What are AI and ML use in medicine and its applications?

By arvind |Email | Aug 7, 2018 | 2214 Views

When it comes to effectiveness of machine learning, more data almost always yields better resultsâ??and the healthcare sector is sitting on a data goldmine. Artificial intelligence in healthcare is the use of algorithms and software to approximate human cognition in the analysis of complex medical data. Specifically, AI is the ability for computer algorithms to approximate conclusions without direct human input. There are various AI programmes that have been developed and put into practice for diagnosis process, treatment protocol development, drug development, personalized medicine,and patient monitoring and care. Large technology companies such as IBM and Google, and startups such as Welltok and Ayasdi, have also developed AI algorithms for healthcare. Various specialties in medicine have shown an increase in research regarding AI. While research on the use of AI in healthcare aims to validate its efficacy in improving patient outcomes before its broader adoption, its use may nonetheless introduce several new types of risk to patients and healthcare providers, such as algorithmic bias, Do not resuscitate implications, and other machine morality issues. These challenges of the clinical use of AI has brought upon potential need for regulations. Currently no regulations exist specifically for the use of AI in healthcare.

Applications of Machine Learning in Pharma and Medicine

1. Disease Identification/Diagnosis
Disease identification and diagnosis of ailments is at the forefront of research in medicine. According to a research more than 800 medicines and vaccines to treat cancer were in trial. It's no surprise that large players were some of the first to jump on the bandwagon, particularly in high-need areas like cancer identification and treatment. IBM Watson Health announced IBM Watson Genomics, a partnership initiative with Quest Diagnostics, which aims to make strides in precision medicine by integrating cognitive computing and genomic tumour sequencing. Other major examples include Google's DeepMind Health, which announced multiple UK-based partnerships, including with Moorfields Eye Hospital in London, in which they're developing technology to address macular degeneration in aging eyes.

2. Personalized Treatment/Behavioural Modification
Personalized medicine, or more effective treatment based on individual health data paired with predictive analytics, is also a hot research area and closely related to better disease assessment. IBM Watson Oncology is a leading institution at the forefront of driving change in treatment decisions, using patient medical information and history to optimize the selection of treatment options. Over the next decade, increased use of micro biosensors and devices, as well as mobile apps with more sophisticated health-measurement and remote monitoring capabilities, will provide another deluge of data that can be used to help facilitate R&D and treatment efficacy. This type of personalized treatment has important implications for the individual in terms of health optimization, but also for reducing overall healthcare costs. If more patients adhere to following prescribed medicine or treatment plans. Behavioral modification is also an imperative cog in the prevention machineSomatix â?? a data-analytics B2B2C software platform company whose ML-based app uses "recognition of hand-to-mouth gestures in order to help people better understand their behavior and make life-affirming changes", specifically in smoking cessation.

3. Drug Discovery/Manufacturing
The use of machine learning in preliminary (early-stage) drug discovery has the potential for various uses, from initial screening of drug compounds to predicted success rate based on biological factors. This includes R&D discovery technologies like next-generation sequencing.Precision medicine, which involves identifying mechanisms for "multifactorial" diseases and in turn alternative paths for therapy, seems to be the frontier in this space. Much of this research involves unsupervised learning, which is in large part still confined to identifying patterns in data without predictions.

4. Clinical Trial Research
Machine learning has several useful potential applications in helping shape and direct clinical trial research. Applying advanced predictive analytics in identifying candidates for clinical trials could draw on a much wider range of data than at present, including social media and doctor visits. ML can also be used for remote monitoring and real-time data access for increased safety, monitoring biological and other signals for any sign of harm or death to participants. There are many other ML applications for helping increase clinical trial efficiency, including finding best sample sizes for increased efficiency; addressing and adapting to differences in sites for patient recruitment; and using electronic medical records to reduce data errors.

5. Radiology and Radiotherapy 
In 20 years, radiologists won't exist in anywhere near their current form. They might look more like cyborgs: supervising algorithms reading thousands of studies per minute. Google's DeepMind Health is working to develop machine learning algorithms capable of detecting differences in healthy and cancerous tissues to help improve radiation treatments.

6. Smart Electronic Health Records:
Document classification using support vector machines, and optical character recognition are both essential ML-based technologies in helping advance the collection and digitization of electronic health information. MATLAB's ML handwriting recognition technologies and Google's Cloud Vision API for optical character recognition are just two examples of innovations in this area.

7. Epidemic Outbreak Prediction:
ML and AI technologies are also being applied to monitoring and predicting epidemic outbreaks around the world, based on data collected from satellites, historical information on the web, real-time social media updates, and other sources. Support vector machines and artificial neural networks have been used, for example, to predict malaria outbreaks, taking into account data such as temperature, average monthly rainfall, total number of positive cases, and other data points. Predicting outbreak severity is particularly pressing in third-world countries, which often lack medical infrastructure, educational avenues, and access to treatments. ProMED-mail is an internet-based reporting program for monitoring emerging diseases and providing outbreak reports in real-time.

Source: HOB