ethical concerns regarding bias and fairness.

  • How does AI assist in drug discovery?

    AI predicts drug efficacy, identifies potential therapeutic targets, and accelerates drug discovery and development processes.

  • What are some examples of AI-driven innovations in healthcare?

    Examples include AI-driven smart bracelets for epilepsy, diagnostic apps for stroke detection, and vocal biomarkers for diseases like Alzheimer's and Parkinson's.

  • How is AI used in medical imaging?

    AI algorithms analyze medical data like X-rays, MRIs, and CT scans to detect abnormalities, aiding radiologists in effective diagnosis.

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  • 00:00:03
    Hello friends so today we are going to
  • 00:00:05
    talk about the nanop particles
  • 00:00:07
    nanomedicine and artificial
  • 00:00:10
    intelligence and their role in advancing
  • 00:00:12
    Health Care Systems so I hope you have
  • 00:00:15
    already uh listened to the part one of
  • 00:00:18
    this video where I have talked about the
  • 00:00:22
    uh recent most noble prizes which have
  • 00:00:25
    been uh awarded in this particular field
  • 00:00:28
    of nanotechnology
  • 00:00:30
    and artificial intelligence I had also
  • 00:00:32
    mentioned about different kinds of
  • 00:00:34
    nanomaterials available also uh how
  • 00:00:38
    these nanom medicines are developed
  • 00:00:40
    which kind of nanop platforms are used
  • 00:00:42
    what is the difference between uh
  • 00:00:44
    traditional and the Nano enabled
  • 00:00:46
    Platforms in terms of uh drug delivery
  • 00:00:50
    and uh also I had given a couple of
  • 00:00:52
    examples about uh these nanom medicines
  • 00:00:56
    uh so I covered almost uh everything
  • 00:01:00
    about nanoparticles and nanom Medicine
  • 00:01:02
    in the first part and how they are being
  • 00:01:05
    utilized uh for
  • 00:01:08
    uh the advancing the Health Care Systems
  • 00:01:11
    uh in the first part so I'm hoping that
  • 00:01:14
    you might have heard that by now if not
  • 00:01:16
    you should go back and listen to that
  • 00:01:17
    part uh first because in this part two
  • 00:01:21
    I'm going to specifically focus on the
  • 00:01:23
    artificial intelligence and its role uh
  • 00:01:26
    in advancing the healthcare system so
  • 00:01:31
    to uh quickly uh go to the artificial
  • 00:01:35
    intelligence part you are you're already
  • 00:01:37
    aware of the fact that uh AI is actually
  • 00:01:41
    revolutionizing the healthcare by
  • 00:01:43
    introducing Advanced Technologies and
  • 00:01:46
    these Technologies actually uh are
  • 00:01:49
    enhancing the Diagnostics treatments and
  • 00:01:52
    patient care all at the same time uh
  • 00:01:55
    simultaneously because most of the time
  • 00:01:57
    these
  • 00:01:58
    Diagnostics um or the treatment or the
  • 00:02:00
    patient care uh systems go hand in hand
  • 00:02:03
    in hand because you need to uh monitor
  • 00:02:06
    everything uh simultaneously while you
  • 00:02:09
    are taking care of a patient in
  • 00:02:10
    particular for a specific uh
  • 00:02:13
    disease so AI applications in Healthcare
  • 00:02:16
    systems are obviously diverse starting
  • 00:02:19
    from the um identifying the medical
  • 00:02:22
    imaging and the uh processing of that
  • 00:02:26
    data and finally leading to the uh
  • 00:02:28
    personalized medicine for individual on
  • 00:02:31
    the basis of all the uh data analysis
  • 00:02:33
    and processing which is done using
  • 00:02:37
    various data analytical tools and uh
  • 00:02:39
    artificial
  • 00:02:41
    intelligence uh uh simultaneously so Bas
  • 00:02:45
    all this is done for the significant
  • 00:02:48
    improvements in accuracy efficiency and
  • 00:02:50
    the patient outcomes because you want
  • 00:02:52
    your uh patient outcomes to be better so
  • 00:02:55
    that uh at
  • 00:02:57
    least you can
  • 00:03:00
    reduce some Global burden of these
  • 00:03:02
    diseases which are immense in number I
  • 00:03:05
    have also talked about the global burden
  • 00:03:07
    of the diseases in the earlier part of
  • 00:03:08
    this video so you can uh check it out
  • 00:03:11
    there that um uh how much Global burden
  • 00:03:16
    these diseases are putting upon us and
  • 00:03:18
    the pharmaceutical companies they are
  • 00:03:20
    growing day by day the market is growing
  • 00:03:22
    day by day lots of pharmaceutics are
  • 00:03:25
    getting develops but still we need
  • 00:03:27
    better uh Diagnostics and better far
  • 00:03:30
    better pharmaceutics for the treatment
  • 00:03:33
    of patients uh so as far as AI
  • 00:03:36
    Healthcare market and numbers is
  • 00:03:38
    concerned if you uh look at uh this data
  • 00:03:42
    uh in
  • 00:03:43
    2020 the global AI healthare Market was
  • 00:03:46
    somewhere around $4.9 billion US in
  • 00:03:50
    2021 a lot of startups came and almost
  • 00:03:53
    2.5 billion US dollars were raised by
  • 00:03:56
    these uh startups in 2024 this month
  • 00:04:00
    Market is expected to increase by 45.2
  • 00:04:03
    billion US and in 2026 it has to it is
  • 00:04:08
    expected to increase by almost 45%
  • 00:04:11
    making it to 150 billion US dollar in
  • 00:04:14
    healthcare by
  • 00:04:17
    2026 so you can understand that how
  • 00:04:20
    important it is to study uh these two
  • 00:04:24
    parallels uh together which means
  • 00:04:26
    artificial intelligence and the uh and
  • 00:04:30
    the Therapeutics u in this case we are
  • 00:04:33
    talking about specifically about the
  • 00:04:35
    nanotherapeutics or the nanom medicines
  • 00:04:37
    and studying them together it becomes
  • 00:04:39
    really important just by looking at the
  • 00:04:42
    number you can understand that recent
  • 00:04:44
    Nobel prizes have been awarded in um for
  • 00:04:47
    the U for the artificial intelligence
  • 00:04:50
    related work uh and also for the
  • 00:04:54
    computational modeling of uh uh 3D
  • 00:04:57
    structure of protein using the machine
  • 00:04:58
    learning so more or less both have been
  • 00:05:00
    awarded in the field of artificial
  • 00:05:02
    intelligence or machine learning making
  • 00:05:04
    it all the more
  • 00:05:06
    important so if I just give you an
  • 00:05:09
    example of an AI application in
  • 00:05:12
    healthcare uh just uh let's say we if we
  • 00:05:16
    talk about the liver cancer and how
  • 00:05:19
    artificial intelligence is playing any
  • 00:05:21
    role in liver cancer so starting from
  • 00:05:24
    the uh virtual assistance which patient
  • 00:05:27
    might might use in terms of of um
  • 00:05:30
    identifying the starting from the uh
  • 00:05:34
    doctors or the Path Labs or uh to talk
  • 00:05:37
    to somebody about uh their symptoms
  • 00:05:41
    these virtual assistance in the form of
  • 00:05:43
    the UMC different kinds of mobile
  • 00:05:46
    applications or the even chat gpds or
  • 00:05:49
    chat boards these applications have been
  • 00:05:52
    utilized um utilized uh
  • 00:05:55
    variously utilized by uh different uh
  • 00:06:00
    even different hospitals or different
  • 00:06:02
    healthc Care Professionals nowadays so
  • 00:06:04
    this is one example then second example
  • 00:06:06
    uh is that the medical image Imaging
  • 00:06:09
    diagnosis is being done um using these
  • 00:06:13
    artificial intelligence related
  • 00:06:15
    applications and uh these medical images
  • 00:06:19
    U are initially collected segmented
  • 00:06:23
    processed and then analyzed by uh by the
  • 00:06:28
    um AI profession and then they develop
  • 00:06:31
    certain AIDS or the or the
  • 00:06:35
    tools diagnostic tools which are lated
  • 00:06:38
    later utilized by the healthc Care
  • 00:06:39
    Professionals also that is another uh
  • 00:06:42
    another research field which is being
  • 00:06:45
    utilized uh for uh these applications
  • 00:06:49
    then they can also be used for the eduin
  • 00:06:52
    therapy which means a follow-up therapy
  • 00:06:54
    after a treatment is more uh more or
  • 00:06:57
    less is done and then uh the follow-ups
  • 00:07:00
    have to be taken care of so that can be
  • 00:07:03
    taken care of by the artificial
  • 00:07:04
    intelligence or so the RIS risk
  • 00:07:08
    screening treatment response prediction
  • 00:07:09
    and prognosis evaluation all that can be
  • 00:07:12
    done computationally using artificial
  • 00:07:14
    intelligence and then also uh in
  • 00:07:19
    case uh better drugs with better uh
  • 00:07:23
    efficacies better um targeting and uh
  • 00:07:28
    better bio distribution and there are a
  • 00:07:30
    lot of things which still needs to be
  • 00:07:32
    improved so drug development and testing
  • 00:07:35
    can also be done by utilizing a AI
  • 00:07:37
    applications and also uh the
  • 00:07:40
    postoperative rehabilation
  • 00:07:42
    rehabilitation management once the
  • 00:07:45
    patient is operated for a disease and
  • 00:07:47
    then after that Rehabilitation is done
  • 00:07:49
    so there can be different kinds of
  • 00:07:50
    applications uh which are AI based which
  • 00:07:53
    can be utilized by um a patient or a
  • 00:07:56
    healthcare professional for the
  • 00:07:58
    betterment of the patient
  • 00:08:00
    outcomes so this is one example which I
  • 00:08:02
    have given to you there are some other
  • 00:08:05
    examples like for example if we talk
  • 00:08:06
    about medical imaging so this can be
  • 00:08:09
    used for the enhanced Diagnostics uh AI
  • 00:08:12
    algorithms uh analyze Medical Data like
  • 00:08:15
    x-rays MRIs and CT scans and uh they try
  • 00:08:19
    to identify different kinds of
  • 00:08:21
    abnormalities which are later uh used by
  • 00:08:24
    the radiologist for diagnosing the um
  • 00:08:28
    patient condition more effective ly and
  • 00:08:31
    um at the earliest possible for
  • 00:08:33
    example Google's deep mind uh has
  • 00:08:37
    developed Ani system that can detect
  • 00:08:39
    over 50 eye diseases from retinal scans
  • 00:08:42
    with uh accuracy comparable to export
  • 00:08:46
    opthalmologist so it can identify more
  • 00:08:48
    than 50 diseases um all it all very um
  • 00:08:53
    clearly specifically and probably better
  • 00:08:56
    than an export uh another example can be
  • 00:08:59
    in the form of the image segmentation
  • 00:09:01
    where AI performs different kind of
  • 00:09:03
    image segmentation uh and differentiate
  • 00:09:06
    between healthy and the disease tissue
  • 00:09:07
    so that a better planning for the
  • 00:09:10
    disease treatment can be done the um an
  • 00:09:13
    example has been uh given as the AI tool
  • 00:09:17
    like unit segment which which basically
  • 00:09:19
    segments tumor boundaries in brain MRIs
  • 00:09:22
    providing crucial information for
  • 00:09:24
    surgical planning so these are some of
  • 00:09:26
    the
  • 00:09:27
    examples which are of artificial
  • 00:09:30
    intelligence in terms of health care but
  • 00:09:34
    these are not limited uh as far as
  • 00:09:36
    medical diagnosis is concerned AI
  • 00:09:39
    basically is being used for uh this
  • 00:09:43
    application uh in particular to improve
  • 00:09:45
    the accuracy and speed of the disease
  • 00:09:47
    diagnosis as we um always keep listening
  • 00:09:51
    uh to this particular thing that um that
  • 00:09:54
    early diagnosis can
  • 00:09:57
    improve the chances of Sur sural of a
  • 00:09:59
    patient or the patient outcome uh can be
  • 00:10:03
    really uh really improved if a disease
  • 00:10:07
    can be identified at an early stage
  • 00:10:10
    specifically um like cancer
  • 00:10:12
    disease so AI has been uh sort of uh
  • 00:10:17
    helping uh people out in identifying or
  • 00:10:20
    diagnosing the diseases uh using the
  • 00:10:23
    different kinds of algorithm based on
  • 00:10:25
    the different kinds of data uh which are
  • 00:10:28
    available um uh available with the
  • 00:10:31
    healthcare professionals and uh
  • 00:10:34
    different
  • 00:10:35
    algorithms uh have been used not only
  • 00:10:38
    for the diagnosis but also for the
  • 00:10:40
    prediction of the diseases and for the
  • 00:10:43
    um identification of the diseases as
  • 00:10:45
    well similarly AI is utilized in uh in
  • 00:10:48
    mental health has been for the uh same
  • 00:10:52
    use cases it can be used so because it
  • 00:10:54
    can analyze data related to different uh
  • 00:10:57
    mood patterns the behavioral data can be
  • 00:11:00
    identified and then depending upon that
  • 00:11:02
    data um identification analysis a
  • 00:11:06
    treatment plan can be based on the same
  • 00:11:09
    and uh other than that obviously there
  • 00:11:12
    are different kinds of chat boards
  • 00:11:13
    available where people can talk to these
  • 00:11:16
    chat BS
  • 00:11:17
    or uh the whatever the social stigma is
  • 00:11:21
    related to these mental health issues
  • 00:11:23
    can be avoided because in that case
  • 00:11:26
    people are not much are not too bothered
  • 00:11:28
    about
  • 00:11:30
    um about uh with whom they are talking
  • 00:11:33
    to they can very confidently talk to
  • 00:11:36
    these chatbots and try to find out the
  • 00:11:39
    solutions of their problems and uh
  • 00:11:42
    sometimes probably this can work better
  • 00:11:44
    than uh better than talking to somebody
  • 00:11:47
    in particular in person um there is a
  • 00:11:51
    possibility so that is also uh another
  • 00:11:55
    example where people have been utilizing
  • 00:11:57
    these applications of late another thing
  • 00:11:59
    is uh AI can be utilized in personalized
  • 00:12:03
    medicines because uh once you have the
  • 00:12:06
    patient history and you have the uh
  • 00:12:09
    genetic profiling of a patient uh
  • 00:12:12
    depending upon the genetic profiling of
  • 00:12:15
    the patient doctor identifies the
  • 00:12:17
    specific disease or the disease markers
  • 00:12:20
    and then on the basis of that uh he
  • 00:12:23
    basically plans the um the treatment of
  • 00:12:27
    the patient and so that his uh therapy
  • 00:12:30
    can be optimized and finally uh the
  • 00:12:33
    there there there can be minimal side
  • 00:12:35
    effects so and a personalized medicine U
  • 00:12:39
    can be planned for an individual and AI
  • 00:12:42
    certainly uh AIDS in these treatment
  • 00:12:46
    plans likewise for the drug Discovery I
  • 00:12:49
    can be used because uh drug drug
  • 00:12:52
    efficacy can be predicted easily by the
  • 00:12:55
    by Ai and uh it can also help in
  • 00:12:57
    identifying the potential t
  • 00:12:59
    in new
  • 00:13:01
    therapies so this application is
  • 00:13:04
    also quite important uh in terms of the
  • 00:13:08
    Healthcare System new drugs can be
  • 00:13:11
    identified on the basis of the same
  • 00:13:13
    likewise AI is utilized in Radiology as
  • 00:13:16
    well because Radiology deals with a lot
  • 00:13:18
    of scans so since it deals with a lot of
  • 00:13:22
    scans so these scans can be um can be
  • 00:13:26
    feed into the
  • 00:13:29
    uh the system and the different AI
  • 00:13:32
    algorithms can predict the uh disease or
  • 00:13:36
    sometimes even which uh which um which
  • 00:13:41
    particular um treatment plant might
  • 00:13:44
    treatment plan might be better um over
  • 00:13:47
    the other can also be planned U on the
  • 00:13:50
    basis of these uh artificial
  • 00:13:52
    intelligence related uh
  • 00:13:55
    applications U surgery robotic surgeries
  • 00:13:58
    you all of you might have heard by now
  • 00:14:02
    uh can be uh done it is now possible in
  • 00:14:05
    uh some specific particular
  • 00:14:07
    hospitals uh likewise Nano robots have
  • 00:14:10
    been utilized for delivering the drug uh
  • 00:14:14
    to these specific target cells um that
  • 00:14:17
    is also another artificial intelligence
  • 00:14:19
    based uh uh based um
  • 00:14:23
    example which is utilized in the
  • 00:14:25
    healthare
  • 00:14:27
    systems so artificial intelligence
  • 00:14:29
    basically is revolutionizing this whole
  • 00:14:31
    world of uh world uh by giving better
  • 00:14:35
    treatment Plans by better uh diagnostic
  • 00:14:38
    measure by giving better diagnostic
  • 00:14:40
    measures by uh sort of giving the uh
  • 00:14:45
    giving the aid to the already existing
  • 00:14:48
    Health Care System uh likewise it can
  • 00:14:52
    also uh be it can also be uh utilized
  • 00:14:57
    for the um uh for the different kinds of
  • 00:15:01
    for some other different kinds of uh
  • 00:15:03
    applications also for example you can
  • 00:15:06
    utilize it for the management of medical
  • 00:15:08
    records uh robot assisted surgery I've
  • 00:15:11
    already mentioned to you it can be also
  • 00:15:14
    utilized to detect the fraud detection
  • 00:15:17
    and then customer service based chatbots
  • 00:15:19
    are already there which I just mentioned
  • 00:15:21
    to you virtual Health assistants are
  • 00:15:23
    there and then um accurate cancer
  • 00:15:27
    diagnosis improved Healthcare access so
  • 00:15:29
    there are different kinds of
  • 00:15:30
    applications which are already aware so
  • 00:15:32
    artificial intelligence is used to
  • 00:15:33
    describe the machines that
  • 00:15:36
    mimic the cognitive functions that
  • 00:15:39
    humans associate with other humans human
  • 00:15:41
    Minds there are a few uh spelling
  • 00:15:43
    mistakes but anyhow so the basically
  • 00:15:47
    artificial intelligence is utilized to
  • 00:15:49
    describe those machines which more or
  • 00:15:51
    less mimic the cognitive functions of
  • 00:15:54
    the human mind and then they try to
  • 00:15:56
    solve the problem by and they try to
  • 00:15:59
    learn the problem and give the solution
  • 00:16:01
    for them uh same so artificial
  • 00:16:04
    intelligence and Nar nanotherapeutics go
  • 00:16:07
    hand in
  • 00:16:09
    hand and they have a potential to
  • 00:16:11
    transform the Health Care
  • 00:16:13
    system so one uh more example can be of
  • 00:16:18
    the prediction of the drug
  • 00:16:20
    potency algorithm can be utilized
  • 00:16:23
    because uh they can analyze B data sets
  • 00:16:26
    of molecular structures experimental
  • 00:16:30
    results and clinical trials to predict
  • 00:16:32
    the effectiveness of drug candidates uh
  • 00:16:35
    accelerating drug Discovery and
  • 00:16:37
    development so these uh these U kind of
  • 00:16:42
    studies which utilize in silico drug
  • 00:16:44
    screening Ino drug scanning and finally
  • 00:16:47
    reaching to the nanoos discs for
  • 00:16:50
    monitoring drug release doing an
  • 00:16:52
    efficacy can also be um aided using
  • 00:16:56
    artificial
  • 00:16:57
    intelligence uh another example is that
  • 00:16:59
    they can be utilized for the microscopic
  • 00:17:01
    analysis where different kinds of images
  • 00:17:04
    come from microscopes then uh different
  • 00:17:07
    uh kind of patterns and features can be
  • 00:17:10
    identified which are usually which might
  • 00:17:12
    usually be missed by the human eye and
  • 00:17:15
    then they can lead to the
  • 00:17:16
    characterization and optimization of
  • 00:17:18
    nanomaterials and the uh which can
  • 00:17:21
    further be utilized by the
  • 00:17:23
    nanotherapeutics uh this example I have
  • 00:17:26
    already shared with all of you already
  • 00:17:27
    personalized medicine which AI can
  • 00:17:30
    analyze the patient data including its
  • 00:17:32
    genetic information and medical history
  • 00:17:35
    uh so that it can so that they can
  • 00:17:37
    identify the personalized treatment plan
  • 00:17:39
    for the targeted delivery of
  • 00:17:40
    nanotherapeutics so nanotechnology
  • 00:17:42
    basically is helping in the not only
  • 00:17:45
    diagnosis but the treatment selection
  • 00:17:47
    and the personalized medicine and
  • 00:17:49
    obviously in the followup of the disease
  • 00:17:52
    so this is a synergistic approach in
  • 00:17:55
    which the um artificial intelligence
  • 00:17:57
    andap itics have been helping each other
  • 00:18:01
    for the better patient outcome one
  • 00:18:04
    example very recent example is of uh
  • 00:18:08
    this uh particular uh kind which is of a
  • 00:18:12
    smart bracelet as you might already know
  • 00:18:16
    that epilepsy is the fourth most common
  • 00:18:18
    neurological disorder in world so um
  • 00:18:22
    identifying the
  • 00:18:24
    scissors uh on time or predicting the
  • 00:18:27
    scissors Before Time is extremely
  • 00:18:29
    important so this AI driven band which
  • 00:18:33
    is a smart bracelet kind of band uh
  • 00:18:36
    embraced to it is uh being uh utilized
  • 00:18:40
    for now the detection of the possible
  • 00:18:42
    convulsing scissors and this is a AI
  • 00:18:45
    driven band which uses the algorithm to
  • 00:18:47
    detect the possible convers convulsive
  • 00:18:50
    scissors so such products have been
  • 00:18:52
    coming up in the market uh now another
  • 00:18:55
    example is of the this app which uses
  • 00:19:00
    the which basically identifies the
  • 00:19:02
    stroke uh it also uses the Deep learning
  • 00:19:05
    algorithms and it automatically detects
  • 00:19:08
    a stroke on CT Imaging as soon as you
  • 00:19:10
    feed in a CT image it will immediately
  • 00:19:13
    um tell you whether uh whether this
  • 00:19:16
    image is of a patient who has just had a
  • 00:19:18
    stroke or not or who had the uh I mean
  • 00:19:21
    even going before to the to discuss it
  • 00:19:24
    with the medical professional you
  • 00:19:26
    already know that um that uh the patient
  • 00:19:30
    already had a stroke this is another
  • 00:19:33
    important uh application which is
  • 00:19:36
    already uh in the market
  • 00:19:39
    now one very important and interesting
  • 00:19:42
    example is of the vocal biomarkers
  • 00:19:44
    basically AI is able to help in
  • 00:19:47
    diagnosis through the sound of the voice
  • 00:19:49
    of patients
  • 00:19:50
    specifically uh for example in the
  • 00:19:52
    patients like of Alzheimer's and
  • 00:19:54
    Parkinson's disease where patient might
  • 00:19:57
    be having a little difficult in speaking
  • 00:20:00
    or um they their their speech is little
  • 00:20:03
    altered so these vocal biomarkers in the
  • 00:20:07
    form of their altered speech uh can be
  • 00:20:10
    utilized uh from the phone records to
  • 00:20:13
    analyze the risk of Alzheimer's or the
  • 00:20:16
    Parkinson's disease and these algorithms
  • 00:20:18
    are developed to
  • 00:20:20
    detect and in addition to that some
  • 00:20:22
    algorithms have been detect to developed
  • 00:20:25
    to detect the covid-19 vaccine Co 19
  • 00:20:29
    virus um even by just scuffing into
  • 00:20:31
    their smartphone apps and um that could
  • 00:20:36
    detect the uh covid-19 virus
  • 00:20:40
    so very nice and very uh Innovative kind
  • 00:20:44
    of applications people have been
  • 00:20:46
    utilizing uh for various applications uh
  • 00:20:51
    which are utilizing artificial
  • 00:20:53
    intelligence uh algorithms for
  • 00:20:56
    identifying diagnosing treating
  • 00:20:59
    uh the patients uh and advancing the
  • 00:21:02
    Health Care
  • 00:21:03
    Systems uh another example is of the
  • 00:21:06
    detecting arthas arthia you are you
  • 00:21:09
    might know that it is a it is also known
  • 00:21:11
    as cardic arithma and it is basically an
  • 00:21:14
    irregular heartbeat that can cause your
  • 00:21:16
    heart to beat either too fast or too
  • 00:21:18
    slow or with an irregular Rhythm and it
  • 00:21:22
    hence increases the risk of stroke or
  • 00:21:24
    heart failure and other heart related
  • 00:21:26
    complications so this AI application
  • 00:21:29
    which is uh medical grade ECG recordered
  • 00:21:32
    that is electrocardiograph recorded it
  • 00:21:34
    is already FD approved over the counter
  • 00:21:37
    use scale available for the over the
  • 00:21:39
    counter use it creates analyzes and
  • 00:21:42
    displays electrocardiograph data and can
  • 00:21:45
    provide information for identifying
  • 00:21:47
    cardiac
  • 00:21:49
    arhas so you can just have it in the
  • 00:21:51
    form of the arist band and uh get your
  • 00:21:54
    electrocardiogram data and identify the
  • 00:21:57
    um the uh cardiac Arias on your own as
  • 00:22:01
    well uh this is this is also in the
  • 00:22:05
    market now another application is of the
  • 00:22:07
    Nano
  • 00:22:08
    Qs which is quantity structure Rel
  • 00:22:11
    activity relationship models these
  • 00:22:14
    models are when combined with AI can
  • 00:22:16
    assess the toxicological effects of
  • 00:22:19
    nanoparticles uh so they by uh by
  • 00:22:22
    predicting toxicity AI can help uh in
  • 00:22:26
    designing safer nanom medicines before
  • 00:22:28
    even clinical trials begin so a model
  • 00:22:31
    called Nano qsr it helps in uh
  • 00:22:35
    evaluating potential toxicities and
  • 00:22:37
    safety concerns for different nanom
  • 00:22:39
    materials and it aids in regulatory
  • 00:22:41
    approval so it collects the data uh
  • 00:22:44
    train the algorithm and then finally uh
  • 00:22:47
    is utilized for evaluating the
  • 00:22:50
    cytotoxicity uh in uh so all these uh
  • 00:22:55
    applications which I have talked about
  • 00:22:58
    uh uh just now in addition to that there
  • 00:23:02
    are some other applications also which
  • 00:23:04
    can be utilized by these AI powered Nano
  • 00:23:08
    AI powered um
  • 00:23:11
    nanotherapeutic uh Nan sorry I mean they
  • 00:23:16
    are sort of AI powered n
  • 00:23:18
    nanotherapeutics Only which are being
  • 00:23:20
    utilized uh in the Healthcare
  • 00:23:22
    systems for
  • 00:23:25
    example they can be utilized for the
  • 00:23:27
    molecular designing so AI algorithms can
  • 00:23:30
    assist in the rational design of
  • 00:23:32
    nanoparticles and optimal
  • 00:23:33
    characteristics for drug delivery you
  • 00:23:35
    can identify which kind of nanoparticle
  • 00:23:37
    do you need and what should be their
  • 00:23:39
    properties and then finally you can
  • 00:23:41
    design them for a specific kind of drug
  • 00:23:44
    delivery uh system likewise you can have
  • 00:23:47
    the predictive modeling predictive
  • 00:23:49
    modeling uh would have the AI powered
  • 00:23:52
    simulations which can predict the
  • 00:23:53
    behavior and performance of
  • 00:23:55
    nanotherapeutics in complex biological
  • 00:23:57
    systems
  • 00:23:59
    then High throughput scen screening can
  • 00:24:01
    be used uh which uh AI enabled platforms
  • 00:24:05
    can accelerate the screening and
  • 00:24:06
    evaluation of large libraries of
  • 00:24:10
    nanomaterials uh these these can be
  • 00:24:13
    utilized for developing uh some uh new
  • 00:24:17
    kind of material and also data driven
  • 00:24:20
    optimization can be done using uh
  • 00:24:23
    machine learning learning algorithms
  • 00:24:25
    because a vast data sets set uh is uh
  • 00:24:28
    large was large data sets are available
  • 00:24:31
    for the design and development of
  • 00:24:33
    nanotherapeutics so artificial
  • 00:24:35
    intelligence can Aid into all these
  • 00:24:38
    kinds of applications similarly if you
  • 00:24:41
    talk about the convergent roles uh the
  • 00:24:44
    roles of AI and nanotherapeutics so uh
  • 00:24:48
    not one example is given here but then
  • 00:24:51
    it can be it can be for uh a lot number
  • 00:24:53
    of
  • 00:24:55
    diseases cancer diagnosis and treat
  • 00:24:57
    treatment
  • 00:24:59
    AI powered image analysis can assist in
  • 00:25:01
    early cancer Diagnostics or detection uh
  • 00:25:05
    and then by nanotherapeutics can deliver
  • 00:25:07
    chemotherapy drugs directly to tumor
  • 00:25:09
    cells improving treatment efficacy and
  • 00:25:12
    reducing side effects so if you know uh
  • 00:25:15
    that a patient has cancer and he's
  • 00:25:17
    diagnosed early he or she's diagnosed
  • 00:25:19
    early then nanotherapeutics can be
  • 00:25:21
    delivered on time with better efficacy
  • 00:25:23
    and better uh specificity so that uh
  • 00:25:27
    side effects can be reduced and the
  • 00:25:29
    patient outcomes can be better likewise
  • 00:25:31
    neurological disorders can be um
  • 00:25:33
    identified early or diagnosed early
  • 00:25:36
    depending upon the U AI based
  • 00:25:38
    applications and finally timely
  • 00:25:40
    interventions can be done in terms of
  • 00:25:42
    the uh in terms of the um treatment
  • 00:25:48
    plans uh also uh one example had
  • 00:25:51
    recently been of covid-19 where
  • 00:25:53
    nanotherapeutics have been used for the
  • 00:25:57
    rapid diagnostic
  • 00:25:58
    tests where uh if we talk about even the
  • 00:26:02
    vaccination which were developed by
  • 00:26:04
    fiser and Monna so those uh vaccines
  • 00:26:07
    were uh mRNA vaccines and they they had
  • 00:26:11
    lipid lipid nanoparticles as their shell
  • 00:26:16
    uh they were encapsulated in Li lipid
  • 00:26:18
    nanoparticles so that they can they can
  • 00:26:21
    go inside the cell
  • 00:26:24
    and uh would not uh would not be caught
  • 00:26:27
    by the
  • 00:26:28
    uh mRNA degrading enzymes and also since
  • 00:26:32
    they are negatively charged and the cell
  • 00:26:33
    membrane is also uh negatively charged
  • 00:26:37
    so lipid nanop particles were needed as
  • 00:26:40
    a core so that they can go inside the
  • 00:26:42
    cell uh easily so the these were the
  • 00:26:46
    recent examples and
  • 00:26:49
    vaccination have been for covid-19
  • 00:26:52
    vaccination uh these have already been
  • 00:26:54
    used similarly in Antics for advancing
  • 00:26:57
    these system systems can be utilized for
  • 00:26:59
    better diagnosis of these infectious
  • 00:27:01
    diseases and for delivering antiviral or
  • 00:27:04
    anti bacterial
  • 00:27:07
    drugs anti antibacterial agents it can
  • 00:27:09
    be
  • 00:27:12
    utilized but uh in addition to uh these
  • 00:27:17
    there are uh a lot of
  • 00:27:19
    challenges uh in using Ai and
  • 00:27:22
    nanotherapeutics Inter
  • 00:27:24
    integration uh the most important
  • 00:27:26
    challenges of the data privacy in
  • 00:27:28
    security because the AI requires large
  • 00:27:31
    data sets and obviously that data set
  • 00:27:35
    comes from the patient history uh and
  • 00:27:38
    the patient data because patient gives
  • 00:27:41
    its data in the form of demography
  • 00:27:44
    biochemical data and then clinical data
  • 00:27:48
    um there are lots of data which is
  • 00:27:50
    collected so that um the the privacy and
  • 00:27:54
    security of that data is one of the
  • 00:27:56
    maing major concern that how to go about
  • 00:27:59
    it and then there are second concern is
  • 00:28:02
    of the regulatory hurdles because taking
  • 00:28:05
    approval um of utilizing nanooptics and
  • 00:28:08
    AI driven Healthcare technology is quite
  • 00:28:10
    complex and time consuming because it is
  • 00:28:12
    a very recent field so still a lot of
  • 00:28:16
    hurdles are coming up in uh in U taking
  • 00:28:21
    the um approval or maybe even the even
  • 00:28:25
    the formulating the approval policy es
  • 00:28:28
    so that is another important challenge
  • 00:28:31
    uh in terms of using these integration
  • 00:28:37
    of AI and Antics then there is third
  • 00:28:40
    challenge which is transparency and
  • 00:28:42
    explainability because AI algorithms can
  • 00:28:44
    be complex and difficult to understand
  • 00:28:47
    so obviously they raise the concern
  • 00:28:48
    about transparency and explainability of
  • 00:28:50
    decisions that how exactly the the
  • 00:28:53
    machine is taking a decision whether it
  • 00:28:55
    is correct or not how corre how much
  • 00:28:58
    percentage of it is correct what is the
  • 00:29:01
    Precision how much is the Precision how
  • 00:29:03
    much is the accuracy so all these
  • 00:29:05
    concerns are obviously there and then
  • 00:29:08
    last but not the least are the ethical
  • 00:29:10
    considerations because AI which is used
  • 00:29:14
    in healthcare it uses it raises these
  • 00:29:17
    concern as well
  • 00:29:19
    because uh they are related to the
  • 00:29:22
    buyers or the fairness or the potential
  • 00:29:24
    of its
  • 00:29:26
    misuse uh so um these ethical
  • 00:29:29
    considerations are also uh there but but
  • 00:29:34
    uh but we assume that uh sooner or later
  • 00:29:38
    since these Technologies are helping the
  • 00:29:40
    humankind as a whole and uh are really
  • 00:29:45
    needed for the betterment of patient
  • 00:29:46
    outcomes so they they will have these uh
  • 00:29:50
    challenges would be overcome by the uh
  • 00:29:54
    by the healthcare uh system
  • 00:29:56
    professionals or people who are working
  • 00:29:58
    in these fields so the future of nanom
  • 00:30:01
    medicine and Health Care is a
  • 00:30:04
    combination of all these that you would
  • 00:30:06
    need AI or ml guided formulations and
  • 00:30:10
    they will be developed using different
  • 00:30:13
    drug Discovery processes and the uh
  • 00:30:17
    different kinds of
  • 00:30:18
    methods uh which are there already being
  • 00:30:22
    utilized by the not only by the research
  • 00:30:25
    scientist as in the own personal Labs
  • 00:30:29
    but by pharmaceutical Industries also as
  • 00:30:31
    a whole and then the standards and
  • 00:30:34
    protocols for Purity and
  • 00:30:37
    reproducibility uh would be would be
  • 00:30:39
    made uh so that things can be reproduced
  • 00:30:43
    easily if anybody wants to and then
  • 00:30:46
    there would be uh good manufacturing PR
  • 00:30:49
    practices there should be good
  • 00:30:50
    manufacturing practices for scalability
  • 00:30:53
    because usually when they're made in lab
  • 00:30:54
    they they're made in very small
  • 00:30:57
    quantities but later they need to be
  • 00:30:59
    scaled
  • 00:31:00
    up so so that these can further be
  • 00:31:03
    utilized by tackling with the infe
  • 00:31:06
    diseases like covid-19 or The Chronic
  • 00:31:09
    lethal conditions like cancer so this is
  • 00:31:12
    what is the future of narom medicine
  • 00:31:14
    going to be that um artificial
  • 00:31:17
    intelligence and nerom medicine should
  • 00:31:19
    go hand in hand and they should be
  • 00:31:21
    utilized for the uh for the benefit of
  • 00:31:25
    the patients as a whole so that more and
  • 00:31:28
    more people get benefited uh we uh we
  • 00:31:32
    are able to save a more number of lives
  • 00:31:35
    so that because we should be able to
  • 00:31:38
    diagnose early we should be able to
  • 00:31:40
    treat better and then also another
  • 00:31:43
    another concern can be of cost so we
  • 00:31:45
    need to take care of the cost as well it
  • 00:31:48
    has to be uh minimal uh so that
  • 00:31:51
    everybody can afford it so all these uh
  • 00:31:54
    things need to be taken care of so I
  • 00:31:56
    hope in due course of time uh the
  • 00:32:01
    situation will get better and better and
  • 00:32:04
    nanom medicine and artificial
  • 00:32:05
    intelligence will uh certainly provide
  • 00:32:09
    uh hope to the uh hope to the patients
  • 00:32:12
    and uh their
  • 00:32:15
    caregivers thank you
Tags
  • Nanomedicine
  • Artificial Intelligence
  • Healthcare Innovation
  • Diagnostics
  • Patient Care
  • AI Applications
  • Nanoparticles
  • Personalized Medicine
  • Medical Imaging
  • Drug Discovery