Artificial intelligence in healthcare: transforming the practice of medicine PMC

Moreover, combining AI with actual knowledge in various medical specialties could result in dramatic changes, such as advanced diagnostics, correct risk and prognosis evaluation, and even treatment suggestions. Thus far, AI is proving to be effective and the research will continue to improve, as more applications are discovered and explored. The integration of digital pathology based on AI systems in our current practice will help enhance patient care. In collaboration with experts in technology and ethics, we can revolutionize health care, making it more precise and we can pave the way for a healthier future with the right implementations of AI.

• Many pharmaceutical giants like Sanofi and Pfizer are teaming up with tech companies IBM and Google.
• To help bring these changes to healthcare, organizations must learn how to use gen-AI platforms, evaluate recommendations, and intervene when the inevitable errors occur.
• To weigh the value of gen-AI applications in healthcare against the risks, leaders should create risk and legal frameworks that govern the use of gen AI in their organizations.
• Because of this, healthcare leaders should begin thinking about how they can improve their data’s fidelity and accuracy through strategic partnerships—with providers, payers, or technology vendors—and interoperability investments.

It would not be an exaggeration to refer to them as ever-present digital health coaches, as increasingly it is encouraged to wear them at all times in order to get the most out of your data. Garmin wearables are a good example of this, with a focus on being active, they cover a vast variety of sports and provide a substantial amount of data on their Garmin connect application where users can analyze and observe their daily activities. Humans interact with their surroundings with audiovisual cues and utilize their limbs to engage and move within this world. This seemingly ordinary ability can be extremely beneficial for those who are experiencing debilitating conditions that limit movement or for individuals who are experiencing pain and discomfort either from a chronic illness or as a side effect of a treatment. A recent study, looking at the effect of immersive VR for patients who had suffered from chronic stroke patients, found this technology to be contributing positively to the state of patients. During the VR experience, the patients are asked to grab a virtual ball and throw it back into the virtual space [31].

Personalizing Treatment Plans

Indexed databases, including PubMed/Medline (National Library of Medicine), Scopus, and EMBASE, were independently searched with notime restrictions, but the searches were limited to the English language. Robots are equipped with mechanical arms, surgical instruments and cameras, and controlled by surgeons from a computer console. This provides surgeons with a three-dimensional, magnified view of difficult or impossible-to-see surgical sites and enables doctors to improve their skills, knowledge and experience.

While considerable progress has been made in leveraging AI techniques and genomics to forecast treatment outcomes, it is essential to conduct further prospective and retrospective clinical research and studies [47, 50]. These endeavors are necessary for generating the comprehensive data required to train the algorithms effectively, ensure their reliability in real-world settings, and further develop AI-based clinical decision tools. Population health management increasingly uses predictive analytics to identify and guide health initiatives.

Nothing Artificial About The Future Of AI But Who Decides Its Intelligent Use In Healthcare?

AI algorithms can be trained to predict an individual’s response to a given drug based on their genetic makeup, medical history, and other factors. This personalized approach to drug therapy can lead to more effective treatments and better patient outcomes [57, 58]. The simultaneous analysis of extensive genomic data and other clinical parameters, such as drug efficacy or adverse effects, facilitates the identification of novel therapeutic targets or the repurposing of existing drugs for new applications [42,43,44,45,46].

Using ML algorithms and other technologies, healthcare organizations can develop predictive models that identify patients at risk for chronic disease or readmission to the hospital [61–64]. Using ML algorithms and other technologies, healthcare organizations can develop predictive models that identify patients at risk for chronic disease or readmission to the hospital [61,62,63,64]. AI would propose a new support system to assist practical decision-making tools for healthcare providers. In recent years, healthcare institutions have provided a greater leveraging capacity of utilizing automation-enabled technologies to boost workflow effectiveness and reduce costs while promoting patient safety, accuracy, and efficiency [77].

Moreover, AI can monitor changes that occur in blood cell counts in time and improve accuracy in detecting disease markers. With an estimated valuation of $15.1 billion for the global market in 2022, AI in healthcare is an irreversible process that is already reshaping medicine and its projected value will continue to rise in the following years. Enlitic uses deep learning to streamline workflows for radiologists and improve diagnoses. The company partnered with a hospital to treat patients with diabetic retinopathy (an eye condition that can cause vision loss) and found its AI system could recommend patient referrals as accurately as doctors for 50+ types of eye diseases.

NLP is a subfield of AI that focuses on the interaction between computers and humans through natural language, including understanding, interpreting, and generating human language. NLP involves various techniques such as text mining, sentiment analysis, speech recognition, and machine translation. Over the years, AI has undergone significant transformations, from the early days of rule-based systems to the current era of ML and deep learning algorithms [1–3].

To avoid them, Kohane said it’s critical that AIs are tested under real-world circumstances before wide release. Second, Lee and colleagues figured out a way to provide a window into an AI’s decision-making, cracking open the black box. The system was designed to show a set of reference images most similar to the CT scan it analyzed, allowing a human doctor to review and check the reasoning.

NIH-funded researchers are working on these issues, as well as many other ways to use AI in medicine. These include modeling the ways a virus might spread between countries and predicting if new drugs will be safe. The team is also testing whether they can use AI to individualize breast cancer treatment based on imaging results that show how breast tumors are responding. AI may better reveal who needs more intensive treatment, like chemotherapy, and who can safely skip it.

This increase in demand is partly due to a technology reliable population that has grown to learn that technological innovation will be able to assist them in leading healthy lives. This technology will continue to push boundaries and certain norms that have been dormant and accepted as the status quo for hundreds of years, will now be directly challenged and significantly augmented. Many elderly people experience a decline in their cognitive abilities and have difficulties in problem-solving tasks as well as maintaining attention and accessing their memory. Cognitive stimulation is a common rehabilitation approach after brain injuries from stroke, multiple sclerosis or trauma, and various mild cognitive impairments. Cognitive stimulation has been demonstrated to decrease cognitive impairment and can be trained using assistive robots. NLP makes use of various classifications to infer meaning from unstructured textual data and allows clinicians to work more freely using language in a “natural way” as opposed to fitting sequences of text into input options to serve the computer.

A team of experts from the Institute of Cancer Research, London, Royal Marsden NHS foundation and Imperial College London have used radiomics to identify if abnormal growths on CT scans are cancerous. Radiomics is a quantitative approach that uses advanced mathematical analysis to enhance the data available to clinicians. In this study, radiomics was used to extract essential information from medical images easy to miss by the human eye. Moreover, artificial intelligence enables researchers to analyze and repurpose existing medicines to combat specific diseases, making the development of new drugs more cost-efficient and effective. The emerging generative AI is accelerating drug discovery through designing molecular structures. AI algorithms provide clinicians with vital insights regarding the condition of the patients, increasing the speed of diagnosis, accuracy of medical results and improving patients’ health outcomes.

AI-powered mental health applications can assist in the early detection and diagnosis of mental health conditions, as well as provide tailored treatment and support [88–91] These applications can also offer round-the-clock support, reducing the need for in-person appointments and wait times. Furthermore, these digital tools can be used to monitor patient progress and medication adherence, providing valuable insights into treatments’ effectiveness [88]. The impact on the workforce will be much more than jobs lost or gained—the work itself will change.

The MoleculeNet platform is built on data from various public databases and more than 700,000 compounds have already been tested for toxicity or other properties [20]. RAISE-Health is a joint initiative between Stanford Medicine and the Stanford Institute for Human-Centered Artificial Intelligence (HAI) to guide the responsible use of AI across biomedical research, education, and patient care. ChatGPT was released by the technology company OpenAI for public use on 30 November 2022.

With its ability to mimic human cognitive functions, AI has revolutionized industries, improved efficiency, and unlocked new possibilities. During the past few years, governments have adopted a variety of smart applications that can use AI and its subsets provide predictions and recommendations in various fields, such as healthcare, finance, agriculture, education, social media, and data security. The greatest challenge to AI in these healthcare domains is not whether the technologies will be capable enough to be useful, but rather ensuring their adoption in daily clinical practice. These challenges will ultimately be overcome, but they will take much longer to do so than it will take for the technologies themselves to mature. As a result, we expect to see limited use of AI in clinical practice within 5 years and more extensive use within 10 years.

Blaschke et al. also applied adversarial autoencoders and Bayesian optimization to generate ligands specific to the dopamine type 2 receptor [22]. Merk et al. trained a recurrent neural network to capture a large number of bioactive compounds such as SMILES strings. This model was then fine-tuned to recognize retinoid X and peroxisome proliferator-activated receptor agonists. The identified compounds were synthesized and demonstrated potent receptor modulatory activity in in vitro assays [23].

The current educational system is limited and for interactive disciplines such as medicine this can be a hindrance. These individuals require certain skills to fulfill the need for an ever-evolving profession. Early in medical school, various concepts are taught to students without them ever experiencing these concepts in real life. So game-like technologies such as VR and AR could enhance and enrich the learning experience for future medical and health-related disciplines [29]. Medical students could be provided with and taught novel and complicated surgical procedures, or learn about anatomy through AR without ever needing to interact or involve real patients at an early stage or without ever needing to perform an autopsy on a real corpse. These students will of course be interacting with real patients in their future careers, but the goal would be to initiate the training at an earlier stage and lowering the cost of training at a later stage.

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