The different Artificial Intelligence (AI) technology to Revolutionize the Field of Medicine (AIM)
The current academic and industrial researches performed with Artificial Intelligence in Medicine (AIM)
As an Electrical Engineering graduate student with research focusing on biomedical technology and innovation; the first thing that I tried to decide was how far the technology has evolved in medicine and what prospect does it present in the near future. Physicians and radiologists are still at the core of high demand but with the recent intervention of AI technology, Machine Learning (ML) and Data Science there is a shift to the automation of data analysis. A part of the reason for this shift is because AI technology improves accuracy and time: both of which are crucial elements when it comes to saving lives.
Medicine in itself is a complex and challenging domain. In many business areas, including the financial technology industry, artificial intelligence technology has shown encouraging results in automation and effective data analysis. AI is expected to redesign and revolutionize the healthcare industry. This can include devising effective treatment plans through Machine Learning (ML) and assisting professionals in the analysis of medical data.
Integration of AI system in healthcare can increase global revenue by 8.4 times. The healthcare industry can expect to save about $150 billion through innovations brought by AI technology.
The different sectors of the medical technology and industries Artificial Intelligence in Medicine (AIM) research is mostly based on biomedicine, patient data-management and information retrieval process. Sufficient investment and research are also allocated to enhance Augmented Intelligence. This field involves combining the power of scientific data with medical professional judgement.
Artificial Intelligence in Medicine (AIM) research is mostly based on biomedicine, patient data-management and information retrieval process. Sufficient investment and research are also allocated to enhance Augmented Intelligence. This field involves combining the power of scientific data with medical professional judgement.
Certain Ivy League universities are already invested in this technology.
Current University affiliated researches on AIM technology
1AIMI Center at Stanford University is utilizing interdisciplinary expertise on statistics, electrical engineering and bioinformatics for developing new AI methods for analyzing medical imaging.
2HMS (Harvard Medical School) is also actively involved at developing data driven models that can improve decision- making in the health care industry.
Research is done in developing a diagnostic algorithm that can find judgement features in digital images e.g. retina image to spot diabetic patients.
3Researchers at University of Buffalo (UB) are using ML to analyze predictive patterns in high- resolution medical images., genetic information and medical records to improve diagnosis in patients. The researchers are also working towards a tumor board database that can use AI for ‘precision oncology ’ that can improve on cancer patient care.
4 University of Maryland is also taking initiative in the field of AI and medicine, project: AIM-HI (AI + Medicine for high impact). As of 2020, they are working on: (i)Tacking chronic pain using ML-enabled biomarker discovery and sensing (ii) a multi-stage ML framework for prioritization in mental health and risk assessment (iii) Precision therapy for neonatal opioid withdrawal syndrome (NOWS).
There are other universities which are not listed above are also conducting similar research in the field of AI and medicine.
Industry affiliated researches on AIM technology
1IBM Watson Health brings a vast amount of medical data into a cloud hub. The cognitive capabilities and traditional analytics turn the data into knowledge. It can be used to determine the right medication for any patient.
This research has great potential because of the variation in patient conditions and the range of medications available. Using AI technology the platform can decide whether a particular medication is actually suitable for a particular patient.
However, some of the limitations and criticism include recommending incorrect treatment advice. Hence this platform has potential when complimented with a registered physician consultation.
2Google search engines have already been sincere in its effort in helping us on providing information about medical conditions or locations of nearby hospitals. They are extending their study of AI in assisting diagnosis of cancer, preventing blindness and other ways to improve on patient health care.
3Project Baseline by Verily is another approach used to collaborate with researchers, clinicians, engineers and volunteers to build on next generation healthcare tools and services. Their website lists certain research projects including a study on COVID-19, skin, mood, sleep studies and many other relevant fields.
There are other industrial projects which are not listed above are also conducting similar research in the field of AI and medicine.
How AI has revolutionized certain sectors of the medical industry?
AI and Deep Learning (DL) has been the primary focus to make the medical sector more efficient in terms of processing of data. The concept of deep learning for image analysis could be easily applied to read X-ray images and draw necessary conclusions. This eliminates the necessity of the scope for human error and offers a faster processing time.
Few applications of AIM
(i) Automation: The medical field requires two inseparable aspects, namely data intensive and knowledge-based. Routine consultation and other background analysis can be easily automated through an AI system.
(ii) Virtual diagnosis: Virtual AI-assisted medical screening platform is gaining popularity due to its lower cost and convenience. This also allows scope for an early diagnosis thereby preventing later healthcare complications. Phone screening or virtual interviews can check relevant data for diagnosis and can recommend appropriate physicians for further consultation (if required).
The platform can allow online diagnosis by asking a series of relevant questions about your symptoms. AI code can easily diagnose the illness. The most common online diagnosis includes sinusitis, bronchitis, asthma, nail infection and urinary tract infection.
(iii) Deep learning (DL)software for image analysis: This field is gaining momentum particularly because it provides scope to integrate the capabilities of a medical professional into an IT platform. Radiology involves the field of diagnosis and treatment of injuries using medical imaging. Deep learning in image analysis can improve the efficiency of diagnosis and actively aid in drawing conclusions on patient treatment from image analysis.
This field has been actively explored in cancer treatment. It can automate the image analysis procedure for tumor diagnosis and give a new error free model for early diagnosis and treatment. Error free analysis can also allow the scope to drive down the healthcare cost. There are several reasons why this field is gaining momentum: (i) lack of professionals in the particular field (ii) complications related to testing and analysis (iii) allows scope for collaboration between physician and pathologist (iv) reduces the cost by improving accuracy in diagnosis.
(iv) Robot-assisted surgery: Robot assisted surgeries involve advances in surgical technologies to improve the efficiency of the surgical procedure. This process usually involves insertion of a 3D camera and miniature surgical instrument into the patient’s body. The surgeon uses external control system to manipulate the instrument to do the surgical task with precision. Though until now the surgeons are only actively involved in the surgical decision- making process but there is scope for the AI technology to improve and automate some of the systems for improved accuracy. This is particularly important because AI technology can run at sites inaccessible by a 3D camera.
(v)Raw data processing: Most professionals struggle at accessing relevant data and have difficulty putting together to make valuable insight. Also, there is a scope for data alteration and integrity of data being compromised. Hence, there is a rising number of healthcare startups that are actively involved at processing a large amount of medical data. They perform data standardization and harmonization and is used in the ML platform.
The future of AIM
The primary focus of AIM is to integrate the ability of medical professionals with the data intensive system. This can allow scope for comprehensive understanding of medical information and data. AI can help to find accurately the relevant data which is both time-consuming and cost-effective. Healthcare industry is a data intensive system and therefore AI integration can find ways to improve care and cut overload of information.
Some of the limitations and criticism include recommending incorrect diagnosis and treatment advice. The AIM platform has potential when complimented with a registered physician consultation. The holistic role of physician , AIM and medical technology can improve the current healthcare system.
