Artificial Intelligence in Healthcare
As recent trends suggest, the healthcare sector is indeed ready for a digital transformation driven by artificial intelligence. AI is increasingly having an important impact on healthcare with this technology now commonly used in areas such as risk assessment, cancer radiology, and chronic disease management. But even more important is the potential that this technology offers to the sector. As analysts have repeatedly pointed out, AI offers endless opportunities to the healthcare sector (Bohr and Memarzadeh 53). The most significant opportunity is the ability to develop impactful interventions to patients at the right moment.
Brain-Computer Interfaces
Creating interfaces that enable the human mind to interact with technology without the need for keyboards is likely to have an important impact on healthcare. Considering that neurological diseases may impair an individual’s ability to meaningfully carry out their daily duties, brain-computer interfaces aided by AI have been proposed as a solution. Indeed, brain-computer interfaces are projected to fundamentally enhance the quality of life among people diagnosed with neurological diseases and stroke (Bohr and Memarzadeh 61). This technology will also be useful for the over 500,000 people who sustain spinal cord injuries globally every year.
Next Generation Radiology Tools
Currently, although radiological images provide non-invasive visibility of body organs, it is important to note that most diagnostic processes rely on tissue samples and this poses a risk of infection. AI is increasingly providing more accurate radiology tools while also eliminating the need for tissue samples. It is expected that the next generation of radiology tools will allow health practitioners to better understand how tumours behave. But even more important is the possibility of ‘virtual biopsies’.
Enhancing Care in Undeserved Regions
As past experiences have shown, a critical shortage of healthcare resources including staff may limit access to care in undeserved regions. AI may help address this problem by performing some of the duties traditionally assigned to humans. For example, tests have shown that AI imaging tools are as accurate as human beings when used to diagnose tuberculosis. Nonetheless, it is imperative to note that for AI to have the desired impact on this area there is a need to develop applications that can perform well in low-resource areas (Bohr and Memarzadeh 66). Even as the sector leverages AI to meet the health needs of undeserved populations, it is important to factor the diversity of disease presentations.
AI and Electronic Health Record Use
Although EHRs have played a particularly fundamental role in the sector’s shift toward digitalization, it is evident that the switch has given rise to myriad problems such as endless documentation and cognitive overload. Currently, AI is being used develop more intuitive interfaces. AI technology is also being used to automate routine processes and this is expected to reduce the amount of time spent on such tasks. The potential that AI offers means that this technology may also be used to prioritize tasks thus allowing clinicians to attend to the most urgent tasks first (Bohr and Memarzadeh 67). Currently, voice recognition is having a profound impact on clinical documentation processes. On the other hand, however, there is wide acceptance that natural language processing tools are not advancing fast enough.
Managing the Risk of Antibiotic Resistance
One of the most urgent challenges facing the healthcare sector is the risk posed by antibiotic resistance. AI capabilities can be used to identify infection patterns thus allowing for more accurate treatment plans. Indeed, it is now increasingly evident that AI can address the challenge of antibiotic resistance. As analysts have observed, AI offers the ideal tool for hospitals to use their huge volumes of EHR data to tackle the problem of antibiotic resistance. Currently, the healthcare sector is simply not developing smarter and faster AI tools to enable it leverage on EHR data in managing the risk of antibiotic resistance.
Pathology Images Analytics
Pathologists provide arguably the most critical diagnostic data in healthcare delivery. It is estimated that up to 75% of clinical decisions are based on pathology results (Bohr and Memarzadeh 71). As such the accuracy of pathology results is important in getting the right diagnosis. Digital pathology driven by AI promises to address this problem. AI may also be applied to enhance productivity in pathology by assisting in the identification of features of interest. Since this technology can analyze large digital images to the pixel level, it may also be used to provide details that would have otherwise escaped the human eye. AI-driven pathology allows clinicians to easily identify what they are looking for and this enhances efficiency.
Immunotherapy for Cancer Treatment
Immunotherapy has emerged as one of the most promising cancer therapies. Nonetheless, it is important to note that existing immunotherapy options have proved unreliable as clinicians lack a way of identifying patients likely to benefit from this treatment. AI has the ability to process highly complex datasets and this would allow clinicians to identify therapies based on the patient’s unique genetic makeup. Yet although AI may eventually transform cancer treatment, it should be noted that most immunotherapies are relatively new meaning not many patients have been subjected to them (Bohr and Memarzadeh 74). The lack of adequate patient data means that advancing immunotherapy remains a highly complex challenge. In recent years, the shift toward checkpoint inhibitors has been arguably the most exciting development. Going forward, however, there is a need for more extensive research on this area.
Risk Prediction through EHRs
As earlier noted, although EHRs contain huge volumes of patient data, retrieval and analysis of this data has always proved challenging. This is largely attributed to the fact that data stored in EHRs often has integrity issues and may not be of the right quality. AI technology may be used to develop effective and reliable risk scoring and stratification tools. An important benefit that AI would offer is the capacity to identify hidden biases in data. As analysts have argued, however, the greatest challenge in using AI to predict risk would be ensuring that data relied upon is accurate.
Wearables and Personal Devices
Owing to the rapid technological advancement of recent years, the overwhelming majority of healthcare consumers today have access to personal devices that can collect health data. Tracking and analyzing this data can provide unique perspectives on an individual’s health more so when such data is supplemented with patient patient-provided data. AI promises to play an important role in the extraction and analysis of this data. Nonetheless, it is imperative to factor that patients may be unwilling to share personal data. As such, it is expected that the potential impact of AI on health monitoring may take longer. However, since patients generally trust healthcare providers, the shift toward AI-driven wearables and personal devices may gain momentum soon.
